Patient ventilator asynchrony management system, including asynchrony mitigation messaging and adjustments

Abstract

Systems and methods are provided for managing, such as mitigating, patient ventilator asynchrony. Example systems include a mechanical ventilation system and at least one controller communicatively coupled with the mechanical ventilation system. The at least one controller obtains input including some or all of: PVA data identifying a detected type of PVA, ventilatory parameter data relating to the ventilation being provided to a patient, a measure of PVA severity relating to the detected type of PVA, and patient data identifying one or more patient characteristics. Based at least in part on the obtained input, the at least one controller determines output including at least one of: one or more PVA mitigation notifications, such as may include root cause information, and at least one adjustment, relating to the mechanical ventilation being provided to the patient, for implementation to mitigate the detected type of PVA.

Description

PATIENT VENTILATOR ASYNCHRONY MANAGEMENT SYSTEM, INCLUDING ASYNCHRONY MITIGATION MESSAGING AND ADJUSTMENTSBACKGROUND

[0001] Optimal patient ventilator interaction is essential in optimizing outcomes, as well as assuring comfort, in mechanical ventilation. Patent ventilator asynchrony (PVA) may result, for example, from a mismatch between the patient and the ventilator, such as may relate to patient demand or neurological drive relative to the output or operation of the ventilator. PVA often leads substantially poorer outcomes, and can cause both immediate and lasting negative health effects. PVA is associated with a longer duration of mechanical ventilation, as well as negative effects such as diaphragm injury, lung strain, lung injury (which may be self-inflicted), dyspnea, delirium, cognitive disruption, anxiety, high stress and associated elevated transpulmonary pressure. Yet, PVA has remained a very common problem in mechanical ventilation. It has been estimated that up to 80% of noninvasive ventilation patients and 25% of invasively ventilated patients experience PVA. Optimally managing PVA has remained a major challenge in mechanical ventilation and improving patient treatment outcomes.SUMMARY

[0002] One example provides a system for managing patient ventilator asynchrony (PVA) during mechanical ventilation therapy of a patient, the system comprising: a mechanical ventilation system for providing mechanical ventilation to the patient; and at least one controller, communicatively coupled with the mechanical ventilation system, the at least one controller configured to: obtain input comprising: PVA data identifying a type of PVA, ventilatory parameter data identifying one or more ventilatory' parameters relating to the mechanical ventilation being provided to the patient, and a measure of PVA severity relating to the type of PVA, and determine output based at least in part on the obtained input, the output comprising at least one of: one or more PVA mitigation notifications and at least one adjustment relating to the mechanical ventilation being provided to the patient. In examples in which the output is determined to relate to an increase or decrease, for example, the output may be the one or more PVA mitigation notifications and / or the at least one adjustment relating to the mechanical ventilation being provided to the patient.

[0003] In some examples, obtaining the measure of PVA severity' comprises determining a magnitude of PVA severity for each of one or more patient breaths. In someexamples, determining the magnitude of PVA severity for each of the one or more patient breaths comprises analyzing at least one of: a patient airway pressure waveform and a patient airway flow waveform, associated with a period of time during which the type of PVA is present. In some examples, the patient airway pressure waveform is obtained using measured patient airw ay pressure. In some examples, the patient airway flow waveform is obtained using measured patient airway flow. In some examples, determining the magnitude of PVA severity for each of one or more patient breaths comprises at least one of: comparing a patient airway pressure waveform to an airway pressure waveform for which the type of PVA is not present, and comparing a patient airway flow waveform to an airway flow waveform for which the type of PVA is not present. In some examples, the measure of severity is based at least in part on an estimated work of breathing of the patient, wherein the estimated work of breathing of the patient is based at least in part on measured patient airway pressure.

[0004] In some examples, determining the magnitude of PVA severity for the type of PVA for each of the one or more patient breaths comprises: obtaining a first airway pressure waveform associated with a breath for which the type of PVA is not present, obtaining a patient airway pressure waveform associated with a patient breath of the one or more breaths for which the type of PVA is present, comparing the first airway pressure waveform to the patient airw ay pressure w aveform to determine a deviation betw een the first airway pressure waveform and the patient airway pressure waveform, determining a magnitude of the deviation, and based at least in part on the magnitude of the deviation, determining the magnitude of PVA severity. In some examples, determining the deviation comprises: determining, for the patent airway pressure waveform, a quantified graphical area associated with the type of PVA, and determining the magnitude of the deviation based on the quantified graphical area. In some examples, providing the at least one of: one or more PVA mitigation notifications and at least one adjustment relating to the mechanical ventilation being provided to the patient comprises determining an adjustment relating to the mechanical ventilation being provided to the patient, the adjustment comprising an amount of a change to at least one of the one or more ventilatory parameters.

[0005] In some examples, the type of PVA is a detected type of PVA. In some examples, the at least one controller is configured to determine the measure of PVA severity. In some examples, the at least one adjustment is for implementation to mitigate the type of PVA. In some examples, the at least one adjustment is to at least one ventilatory parameter of the one or more ventilatory parameters and for mitigation of the type of PVA. In some examples, the measure of PVA severity is based at least in part on measured patient airwaypressure. In some examples, the measure of severity is based at least in part on an estimated work of breathing of the patient, wherein the estimated work of breathing of the patient is based at least in part on the measured patient airway pressure. In some examples, the estimated work of breathing is estimated based at least in part on the measured patient airway pressure for at least a portion of an inspiration and estimated using an artificial neural network. In some examples, the measure of severity is based at least in part on a calculated pressure-time product relating to the measured patient airway pressure for at least a portion of an inspiration.

[0006] In some examples, the measure of severity is based at least in part on airway occlusion pressure measured during at least a portion of an inspiration. In some examples, the measure of PVA severity comprises a numerical PVA severity score, wherein a magnitude of the PVA severity score corresponds with PVA severity. In some examples, the at least one controller is configured to: monitor the measure of PVA severity over a period of time, and determine the output based at least in part on the monitored measure of PVA severity over the period of time. In some examples, the at least one controller is configured to: forecast the measure of PVA severity' over a future period of time based at least in part on the measure of PVA severity' as monitored over the period of time, and determine the output based at least on the forecasting. In some examples, the at least one controller is configured to: determine a level of improvement of a patient condition over the period of time, based at least in part on the monitored measure of PVA severity, and determine at least one intervention recommendation, for providing to a care provider, based at least in part on the determined level of improvement of the patient condition.

[0007] In some examples, the input comprises patient data identifying one or more patient characteristics comprising at least one of: patient sex, patient age. patient height, a patient bodyweight related characteristic, and a patient respiratory mechanics parameter. In some examples, the at least one controller is configured to: compare at least a portion of the input to at least one specified threshold, the at least a portion of the input comprising at least a portion of the ventilatory parameter data, and determine the output based at least in part on the comparison. In some examples, the type of PVA comprises at least one of: a trigger related asynchrony, a cycling related asynchrony, a flow related asynchrony, ineffective efforts during expiration, autotriggering, prolonged cycling, short cycling, insufficient flow, excessive flow, and double triggering. In some examples, the one or more ventilatory parameters comprise at least one of: one or more measured ventilation related parameters, one or more ventilation relating settings, a ventilation mode, a pressure support level, arespiratory rate, a pressure rise time, a ventilation targeting scheme, a baseline airway pressure (BAP), an inspiratory time, an expiratory time, a trigger sensitivity’, a tidal volume, a flow cycle threshold, and active ventilation related alarm data. In some examples, the one or more PVA mitigation notifications comprise one or more PVA mitigation messages.

[0008] In some examples, the one or more PVA mitigation messages comprise one or more recommendations regarding one or more adjustments to one or more ventilatory parameters or one or more alarm messages that are at least one of: capable of being muted by a care provider, capable of being interacted with by the care provider to display additional PVA mitigation information, and capable of being interacted with by the care provider to implement at least one of measure to mitigate the type of PVA. In some examples, the one or more PVA mitigation notifications comprise at least one of: one or more user actions, performing a check relating to the mechanical ventilation system, administering at least one drug to the patient, and one or more adjustments to one or more ventilation parameters. In some examples, the system comprises at least one display incorporated into at least one of: the mechanical ventilation system, a portable computing device and a defibrillator, and wherein the one or more PVA mitigation notifications are presented on the at least one display.

[0009] In some examples, the at least one adjustment is automatically implemented, and wherein the automatic implementation comprises implementation without user confirmation. In some examples, the at least one controller is configured to implement the at least one adjustment after receiving confirmation from a care provider to implement the at least one adjustment. In some examples, the output comprises at least one adjustment comprising a plurality of periodic adjustments, and wherein the at least one controller is configured to: monitor a set of one or more ventilatory parameters during a period of the mechanical ventilation being provided to the patient, and determine the plurality of periodic adjustments such that the monitored one or more ventilatory parameters are maintained within one or more specified ranges during the period. In some examples, the output comprises at least one adjustment comprising a plurality’ of periodic adjustments, and wherein the at least one controller is configured to: monitor the measure of PVA severity during a period of the mechanical ventilation being provided to the patient, and determine the plurality of periodic adjustments such that the measure of PVA severity’ is minimized during the period.

[0010] In some examples, at least a first controller, of the at least one controller, is incorporated into at least one device communicatively coupled with the mechanicalventilation system. In some examples, the mechanical ventilation system comprises at least a first controller of the at least one controller. In some examples, the at least a first controller of the mechanical ventilation system obtains the PVA data from at least one communicatively coupled device. In some examples, the at least a first controller of the mechanical ventilation system determines the PVA data. In some examples, the at least a first controller of the mechanical ventilation system obtains the measure of PVA severity from at least one communicatively coupled device. In some examples, the at least a first controller of the mechanical ventilation system determines the measure of PVA severity. In some examples, the at least a first controller of the mechanical ventilation system determines the output. In some examples, the at least a first controller of the mechanical ventilation system obtains the output from at least one communicatively coupled device. In some examples, the type of PVA comprises ineffective efforts during expiration, and wherein the at least one controller is configured to determine the output to relate to an increase in trigger sensitivity.

[0011] In some examples, the type of PVA comprises ineffective efforts during expiration, and wherein the at least one controller is configured to: detect whether autopositive end expiratory pressure (auto-PEEP) is present, and if auto-PEEP is detected, determine the output to relate to at least one of: an increase in PEEP, an increase expiratory time, a decrease in inspiratory time, and a decrease in respiratory rate. In some examples, the type of PVA comprises ineffective efforts during expiration, and wherein the at least one controller is configured to: determine whether patient respiratory muscle effort, associated with the ineffective efforts during expiration, is less than a specified threshold, determine whether respiratory rate is not equal to a set respiratory rate, and if the patient respiratory' muscle effort is less than the specified threshold, and if the respiratory' rate is not equal to the set respiratory rate, then determine the output to relate to a decrease in sedation. In some examples, the type of PVA comprises ineffective efforts during expiration, and wherein the at least one controller is configured to: determine whether measured tidal volume is greater than a specified tidal volume threshold, and if the measured tidal volume is greater than the specified tidal volume threshold, then determine the output to relate to a reduction in pressure support.

[0012] In some examples, the specified tidal volume threshold is a value between 6 and 10 ml / kg of a body weight associated with the patient. In some examples, the bodyweight is predicted or obtained based at least in part on patient sex and patient height. In some examples, the at least one controller is configured to: if the measured tidal volume is greater than the specified tidal volume threshold, then determine the output to relate to a reduction inpressure support to deliver a tidal volume of between a minimum value and a maximum value. In some examples, the type of PVA comprises autotriggering, and wherein the at least one controller is configured to determine the output to relate to a decrease in trigger sensitivity. In some examples, the type of PVA comprises autotriggering, and wherein the at least one controller is configured to: determine whether a gas leak is detected relating to the mechanical ventilation system, and if the gas leak is detected, then determine the one or more PVA mitigation notifications to include at least one of a message to: check the mechanical ventilation system for loose connections, check exhalation valve, check placement of an endotracheal tube, check placement of a tracheotomy cuff, check a mask or other noninvasive patient-interface accessary replace a portion of a patient circuit, and replace the patient circuit.

[0013] In some examples, the at least one controller is configured to determine whether the gas leak is detected based at least in part on whether a difference between an inspiratory volume and an expiratory' volume is greater than a specified threshold. In some examples, the type of PVA comprises autotriggering, and wherein the at least one controller is configured to: determine whether condensate is detected in a patient circuit of the mechanical ventilation system, and if the condensate is detected, then determine the one or more PVA mitigation notifications to include a message to remove condensate from the patient circuit. In some examples, the type of PVA comprises prolonged cycling, and wherein the at least one controller is configured to determine the output to relate to at least one of: a decrease in inspiratory time, and an increase in flow cycle threshold. In some examples, the at least one controller is configured to: determine whether a gas leak relating to the mechanical ventilation system is detected, and if the gas leak is detected, then determine the one or more PVA mitigation notifications to include at least one of a message to: check the mechanical ventilation system for loose connections, check exhalation valve, check placement of an endotracheal tube, check placement of a tracheotomy cuff, check a mask or other noninvasive patient-interface accessary, replace a portion of a patient circuit, and replace the patient circuit.

[0014] In some examples, the at least one controller is configured to determine whether the gas leak is detected based at least in part on whether a difference between an inspiratory volume and an expiratory' volume is greater than a specified threshold. In some examples, the at least one controller is configured to: determine whether an obstructive respiratory mechanics condition is detected, and if the obstructive respiratory' mechanicscondition is detected, then determine the output to relate to at least one of: an increase in a flow cycling threshold, a decrease in pressure support, and a decrease in pressure rise time. In some examples, the at least one controller is configured to determine whether the obstructive respiratory mechanics condition is detected based at least in part on whether the patient’s respiratory resistance is greater than a specified respiratory7resistance threshold. In some examples, the type of PVA comprises short cycling, and wherein the at least one controller is configured to determine the output to relate to at least one of: an increase in inspiratory time, and a decrease in flow cycle threshold.

[0015] In some examples, the ty pe of PVA comprises short cycling, and wherein the at least one controller is configured to: determine whether a restrictive respiratory7mechanics condition is detected, and if the restrictive respiratory mechanics condition is detected, then determine the output to relate to an increase in pressure support. In some examples, the at least one controller is configured to determine whether the restrictive respiratory mechanics condition is detected based at least in part on whether the patient's respiratory7compliance is less than a specified respiratory compliance threshold. In some examples, the type of PVA comprises insufficient flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is volume targeted, determine whether an inspiratory7flow is below a specified inspiratory7flow threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the inspiratory flow is below the specified inspiratory flow threshold, then determine the output to relate to an increase in the inspiratory flow. In some examples, the type of PVA comprises insufficient flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is pressure targeted, determine whether a patient airway pressure is below a specified airway pressure threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the pressure is below the specified airw ay pressure threshold, then determine the output to relate to an increase in the patient airway pressure.

[0016] In some examples, the type of PVA comprises insufficient flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is pressure targeted, determine whether a patient airway pressure rise time is above a specified rise time threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the patient airway pressure rise time is above the specified rise time threshold, then determine the output to relate to a decrease pressure rise time. In some examples, the type of PVA comprises insufficient flow, andwherein the at least one controller is configured to: determine whether at least one of: excessive ventilator demand and excessive neural drive is detected, and if at least one of: the excessive ventilator demand, and the excessive neural drive is detected, then determine the output to relate to at least one of: a reduction in metabolic demand, and a reduction in the neural drive.

[0017] In some examples, the type of PVA comprises excessive flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is volume targeted, determine whether an inspiratory flow is above a specified inspiratory flow threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the inspiratory flow is above the specified inspiratory flow threshold, then determine the output to relate to a decrease in the inspiratory flow. In some examples, the type of PVA comprises excessive flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is pressure targeted, determine whether a patient airway pressure is above a specified airwaypressure threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the patient airway pressure is above the specified airway pressure threshold, then determine the output to relate to a decrease in the patient airway pressure. In some examples, the type of PVA comprises excessive flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is pressure targeted, determine whether a patient airway pressure rise time is below a specified rise time threshold, and if the mechanical ventilation being provided to the patient is pressure targeted, and if the patient airw ay pressure rise time is below the specified rise time threshold, then determine the output to relate to an increase the patient airway pressure rise time.

[0018] In some examples, the type of PVA comprises excessive flowy and wherein the at least one controller is configured to: determine whether at least one of: low- ventilator demand, and low neural drive is detected, and if at least one of: the low ventilator demand, and the excessive neural drive is detected, then determine the output to relate to at least one of: a reduction in metabolic demand, and a reduction in the neural drive. In some examples, the type of PVA comprises double triggering, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is volume targeted, determine whether tidal volume is below a specified tidal volume threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if thetidal volume is below the specified tidal volume threshold, then determine the one or more PVA mitigation notifications to include at least one of a message to: increase sedation or neuromuscular blockages, and change to pressure targeted mechanical ventilation. In some examples, the type of PVA comprises double triggering, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is volume targeted, determine whether patient respiratory compliance is below a specified respiratory resistance threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the patient respiratory compliance is below a specified respiratory compliance threshold, then determine the output to relate to a decrease in a flow cycling threshold.

[0019] One example provides a system for managing patient ventilator asynchrony (PVA) during mechanical ventilation therapy of a patient, the system comprising: a mechanical ventilation system for providing mechanical ventilation to the patient; and at least one controller, communicatively coupled with the mechanical ventilation system, the at least one controller configured to: obtain input comprising: PVA data identifying a type of PVA, ventilatory parameter data identifying one or more ventilatory parameters relating to the mechanical ventilation being provided to the patient, patient data identifying one or more patient characteristics comprising at least one of: patient sex, patient age, patient height, a patient body weight related characteristic, and a patient respiratory- mechanics parameter, and compare at least a portion of the obtained input to at least one specified threshold, the at least a portion of the obtained input comprising at least a portion of the ventilatory parameter data, and determine output based at least in part on the obtained input and the comparison, the output comprising at least one of: one or more PVA mitigation notifications to provide guidance relating to one or more measures to be taken to mitigate the type of PVA, and at least one adjustment to at least one ventilatory parameter. In examples in which the output is determined to relate to an increase or decrease, for example, the output may be the one or more PVA mitigation notifications and / or the at least one adjustment relating to the mechanical ventilation being provided to the patient.

[0020] In some examples, the type of PVA is a detected type of PVA. In some examples, the at least one adjustment is for implementation to mitigate the type of PVA. In some examples, the one or more PVA mitigation notifications are for presentation to a care provider. In some examples, the one or more PVA mitigation notifications comprises root cause information associated with the type of PVA. In some examples, the one or morepatient characteristics further comprise at least one past or present medical related or health related condition of the patient. In some examples, the at least one specified threshold is associated with at least one the at least one specified magnitude comprises at least one specified numerical magnitude. In some examples, the comparison comprises determining whether at least one ventilatory' parameter, of the one or more ventilatory parameters, meets the at least one specified threshold. In some examples, the at least one specified threshold comprises a plurality of thresholds. In some examples, the type of PVA comprises ineffective efforts during expiration, and wherein the at least one controller is configured to determine the output to relate to an increase in trigger sensitivity.

[0021] In some examples, the type of PVA comprises ineffective efforts during expiration, and wherein the at least one controller is configured to: detect whether autopositive end expiratory pressure (auto-PEEP) is present, and if auto-PEEP is detected, determine the output to relate to at least one of: an increase in PEEP, an increase expiratory time, a decrease in inspiratory time, and a decrease in respiratory rate. In some examples, the type of PVA comprises ineffective efforts during expiration, and wherein the at least one controller is configured to: determine whether patient respiratory muscle effort, associated with the ineffective efforts during expiration, is less than a specified threshold, determine whether respirator}' rate is not equal to a set respiratory rate, and if the patient respiratory' muscle effort is less than the specified threshold, and if the respiratory rate is not equal to the set respiratory rate, then determine the output to relate to a decrease in sedation. In some examples, the type of PVA comprises ineffective efforts during expiration, and wherein the at least one controller is configured to: determine whether measured tidal volume is greater than a specified tidal volume threshold, and if the measured tidal volume is greater than the specified tidal volume threshold, then determine the output to relate to a reduction in pressure support.

[0022] In some examples, the specified tidal volume threshold is between 6 and 10 ml / kg of a body weight associated with the patient, and wherein the body weight is predicted or obtained based at least in part on patient sex and patient height. In some examples, the at least one controller is configured to: if the measured tidal volume is greater than the specified tidal volume threshold, then determine the output to relate to a reduction in pressure support to deliver a tidal volume of between 4 and 10 ml / kg of body weight. In some examples, the type of PVA comprises autotriggering, and wherein the at least one controller is configured to determine the output to relate to a decrease in trigger sensitivity. In some examples, the type of PVA comprises autotriggering, and wherein the at least one controller is configured to:determine whether a gas leak is detected relating to the mechanical ventilation system, and if the gas leak is detected, then determine the one or more PVA mitigation notifications to include at least one of a message to: check the mechanical ventilation system for loose connections, check exhalation valve, check placement of an endotracheal tube, check placement of a tracheotomy cuff, check a mask or other noninvasive patient-interface accessary, replace a portion of a patient circuit, and replace the patient circuit.

[0023] In some examples, the at least one controller is configured to determine whether the gas leak is detected based at least in part on whether a difference between an inspiratory volume and an expiratory volume is greater than a specified threshold. In some examples, the type of PVA comprises autotriggering, and wherein the at least one controller is configured to: determine whether condensate is detected in a patient circuit of the mechanical ventilation system, and if the condensate is detected, then determine the one or more PVA mitigation notifications to include a message to remove condensate from the patient circuit. In some examples, the type of PVA comprises prolonged cycling, and wherein the at least one controller is configured to determine the output to relate to at least one of: a decrease in inspiratory time, and an increase in flow cycle threshold. In some examples, the at least one controller is configured to: determine whether a gas leak relating to the mechanical ventilation system is detected, and if the gas leak is detected, then determine the one or more PVA mitigation notifications to include at least one of a message to: check the mechanical ventilation system for loose connections, check exhalation valve, check placement of an endotracheal tube, check placement of a tracheotomy cuff, check a mask or other noninvasive patient-interface accessary, replace a portion of a patient circuit, and replace the patient circuit.

[0024] In some examples, the at least one controller is configured to determine whether the gas leak is detected based at least in part on whether a difference between an inspiratory volume and an expiratory volume is greater than a specified threshold. In some examples, the at least one controller is configured to: determine whether an obstructive respiratory mechanics condition is detected, and if the obstructive respiratory mechanics condition is detected, then determine the output to relate to at least one of: an increase in a flow cycling threshold, a decrease in pressure support, and a decrease in pressure rise time. In some examples, the at least one controller is configured to determine whether the obstructive respiratory mechanics condition is detected based at least in part on whether the patient’s respiratory resistance is greater than a specified respiratory compliance threshold. In some examples, the type of PVA comprises short cycling, and wherein the at least one controller isconfigured to determine the output to relate to at least one of: an increase in inspiratory time, and a decrease in flow cycle threshold. In some examples, the type of PVA comprises short cycling, and wherein the at least one controller is configured to: determine whether a restrictive respiratory mechanics condition is detected, and if the restrictive respiratory mechanics condition is detected, then determine the output to relate to an increase in pressure support.

[0025] In some examples, the at least one controller is configured to determine whether the restrictive respiratory mechanics condition is detected based at least in part on whether the patient’s respiratory compliance is less than a specified respiratory' compliance threshold. In some examples, the type of PVA comprises insufficient flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is volume targeted, determine whether an inspiratory flow is below a specified inspiratory' flow threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the inspiratory' flow is below the specified inspiratory' flow threshold, then determine the output to relate to an increase in the inspiratory flow. In some examples, the type of PVA comprises insufficient flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is pressure targeted, determine whether a patient airway pressure is below a specified airway pressure threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the pressure is below the specified airway pressure threshold, then determine the output to relate to an increase in the patient airway pressure.

[0026] In some examples, the type of PVA comprises insufficient flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is pressure targeted, determine whether a patient airwaypressure rise time is above a specified rise time threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the patient airway pressure rise time is above the specified rise time threshold, then determine the output to relate to a decrease the patient airway pressure rise time. In some examples, the type of PVA comprises insufficient flow, and wherein the at least one controller is configured to: determine whether at least one of: excessive ventilator demand and excessive neural drive is detected, and if at least one of: the excessive ventilator demand, and the excessive neural drive is detected, then determine the output to relate to at least one of: a reduction in metabolic demand, and a reduction in the neural drive. In some examples, the type of PVA comprises excessive flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation beingprovided to the patient is volume targeted, determine whether an inspirator)' flow is above a specified inspiratory flow threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the inspiratory flow is above the specified inspiratory flow threshold, then determine the output to relate to a decrease in the inspiratory flow.

[0027] In some examples, the type of PVA comprises excessive flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is pressure targeted, determine whether a patient airway pressure is above a specified airway pressure threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the patient airway pressure is above the specified airway pressure threshold, then determine the output to relate to a decrease in the patient airway pressure. In some examples, the type of PVA comprises excessive flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is pressure targeted, determine whether a patient airwaypressure rise time is below a specified rise time threshold, and if the mechanical ventilation being provided to the patient is pressure targeted, and if the patient airway pressure rise time is below the specified rise time threshold, then determine the output to relate to an increase the patient airway pressure rise time. In some examples, the type of PVA comprises excessive flow, and wherein the at least one controller is configured to: determine whether at least one of: low ventilator demand, and low neural drive is detected, and if at least one of: the low ventilator demand, and the low neural drive is detected, then determine the output to relate to at least one of: a reduction in metabolic demand, and a reduction in the neural drive.

[0028] In some examples, the type of PVA comprises double triggering, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is volume targeted, determine whether tidal volume is below a specified tidal volume threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the tidal volume is below the specified tidal volume threshold, then determine the one or more PVA mitigation notifications to include at least one of a message to: increase sedation or neuromuscular blockages, and change to pressure targeted mechanical ventilation. In some examples, the type of PVA comprises double triggering, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is volume targeted, determine whether patient respiratory resistance is below a specified respiratory- resistance threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the patient respiratory resistance is below a specified respiratory resistance threshold, then determine theoutput to relate to a decrease in a flow cycling threshold.

[0029] One example provides a method for managing patient ventilator asynchrony (PVA) during mechanical ventilation therapy of a patient, the method comprising at least one controller, communicatively coupled with a mechanical ventilation system for providing mechanical ventilation to the patient: obtaining a detected ty pe of PVA; determining a measure of PVA severity for the detected type of PVA, comprising determining a magnitude of PVA severity for each of one or more patient breaths; and providing, based at least in part on the detected type of PVA and the measure of PVA severity for each of one or more patient breaths, in relation to one or ventilatory parameters relating to the mechanical ventilation being provided to the patient, at least one of: one or more PVA mitigation notifications and at least one adjustment relating to the mechanical ventilation being provided to the patient. In some examples, the one or more patient breaths comprises multiple patient breaths. In some examples, determining the measure of PVA severity comprises determining the measure of PVA severity' for a period of time including the multiple patient breaths. In some examples, the method comprises obtaining, by the at least one controller, patient data identifying one or more patient characteristics comprising at least one of: patient sex, patient age. patient height, a patient body weight related characteristic, and a patient respiratory mechanics parameter, and comprising providing the at least one of: one or more PVA mitigation notifications and at least one adjustment relating to the mechanical ventilation being provided to the patient based at least in part on the patient data. In examples in which the output is determined, including being determined to relate to an increase or decrease, for example, the output may be the one or more PVA mitigation notifications and / or the at least one adjustment relating to the mechanical ventilation being provided to the patient.

[0030] In some examples, determining the magnitude of PVA severity for each of the one or more patient breaths comprises analyzing at least one of: a patient airway pressure waveform and a patient airway flow waveform, associated with a period of time during which the detected type of PVA is determined to be present. In some examples, the patient airway pressure waveform is obtained using measured patient airway pressure. In some examples, the patient airway flow waveform is obtained using measured patient airway flow. In some examples, determining the magnitude of PVA severity for each of one or more patient breaths comprises at least one of: comparing a patient airway pressure waveform to an airway pressure waveform for which the detected type of PVA is not present, and comparing a patient airway flow waveform to an airway flow waveform for which the detected type of PVA is not present. In some examples, determining the magnitude of PVA severity for eachof one or more patient breaths is based at least in part on an estimated work of breathing of the patient, wherein the estimated work of breathing of the patient is based at least in part on measured patient airway pressure. In some examples, determining the magnitude of PVA severity for the detected type of PVA for each of the one or more patient breaths comprises: obtaining a first airway pressure waveform associated with a breath for which the detected type of PVA is not present, obtaining a patient airway pressure waveform associated with a patient breath of the one or more breaths for which the detected type of PVA is determined to the present, comparing the first airway pressure waveform to the patient airway pressure waveform to determine a deviation between the first airway pressure waveform and the patient airway pressure waveform, determining a magnitude of the deviation, and based at least in part on the magnitude of the deviation, determining the magnitude of PVA severity.

[0031] In some examples, determining the deviation comprises: determining, for the patient airway pressure waveform, a quantified graphical area associated with the detected type of PVA, and determining the magnitude of the deviation based on the quantified graphical area. In some examples, determining the magnitude of PVA severity for the detected type of PVA for each of the one or more patient breaths comprises: obtaining a first airway flow waveform associated with a breath for which insufficient flow is not present, obtaining a patient airway flow waveform associated with a patient breath of the one or more breaths, for which the detected type of PVA is determined to the present, comparing the first airway flow waveform to the patient airway flow waveform to determine a deviation between the first airway flow waveform and the patient airway flow waveform, determining a magnitude of the deviation, and based at least in part on the magnitude of the deviation, determining the magnitude of PVA severity.

[0032] In some examples, determining the deviation comprises: determining, for the patient airway flow waveform, a quantified graphical area associated with the detected type of PVA, and determining the magnitude of the deviation based on the quantified graphical area. In some examples, determining the magnitude of the deviation to be proportional to the quantified graphical area. In some examples, determining a plurality of ranges of the magnitude of PVA severity, wherein each of the ranges is associated with a category relating to a level of severity of the detected type of PVA. In some examples, the categories comprise a mild category', a moderate category' and a severe category, wherein a range associated with the moderate category is higher than a range associated with the mild category, and wherein a range associated with the severe category is higher than the range associated with themoderate category.

[0033] In some examples, the detected type of PVA is insufficient flow, and wherein the mild category is associated with the magnitude of the deviation in patient airway pressure in a range of between 0.4-0.8 cm H2O * second, the moderate category' is associated with the magnitude of the deviation in patient airw ay pressure in a range of between 2.0-2.4 cm H2O * second, and the severe category is associated with the magnitude of the deviation in patient airway pressure at or above 7.9 cm H2O * second. In some examples, the detected type of PVA is insufficient flow, and wherein the mild category' is associated with the magnitude of the deviation in patient airway pressure of 0.6 cm H20 * second, the moderate category is associated with the magnitude of the deviation in patient airway pressure of 2.2 cm H2O * second, and the severe category is associated with the magnitude of the deviation in patient airway pressure at or above 7.9 cm H2O * second.

[0034] In some examples, the detected type of PVA is ineffective efforts during expiration, and wherein the mild category is associated with the magnitude of the deviation in patient airway pressure in a range of between 0.08-0. 12 cm H2O * second, the moderate category is associated with the magnitude of the devration in patient arrway pressure in a range of between 0.3-0.5 cm H2O * second, and the severe category is associated with the magnitude of the deviation in patient airway pressure at or above 1.3 cm H2O * second. In some examples, the detected type of PVA is ineffective efforts during expiration, and wherein the mild category is associated with the magnitude of the deviation in patient airway pressure of 0.1 cm H2O * second, the moderate category is associated with the magnitude of the deviation in patient airway pressure of 0.4 cm H2O * second, and the severe category is associated with the magnitude of the deviation in patient airway pressure at or above 1.3 cm H2O * second. In some examples, the method comprises determining a plurality of ranges of the magnitude of PVA severity, wherein each of the ranges is associated with a category relating to a level of severity of the detected type of PVA. In some examples, the categories comprise a mild category, a moderate category' and a severe category, wherein a range associated with the moderate category is higher than a range associated with the mild category, and wherein a range associated with the severe category' is higher than the range associated with the moderate category.

[0035] In some examples, the detected type of PVA is ineffective efforts during expiration, and wherein the mild category is associated with the magnitude of the deviation in patient airway flow in a range of between 0.3-0.5 1 / min * sec. the moderate category is associated with the magnitude of the deviation in patient airway flow in a range of between1.3-1.5 1 / min * sec, and the severe category is associated with the magnitude of the deviation in patient airway flow at or above 3.8 1 / min * sec. In some examples, the detected type of PVA is ineffective efforts during expiration, and wherein the mild category is associated with the magnitude of the deviation in patient airway flow of 0.4 1 / min * sec, the moderate category7is associated with the magnitude of the deviation in patient airway flow of 1.4 1 / min * sec, and the severe category is associated with a magnitude of the deviation in patient airway flow at or above 3.8 1 / min * sec. In some examples, the method comprises determining the magnitude of PVA severity for each of the one or more patient breaths as a number, wherein the number is proportional to the magnitude of PVA severity. In some examples, the method comprises determining, based on the number, a PVA severity score between 0 and 1. wherein the PVA severity score is proportional to the number.

[0036] In some examples, determining the measure of PVA severity for the period of time comprises determining a PVA severity index, and wherein determining the PVA severity index comprises: for each of the multiple patient breaths that are synchronous, assigning a PVA severity score of 0. for each of the multiple patient breaths that include the detected type of PVA, assigning a PVA severity score associated with PVA severity, and determining the PVA severity index based on the assigned PVA severity scores for each of the multiple patient breaths. In some examples, the method comprises determining, based on the number, a PVA severity score between 0 and 1, wherein: when the number is between 0 and a specified threshold, the PVA severity score is determined to be proportional to the number, when the number is at or above the specified threshold, the PVA severity score is determined to be 1. In some examples, the measure of severity' is based at least in part on an estimated work of breathing of the patient estimated on a breath by breath basis, wherein the estimated work of breathing of the patient is based at least in part on measured patient airway pressure.

[0037] In some examples, the one or more patient breaths comprises a plurality' of successive breaths. In some examples, determining the measure of PVA severity comprises utilizing the magnitude of PVA severity associated with each of the plurality of successive breaths. In some examples, determining the measure of PVA severity comprises: determining a moving average relating to the plurality of successive breaths over a specified period of time, and determining the measure of PVA severity based at least in part on the moving average. In some examples, the detected type of PVA comprises insufficient flow, and wherein determining the deviation comprises, for a period of time associated with the insufficientflow, determining decreased patient airw ay pressure in the patient airway pressure waveform relative to the first airway pressure waveform. In some examples, the detected type of PVA comprises ineffective efforts during expiration, and wherein determining the deviation comprises, for a period of time associated with the ineffective efforts during expiration, determining decreased patient airway pressure in the patient airway pressure waveform relative to the first airway pressure waveform.

[0038] In some examples, the detected type of PVA comprises ineffective efforts during expiration, and wherein determining the deviation comprises, for a period of time associated with the ineffective efforts during expiration, determining decreased patient airway flow in the patient airway flow w aveform relative to the first airway flow w aveform. In some examples, the one or patient breaths comprises a plurality of successive patient breaths, and wherein determining the measure of PVA severity comprises determining a numerical PVA severity score representative of PVA severity over a specified period of time. In some examples, determining the PVA severity' score comprises: identifying each of the plurality of successive patient breaths as either a synchronous breath if the breath does not include any type of PVA or an asynchronous breath if the breath includes any type of detected PVA, for each of the synchronous breaths, assigning the magnitude of PVA severity to be 0, for each of the asynchronous breaths, determining the magnitude of PVA severity' for the asynchronous breath, and determining the PVA severity score based on the magnitude of PVA severity’ for each of the synchronous breaths and each of the asynchronous breaths.

[0039] In some examples, determining the PVA severity score comprises an average of the magnitude of PVA severity for all of the plurality' of breaths. In some examples, determining the magnitude of PVA severity for each of the asynchronous breaths as a number between 0-1, wherein the number is proportional to the magnitude of PVA severity, and determining the PVA severity score as a numerical score between 0-1. In some examples, determining the measure of PVA severity comprises: determining a moving average over a specified period of time for the PVA severity’ score, identifying a PVA cluster period as a period of time in which the moving average of the PVA severity score is continuously above a specified threshold for at least a specified amount of time, determining the measure of PVA severity for the specified period of time based at least in part on the PVA cluster period. In some examples, the specified amount of time is between 45 seconds and 75 seconds. In some examples, the specified amount of time is 1 minute. In some examples, the measure of PVA severity is increased based on the PVA cluster period. In some examples, the measure of PVA severity' is determined to be a number between 0-1, and wherein the specified thresholdis between 0.3-0.7. In some examples, the measure of PVA severity is determined to be a number between 0-1, and wherein the specified threshold is 0.5. In some examples, the measure of PVA severity is determined to be a number between 0-1, and wherein the specified threshold is between 0.5.

[0040] In some examples, the method comprises: determining a quantified graphical area associated with a moving average of the PVA severity score over the PVA cluster period. determining a cluster power, associated with the PVA cluster period, based on the quantified graphical area, and determining the measure of PVA severity for the specified period of time based at least in part on the cluster power. In some examples, the at least one of: one or more PVA mitigation notifications and at least one adjustment relating to the mechanical ventilation being provided to the patient comprises a quantified adjustment to a first ventilatory parameter of the one or more ventilator}' parameters. In some examples, the quantified adjustment is proportional to the measure of PVA severity. In some examples, each of a plurality of specified ranges of the measure of PVA severity are associated with a specific quantified adjustment. In some examples, the plurality of specified ranges of the measure of PVA severity correspond to categories relating to levels of the measure of PVA severity comprising a mild category7, a moderate category7and a severe category7, wherein a range associated with the moderate category is higher than a range associated with the mild category, and wherein a range associated with the severe category- is higher than the range associated with the moderate category.

[0041] In some examples, the method comprises: monitoring, by the at least one controller, the measure of PVA severity over time following implementation of a first measure to mitigate the detected type of PVA. determining, by the controller, based on the monitored measure of PVA severity, that one or more conditions have been met to trigger providing a recommendation to implement a second measure to mitigate the detected type of PVA, and providing the recommendation to a care provider to implement the second measure. In some examples, satisfaction of the one or more conditions indicates that the first measure did not meet one or more specified criteria of effectiveness in mitigating the detected ty pe of PVA over a first period of time. In some examples, the one or more conditions comprises at least one of: a condition that the measure of PVA severity7has not decreased during a specified period of time following implementation of the first measure, and that the measure of PVA severity7has not decreased at least to a specified threshold during the specified period of time following implementation of the first measure.

[0042] One example provides a system for managing patient ventilator asynchrony (PVA) during mechanical ventilation therapy of a patient, the system comprising: a mechanical ventilation system for providing mechanical ventilation to the patient: and at least one controller, communicatively coupled with the mechanical ventilation system, the at least one controller configured to: obtain a detected type of PVA, determine a measure of PVA severity for the detected type of PVA, and provide, based at least in part on the detected type of PVA and the measure of PVA severity, in relation to one or more ventilatory parameters relating to the mechanical ventilation being provided to the patient, at least one of: one or more PVA mitigation notifications and at least one adjustment relating to the mechanical ventilation being provided to the patient, wherein providing the at least one of: one or more PVA mitigation notifications and at least one adjustment relating to the mechanical ventilation being provided to the patient comprises determining an adjustment relating to the mechanical ventilation being provided to the patient, the adjustment comprising an amount of a change to at least one of the one or more ventilatory parameters.

[0043] In some examples, the at least one controller is configured to obtain patient data identifying one or more patient characteristics comprising at least one of: patient sex, patient age, patient height, a patient body weight related characteristic, and a patient respiratory7mechanics parameter, and to provide the at least one of: one or more PVA mitigation notifications and at least one adjustment relating to the mechanical ventilation being provided to the patient based at least in part on the patient data. In some examples, determining the measure of PVA severity comprises determining a magnitude of PVA severity for each of one or more patient breaths. In some examples, the one or more patient breaths comprises a plurality of successive breaths. In some examples, the method comprises determining the amount of the change based at least in part on the magnitude of PVA severity for each of the plurality of successive breaths. In some examples, the method comprises determining the magnitude of PVA severity for each of the one or more patient breaths as a number, wherein the number is proportional to the magnitude of PVA severity. In some examples, the at least one controller is configured to determine, based on the number, a PVA severity score between 0 and 1, wherein the PVA severity score is proportional to the number, and to determine the amount of the change based at least in part on the PVA severity score. In some examples, the at least one controller is configured to determine, based on the number, a PVA severity score between 0 and 1, and to determine the amount of the change based at least in part on the PVA severity score, wherein: when the number is between 0 and a specified threshold, the PVA severity score is determined to be proportional to the number,when the number is at or above the specified threshold, the PVA severity score is determined to be 1.

[0044] One example provides a method for managing patient ventilator asynchrony (PVA) during mechanical ventilation therapy of a patient, the method comprising: determining presence of a type of PVA; determining a measure of PVA severity7for the ty pe of PVA determined to be present; and based at least in part on the determined measure of PVA severity7, adjusting at least one ventilatory parameter relating to the mechanical ventilation being provided to the patient to mitigate the detected type of PVA. In some examples, the adjusting is based at least in part on patient data identifying one or more patient characteristics comprising at least one of: patient sex, patient age. patient height, a patient body weight related characteristic, and a patient respiratory mechanics parameter. In some examples, determining the measure of PVA severity7comprises determining a magnitude of PVA severity7for each of one or more patient breaths. In some examples, the one or more patient breaths comprises a plurality of patient breaths. In some examples, the adjusting is based at least in part on the magnitude of PVA severity for each of the plurality7of patient breaths.

[0045] In some examples, the method includes determining the magnitude of PVA severity7for each of the plurality7of patient breaths of the as a number, wherein the number is proportional to the magnitude of PVA severity. In some examples, the method including determining, based on the number, a PVA severity score between 0 and 1, wherein the PVA severity score is proportional to the number, and wherein the adjusting is based at least in part on the PVA severity score. In some examples, the method includes determining, based on the number, a PVA severity score between 0 and 1, wherein the adjusting is based at least in part on the PVA severity score, and wherein: when the number is between 0 and a specified threshold, the PVA severity score is determined to be proportional to the number, when the number is at or above the specified threshold, the PVA severity score is determined to be 1. In some examples, the determining the magnitude of PVA severity for each of the plurality of patient breaths comprises: obtaining a first airway pressure waveform associated with a breath for which the detected type of PVA is not present, obtaining a patient airway pressure waveform associated with a patient breath of the one or more breaths for which the detected ty pe of PVA is determined to the present, comparing the first airway pressure waveform to the patient airway pressure waveform to determine a deviation between the first airway pressure waveform and the patient airway pressure waveform, determining a magnitude of the deviation, and based at least in part on the magnitude of the deviation, determining themagnitude of PVA severity.

[0046] One example provides a system for managing patient ventilator asynchrony (PVA) during mechanical ventilation therapy of a patient, the system comprising at least one controller, communicatively coupled with a mechanical ventilation system for providing mechanical ventilation to the patient, the controller configured to: obtain a detected type of PVA; determine a measure of PVA severity for the detected type of PVA, comprising determining a magnitude of PVA severity for each of one or more patient breaths; and provide, based at least in part on the detected type of PVA and the measure of PVA severity for each of one or more patient breaths, in relation to one or ventilatory parameters relating to the mechanical ventilation being provided to the patient, at least one of: one or more PVA mitigation notifications and at least one adjustment relating to the mechanical ventilation being provided to the patient.

[0047] One example provides a system for managing patient ventilator asynchrony (PVA) during mechanical ventilation therapy of a patient, the system comprising: a mechanical ventilation system for providing mechanical ventilation to the patient; and at least one controller, communicatively coupled with the mechanical ventilation system, the at least one controller configured to: obtain input comprising: PVA data, ventilatory parameter data identifying one or more ventilatory parameters relating to the mechanical ventilation being provided to the patient, and determine output based at least in part on the obtained input, the output comprising at least one of: one or more PVA mitigation notifications, which may instruct a caregiver to adjust one or more ventilatory’ parameters of the mechanical ventilation system, such as by an amount determined based at least on the PVA data and the ventilatory parameter data, and at least one adjustment relating to the mechanical ventilation being provided to the patient, wherein said adjustment may comprise a signal to the mechanical ventilation system to cause automatic adjustment of one or more ventilatory parameters of the mechanical ventilation system.BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Various aspects of embodiments of the present disclosure are discussed below with reference to the accompanying figures, which are not intended to be drawn to scale. The figures are included for illustrative purposes and a further understanding of the variousaspects and examples. The figures are incorporated in and constitute a part of this specification, but are not intended to limit the scope of the disclosure. In the figures, identical or nearly identical components that are illustrated in various figures may be represented by like numerals. For purposes of clarity, not every component may be labeled in every figure.

[0049] FIGs. 1-4 illustrate example systems including patient ventilator asynchrony managers (PVAMs) for use in providing PVA mitigation notifications and adjustments.

[0050] FIG. 5 is a block diagram illustrating example PVAM operation.

[0051] FIG. 6 is a block diagram illustrating example techniques for determining a measure of PVA severity based on patient airway pressure, including using estimated work of breathing, pressure-time product, and based on airway occlusion pressure.

[0052] FIG. 7 illustrates an example of use of estimated work of breathing in determining a measure of PVA severity for use in determining PVA mitigation notifications and adjustments.

[0053] FIG. 8 includes plots illustrating an example of determining a PVA severity score for a period of time based on estimated work of breathing.

[0054] FIG. 9 illustrates an example including determining a measure of PVA severity for a period of multiple breaths, using a determined magnitude of PVA severity for each breath.

[0055] FIG. 10 is a flow diagram illustrating a method for determining a magnitude of PVA severity’ for a single patient breath, including comparison of an asynchronous patient airway pressure or flow waveform with a sample synchronous airway pressure or flow waveform.

[0056] FIG. 11 A includes plots illustrating an example of determining amounts of deviation between each of multiple patent breaths and a sample synchronous breath, for use in determining a measure of PVA severity for a period of time.

[0057] FIG. 1 IB includes plots including a close-up of a portion of the patient airway pressure plot of FIG. 11 A, showing several waveform deviations and the corresponding determined deviation amounts, for use in determining a measure of PVA severity over a period of time.

[0058] FIG. 12 includes plots illustrating an example of determining a PVA severity score over a period of time, including the period of time of FIGs. 11A-B, using a moving average of determined waveform deviations.

[0059] FIG. 13 is a block diagram illustrating an example of calculation of a measure of PVA severity based on factors relating to work of breathing and waveform deviation.

[0060] FIG. 14 includes plots illustrating an example of determination and use of PVA clusters in determining a measure of PVA severity- for a period of time, including use of a PVA severity index.

[0061] FIG. 15 includes plots illustrating an example of use of ranges of determined PVA severity- scores in determining amounts of adjustments to ventilatory- parameters to mitigate a detected type of PVA.

[0062] FIG. 15 A includes plots illustrating an example of use of determined categories of PVA severity- for a detected type of PVA in determining and presenting a recommended measure to mitigate the detected ty pe of PVA.

[0063] FIG. 16 is a flow diagram illustrating an example method for providing recommendations for mitigation of PVA severity, which may include a series of recommended measures.

[0064] FIG. 17 includes a plot illustrating an example of use of a method for providing recommendations for mitigation of PVA severity.

[0065] FIG. 18 is a block diagram illustrating an example of data utilized by a PVAM, and example operation of a PVAM.

[0066] FIG. 19 is a table of some example types of PVA, for which PVA mitigation notifications and adjustments may be determined.

[0067] FIG. 20 is an example flow diagram relating to ineffective efforts during expiration (IEE) PVA type.

[0068] FIG. 21 includes plots illustrating an example of determining a category of PVA severity- based on determined waveform deviation for IEE.

[0069] FIG. 22 is an example flow diagram relating to autotriggering (AT) PVA type.

[0070] FIG. 23 is an example flow diagram relating to double triggering (DT) PVA ty pe.

[0071] FIG. 24 is an example flow diagram relating to prolonged cycling (PC) PVA type.

[0072] FIG. 25 is an example flow diagram relating to short cycling (SC) PVA ty pe.

[0073] FIG. 26 is an example flow diagram relating to insufficient flow (IF) PVA ty pe.

[0074] FIG. 27 includes plots illustrating an example of determining a category of PVA severity- based on determined waveform deviation for IF.

[0075] FIG. 28 is an example flow diagram relating to excessive flow (EF).

[0076] FIG. 29 is a block diagram illustrating example software related aspects thatmay be used in a ventilation system including a PVAM. which software aspects may include physiologic closed-loop control (PCLC) of FIO2, ventilation and PEEP.

[0077] FIG. 30 is a block diagram of an example portable ventilator that can be used in a ventilation system including a PVAM.

[0078] FIG. 31 is a block diagram illustrating example aspects of a mechanical ventilation apparatus of a portable ventilator that can be used in a ventilation system including a PVAM.

[0079] FIG. 32 is an illustration of an example portable ventilator and display or user interface, with FIO2 closed loop control, which can be used in a ventilation system including a PVAM

[0080] FIG. 33 illustrates example aspects of patient circuits that can be used in a ventilation system including a PVAM.

[0081] FIG. 34 is a block diagram illustrating example components of various devices described with reference to preceding figures.DETAILED DESCRIPTION

[0082] Patent ventilator asynchrony (PVA) may result, for example, from a mismatch between the patient and the ventilator, such as may relate, e.g., to breath initiation (triggering), breath termination (cycling), airw ay flow or air ay pressure. PVA may be detected, such as manually or algorithmically, e.g., using airway pressure and flow waveforms, which may include features or patterns associated with particular types of PVA. In some embodiments, PVA management may include, e.g., PVA mitigation. PVA mitigation may include, e.g., measures determined and taken, such as manually or algorithmically, to mitigate, which may include eliminate, PVA or a detected type of PVA. PVA mitigation measures may include, for example, adjustment of specific ventilatory parameters or other measures. The PVA mitigation measures may, for example, mitigate a detected PVA and thereby reduce negative effects associated with the PVA, both immediate and lasting, and may substantially improve PVA management as well as mechanical ventilation patient outcomes.

[0083] Ventilatory parameters may include, e.g., any parameter relating to ventilation, such as mechanical ventilation being provided to a patient, which may include, e.g., any of various ventilation related settings that are directly controlled by the user (e.g., the ventilation mode, a PEEP setting of 5 cm H2O, inflation type, etc.), measured ventilation related dependent parameters (e.g., a measured airway flow' rate in l / min, measured patient airwaypressure in cm H20, exhaled volume, etc.) ventilatory rate, ventilatory volume, ventilatory pressure, provided breathing gas and breathing gas make-up, and others.

[0084] In various embodiments, ventilation or ventilation modes may include, e.g., volume targeting and / or pressure targeting. In some embodiments, volume targeting may assure that a constant volume is delivered to the patient in an inspiratory7time using a constant flow rate. During volume targeting, the measured PIP parameter is displayed or highlighted. Pressure targeting may assure a constant airway pressure for the duration of the inspiratory time. In some embodiments, volume targeting may include delivery of a set amount of breathing gas to a patient while adjusting pressure.

[0085] In some embodiments, a controller, e.g., coupled with a mechanical ventilation system (e.g., whether or not the controller is part of the mechanical ventilation system), may be included on one or more devices or systems, and includes all hardware (e.g., processor(s), central processing units (CPUs) and memories), software and programming used in implementing embodiments described herein, including, e.g., as relates to closed loop control of ventilatory7or other parameters, and as relates to embodiments associated with asynchrony, such as in asynchrony detection, management, mitigation, adjustments, and associated communication and messaging, such as to a user. In some embodiments, for example, the controller may include a PVA manager (PVAM), embodiments of which are described herein.

[0086] In some embodiments, a PVAM. which may be. e.g., of the controller and may be software based or include software based aspects, may be used in managing PVA root cause and mitigation notifications and / or adjustments, which may include, e.g., determining and / or providing, which may include, e.g., presenting and / or implementing, PVA root cause and mitigation notifications and / or adjustments. In various embodiments, such adjustments may or may not be incorporated into closed-loop control. In some embodiments, PVA mitigation notifications may include information about one or more root causes, or possible or likely root causes, of the detected ty pe of PVA. This may include, for example, information about physiological conditions (which may include, e.g., states, disease states, problems, situations, etc.) that may represent physiological root causes. Examples of conditions that may represent various such potential root causes are provided herein. As one example, prolonged cycling (a type of PVA) is common in patients with, e.g., chronic obstructive pulmonary disease (COPD), and COPD may represent a root cause of prolonged cycling. In some embodiments, root cause information may assist care providers in their understanding of a patient’s overall situation or possible situation, which, in turn, may behelpful in helping the care provider optimize overall care provided to the patient. For example, alarm names may not always be enough to fully inform the care provider in this regard, and notifications with root cause information may be much more helpful.

[0087] In some embodiments, one or more processors of a controller of one or more devices or systems, e.g., of, or in communication with, a mechanical ventilation system (which may include or be a mechanical ventilator), determine output based on obtained input, which may include use of a PVAM. The output may be used, e.g., in mitigation of a detected type of PVA. For example, the output may include notifications, which may include or provide root cause messages, recommendations, guidance, etc., for presentation to a user. The notifications may include, for example, information about the root cause, recommendations or guidance relating to measures that may be taken to mitigate the detected type of PVA. such as, e.g., adjustment of one more ventilatory parameters, and / or other measures. The provided information and guidance may be helpful to users including, e.g., emergency or field care providers, some of whom may have limited training, limited time, and / or many distractions. The notifications, e.g., may alert a user of a detected or possible PVA and may inform or guide the user in their understanding the nature of the PVA. response(s) to mitigate it, and may thus provide solutions to technical problems associated therewith, including as relates to, e.g., determining and providing output for optimized PVA management. The recommendations or response(s) to mitigate the detected or possible PVA may include instructions to the user to make one or more adjustments to one or more ventilatory parameters of the mechanical ventilation system, such as to identity’ the ventilatory parameter and, optionally, an amount of adjustment or a value to which a ventilatory parameter should be set.

[0088] In some embodiments, the output may include determined adjustments relating to mechanical ventilation being provided to the patient, such as adjustments to one or more ventilatory parameters, for implementation to mitigate a detected ty pe of PVA. In some examples, determined adjustments may be implemented after confirmation by a user, who may, e.g., be prompted to provide such confirmation. However, in some examples, determined adjustments may be implemented without such confirmation, such as by being automatically implemented by the system, and / or by being included in or by one or more physiologic closed-loop control algorithms. In some embodiments, amounts of adjustments may be based on, related to, or proportional to an associated measure of PVA severity. The adjustments may thereby provide for more optimal management, including mitigation, of PVA, and provide solutions to technical problems and clinical challenges associatedtherewith. In some examples, an adjustment may include, e.g., an amount of adjustment, which may include, e.g., a value, quantity, magnitude, size, percentage, proportion, etc., which may, e.g., relate to one or more parameters being adjusted, to be adjusted, or potentially to be adjusted. Furthermore, in some examples, an adjustment may include, e.g., an amount range, which may, e.g., include a continuous or discrete range of amounts.

[0089] In some embodiments, the notifications, such as the PVA root cause and mitigation notifications, may include one or more recommended adjustments. In some examples, a user may be provided with the ability to manually implement such recommended adjustments, such as, e.g., by using controls of a display or GUI of the ventilator, portable computing device or tablet, or another device. In some embodiments, a user may interact with a notification, such as to obtain more information and / or to select a specific recommended adjustment (e.g., within a recommended or permitted range) or to direct or cause implementation of a recommended adjustment.

[0090] In some embodiments, input to a PVAM may include, e.g., a detected ty pe of PVA, ventilatory parameter data (which may relate to or represent one or more ventilatory parameters), and patient data. The patient data may include, e.g., among other things, sex, age, height, predicted bodyweight or a predicted body weight related parameter, and / or respiratory7mechanics parameters or related parameters. In some embodiments, one or more processors and / or controllers, of one or more devices, may use one or more PVAM algorithms in determining output. The algorithms may be used, e.g., in comparing at least a portion of the input to one or more thresholds. The output may be, e g., determined based at least in part on the comparison(s). In some embodiments, incorporating use of such patient data, such as in algorithms for determining or providing PVA notifications or adjustments, may allow for greater optimization of PVA management, including mitigation. For example. PVA mitigation notifications and adjustments may be determined or provided that are more customized to, or more optimized based on, characteristics of an individual patient to whom mechanical ventilation is being provided, and may provide solutions to technical problems and clinical challenges associated therewith, including as relates, e.g., to providing PVA management that is optimized relative to the patient's characteristics.

[0091] In some embodiments, the input may include a measure of PVA severity, e.g., relating to the detected type of PVA. The measure of PVA severity' (which may, e.g., have an associated magnitude, such as may be expressed, e.g., as a numerical value) may be, e.g., determined based at least in part on patient airway pressure (although, in other embodiments, a measure of PVA severity’ may be otherwise based). Use of patient airway pressure in thisregard may, e.g., allow leveraging of a ventilatory parameter that is normally measured during mechanical ventilation, rather than, e.g., requiring additional sensing arrangements and measurement, and thus may provide solutions to technical problems associated therewith, including in PVA mitigation that is optimized including use of a measure of PVA severity, and / or including use of a measure of PVA severity' that is based at least in part on measured patient airway pressure.

[0092] In some embodiments, measured patient airway pressure may be used in estimating one or more breathing related parameters of the patient, such as, e.g., work of breathing (WOB), other WOB related parameters such as pressure-time product (PTP) or airway occlusion pressure (AOP), or other parameters. The estimated breathing related parameter(s) may then, e.g., be used in determining the measure of PVA severity in various ways, examples of which are described herein. In some embodiments, the measure of PVA severity may be based at least in part on, associated with, or correspond with, the estimated breathing related parameter(s). For example, in various embodiments, the measure of PVA severity’ may be associated with, or correspond with, the breathing related parameter(s), such as linearly or non-linearly. may be based at least in part on one or more weighting parameters or one or more mathematical functions, and / or may be determined using one or more algorithmic, mathematical, machine learning, artificial intelligence, or neural network related models or methods, yvhich may utilize various types of input, and / or which may utilize input data, which may include training data.

[0093] In some embodiments, for a period of time including multiple patient breaths, an estimated (or otherwise determined) WOB for each breath (or, e.g., some breaths) may be used in determining a measure of PVA severity for each breath (or, e.g., some breaths) occurring over a period of time including multiple patient breaths. For example, the varying (breath by breath) WOB over the period of time may be used in determining a varying (breath by breath) PVA severity score over the period of time, where the PVA severity’ score (which may, e.g., have a value from 0 to 1) may be or represent, or may otherwise be associated with, a measure of PVA severity. The varying PVA severity score over the period of time may be used in determining a measure of PVA severity for the period of time as a whole. In some embodiments, a measure of PVA severity and / or a PVA severity score may be determined for a single breath, or for each of multiple breaths over a period of time, or may be determined as an overall for a period of time including multiple breaths.

[0094] In some embodiments, patient airway pressure and / or flow waveforms may be obtained or determined, and used in determining a measure of PVA severity, such as for adetected type of PVA. The patient airway pressure and / or flow waveforms may be based on or determined using measured patient airway pressure and / or measured patient airway flow. The measured patient airway pressure and / or flow, and / or the patient airway pressure and / or flow waveforms, may be obtained by a controller coupled with a mechanical ventilation system (e.g., whether or not the controller is part of the mechanical ventilation system), e.g., by being determined by the controller or input to the controller, such as from a device outside of the mechanical ventilation system.

[0095] In some embodiments, a measure of PVA severity may be determined for a period of time including one or more patient breaths. A patient breath (which may also be called a breath) may include, e.g., a mandatory' breath (e.g., one delivered entirely by the ventilator) or a spontaneous breath. Furthermore, for each breath (patient breath, delivered by the mechanical ventilation system) during the period of time (or, e.g., for some of the breaths), a magnitude of PVA severity for the individual breath may be determined. The measure of PVA severity for the period of time may be determined using the magnitude of PVA severity’ as determined for each of the individual breaths.

[0096] In some embodiments, the magnitude of PVA severity for each breath may be determined using airway pressure and / or flow waveforms. For example, a sample (e.g. standard, template or idealized) airway pressure or flow waveform (or both) may be obtained (e.g., input or determined) for a synchronous breath (e.g., a breath not including any type of PVA, including the detected type of PVA), which sample, in various embodiments, may be from the patient for whom the magnitude of PVA severity is being determined, or from another patient, or not relating to a specific patient. Additionally, a patient airway pressure or flow waveform (or both) may be obtained (e.g., input or determined) for a single asynchronous breath (e.g., a patient breath including the detected type of PVA). A comparison of the sample synchronous breath waveform with the asynchronous patient breath waveform may be performed and used in determining a magnitude of PVA severity for the single patient breath.

[0097] For example, based on the comparison of the two waveforms, a deviation may be identified and determined between the two waveforms. The magnitude of PVA severity for the patient breath may be determined based on a determined magnitude of the deviation. For example, the magnitude of the deviation may be based on a difference in a quantified graphical area between the two waveforms (it is noted that, in some embodiments, the deviation or quantified graphical area be algorithmically calculated by the controller without forming any computational or image-based graphical construct or plot). For example, sometypes of PVA may result in what may, if visually depicted, appear to be a dip or raised area on the asynchronous patient airway pressure or flow waveform, relative to the synchronous breath airw ay pressure or flow waveform. A calculated area of this dip or raised area (if visually depicted) may be used in determining the magnitude of PVA severity for the patient breath.

[0098] In some embodiments, the varying magnitude of PVA severity (which may vary on a breath by breath basis) may be used in determining a var ing PVA severity score or PVA severity’ index over the period of time. The varying PVA severity score or index over the period of time may be used in determining an overall measure of PVA severity’ for the period of time as a whole.

[0099] In some embodiments, for a period of time, one or more PVA cluster periods occurring during the period of time may be identified and used in determining a measure of PVA severity' for the period of time as a whole. Cluster periods may be associated with poor mechanical ventilation outcomes, and, in some embodiments, may be factored into a measure of PVA severity for the period of time including the one or more cluster periods.

[0100] In some embodiments, categories, such as PVA severity categories, may be defined and used. For example, a measure of PVA severity (e.g., a PVA severity score) for a single patient breath, or for a period of time including multiple patient breaths, may be assigned or categorized into one of a set of categories (e.g., mild, moderate or severe PVA severity). For example, the assigned category may be associated with the value of the measure of PVA severity' or PVA severity score, where each category is associated with a specified range of PVA severity values (e.g., mild PVA severity may be associated with a lower range of PVA severity values than moderate PVA severity, and moderate PVA severity may be associated with a lower range of PVA severity values than severe PVA severity).

[0101] In some embodiments, a measure of PVA severity, or an associated PVA severity category, may be used in determining an amount of an adjustment to a ventilatory parameter for mitigation of the detected type of asynchrony (e.g., in a mitigation measure recommended to a care provider, or as an automatic adjustment to be implemented by a mechanical ventilation system). For example, for a detected type of PVA, a set of ranges of PVA severity' scores may each be assigned a specified, single adjustment amount to a ventilatory parameter (e.g., a PVA severity score within in a lower range may be assigned a specified adjustment amount, while a PVA severity score within a higher range may be assigned a higher specified adjustment amount or different specified adjustment amount for one or more ventilatory parameters). This may be particularly advantageous considering thatadjustments to ventilatory parameters may only be possible in discrete amounts. As another example, one of a set of adjustment amounts to a ventilatory parameter (e.g., trigger sensitivity) may be determined or selected to correspond with the PVA severity category of the measure of PVA severity for a period of time (e.g., a certain adjustment amount for mild PVA severity, a larger adjustment amount for moderate PVA severity', and a still larger adjustment amount for severe PVA severity).

[0102] In some embodiments, during mechanical ventilation of a patient, following implementation of a first measure taken to mitigate a detected type of PVA, a breath by breath measure of PVA severity is determined and monitored over time. If and when certain specified conditions are met. a recommendation may be triggered to implement a second PVA mitigation measure. The specified conditions may relate to a tracked measure of PVA severity for a period of time following implementation of the first measure, and may indicate that the first measure has been ineffective or insufficiently effective in mitigating the detected ty pe of PVA, thus warranting implementation of a second PVA mitigation measure. In some embodiments, a change in PVA severity index, such as from a first severity index for a first period to a second severity index for a subsequent, second period, during the provision of mechanical ventilation to the patient may provide for a corresponding change in the notifications or determined adjustments.

[0103] Input data, used in determining the measure of PVA severity, may include, for example, the detected type of PVA, data relating to the patient (which may include, for example, patient age, sex, predicted body weight, respiratory mechanics data, ventilatory data or other data), as well as other data, such as, e.g., historical data relating to mechanical ventilation of many patients and patient data, such as may include airway pressure and flow waveforms, e.g., for patients experiencing or not experiencing particular types of PVA, and may also include historical measure of PVA severity data or time-based waveforms, for example. Furthermore, in some embodiments, the measure of PVA severity may be determined differently for different types of PVA.

[0104] In some embodiments, use of the measure of PVA severity, and / or use of. e.g., a numerical PVA severity score, may allow optimization of determination or providing of PVA mitigation notifications or adjustments, such as may relate to one or more ventilatory parameters, and may provide solutions to technical problems associated therewith. For example, a higher measure of PVA severity may lead to a higher (or first) adjustment amount and / or frequency, and a lower measure of PVA severity may optimally lead to a lower (or different, second) adjustment amount and / or frequency, e.g., within a safe or optimal range.Such first adjustment and second adjustment amounts / frequency may comprise a set of adjustments applied to a corresponding set of ventilatory parameters. In some embodiments, adjustments, such as adjustment amounts, to particular ventilatory parameters may be optimally determined using one or more algorithmic, mathematical, machine learning, artificial intelligence, or neural network related models or methods, which may utilize various types of input, which may include use of input data, which may include training data. Input data may include, for example, the detected type of PVA. the measure of PVA severity, data relating to the patient (which may include, for example, patient age, sex, predicted body weight, respiratory mechanics data, ventilatory data or other data), as well as historical data relating to mechanical ventilation of many patients and patient data, such as may include airway pressure and flow waveforms, including for patients experiencing particular types of PVA, and may include historical measure of PVA data or time-based waveforms, for example.

[0105] In some embodiments, the measure of PVA severity may be monitored and tracked over time during the mechanical ventilation being provided to the patient, and associated PVA severity related data may be determined or obtained. PVA severity related data may include, e.g., measure of PVA severity data and data associated with, e.g., determined based at least in part on, measure of PVA severity data (where measure of PVA severity data can include, e.g., data representing a measure of PVA severity). In some embodiments, PVA severity related data may be used in the determination or providing of PVA mitigation notifications or adjustments. In some embodiments, determined adjustments may be used in, or as part of, physiologic closed-loop control (PCLC) of one or more ventilatory parameters.

[0106] Furthermore, in some embodiments, PVA severity related data may be used in minimization of the measure of PVA severity, such as over time, which may include, e.g., minimization within specified parameters or conditions, such as, e.g., permitted ventilatory parameter values ranges. In some embodiments, PCLC or PVA severity minimization (which may include minimization of a measure of PVA severity’) may include use of one or more algorithmic, mathematical, machine learning, artificial intelligence, or neural network related models or methods. Some embodiments include determination and analysis of PVA severity patterns and trending over time (e.g., past and present), and PVA severity forecasting over a future period of time, such as based at least in part on a tracked measure of PVA severity . In some embodiments, for example, forecasting may include identifying trends or patterns in the measure of PVA severity as monitored over time, such as using one or models, and, e.g.,using the foregoing in predicting future changes in the measure of PVA severity over time. Forecasting related PVA severity related data may be used, for example, in determining or providing notifications or adjustments. For example, if PVA severity is forecasted to increase, or more rapidly increase, then larger, more rapid or more frequent adjustments may be optimally determined and / or presented, and vice versa. Furthermore, in some embodiments, forecasting related PVA severity related data may be used in minimization of PVA severity, such as over time during the providing of mechanical ventilation.

[0107] Some embodiments incorporate use of PCLC, such as in continual (e.g., constant, ongoing, repeating, or periodic) adjustment of one or more specific ventilatory parameters. For example, PCLC may be used in optimizing one or more ventilatory' parameters, or in maintaining one or more ventilatory parameters at one or more specified values, or within one or more specified ranges, or in continually adjusting one or more ventilatory parameters towards one or more specified target values or ranges. In various examples, one or more PCLC algorithms may be used in continually adjusting or optimizing one or more specified ventilatory’ parameters (e.g., PEEP, fraction of inspired oxygen (FIO2), or any of various other ventilatory parameters), potentially algorithmically taking into account various conditions or factors, and / or weighting thereof, such as may relate, for example, to one or more other ventilatory' parameters (e.g., plateau pressure) or other parameters (e.g., oxygen saturation (SpO2)).

[0108] In some embodiments, PCLC algorithms are provided and used that incorporate or factor in a monitored measure of PVA severity, or other PVA severity related data, such as may include, e.g., historical, tracked, trending, or future forecasted measure of PVA severity' data, such as may be associated with one or more periods of time. For example, in some embodiments, one or more PCLC algorithms may continually operate to reduce or minimize a measure of PVA severity or forecasted measure of PVA severity, or such reduction or minimization may be taken into account and / or weighted as part of the operation of the one or more PCLC algorithms, which may also operate in closed loop control of one or more other ventilatory parameters. For example, in various embodiments, one or more PCLC algorithms may be used in reducing or minimizing a measure of PVA severity, and / or a forecasted measure of PVA severity, or may be used in doing so while also maintaining one or more ventilatory parameters at one or more specified values or ranges, or towards one or more specified target values or ranges. Furthermore, in various embodiments, PCLC algorithms may prioritize or weight different objectives differently, based on specified conditions or parameters, and / or optimally (e.g., to optimize overall patient treatment oroutcome), where such objectives may include, e.g., maintaining one or more specified ventilatory parameters at, or targeted toward, one or more values or ranges, while also minimizing a measure of PVA severity. In some embodiments, a PVAM may be used in one or more aspects of PVA management that may be integrated with or into PCLC, and / or in providing PCLC that includes one or more aspects of PVA management, such as may include minimization of a measure of PVA severity, for example.

[0109] Additionally, in some embodiments. PCLC may include use of one or more algorithmic, mathematical, machine learning, artificial intelligence, or neural network related models or methods, which may utilize various types of input, which may include training data. Such data may include, for example, the detected type of PVA, the measure of PVA severity, data relating to the patient (which may include, for example, patient age, sex, predicted body weight, respiratory mechanics data or other data), as well as historical data relating to mechanical ventilation of many patients and patient data, and may include historical measure of PVA data or time-based waveforms, for example.

[0110] FIG. 1 illustrates an example emergency care environment 100 including a software-based PVA manager (PVAM) 130 incorporated into a portable ventilator 106 (however, in various embodiments, a portable or non-portable ventilator may be included). More generally, the PVA manager may be provided by a controller, which may be part of a ventilator. The PVAM 130 may obtain input (where obtaining can include determining by the PVAM 130 and / or obtaining by the PVAM 130 from outside of the PVAM 130) and determine output relating to PVA mitigation. The input may include, e.g., a detected type of PVA, patient data, ventilatory parameter data, PVA severity related data, and various other data. The output may include, e.g., PVA mitigation notification(s) 122, such as messages, and / or determined PVA mitigation adjustments. In FIGs. 1-4. various of the depicted devices and systems may be in communication with various of the other devices, such as via the Internet and / or one or more wireless or wired networks. In some embodiments, authentication or authorization may be required for connection of, or communication from or to, particular devices or systems.

[0111] In some embodiments, the PVAM 130 may include one or more algorithms for implementing various embodiments described herein, which may include one or more algorithmic, mathematical, machine learning, artificial intelligence, or neural network related models or methods, which may utilize various types of input, which may include training data. Such input may include, for example, the detected type of PVA. the measure of PVA severity, PVA severity related data, and data relating to the patient (which may include, forexample, patient age, sex, predicted body weight, respiratory mechanics data or other data), as well as historical data relating to mechanical ventilation of many patients.

[0112] The PVAM 130 may also be used, among other things, for presenting or implementing output, such as presenting PVA root cause and mitigation notification(s) or implementing determined PVA mitigation adjustments. In various embodiments, the PVAM 130 may determine at least a portion of the obtained input, such as the measure of PVA severity, and / or may obtain at least a portion of the obtained input from, sources such as the ventilator 106, or other devices or systems. In some embodiments, the PVAM 130 may be used, e.g., in managing and allowing user interaction relating to presented or determined PVA root cause and mitigation notifications and adjustments. For example, the PVAM 130 may be used in, e.g., presenting, or obtaining from the user, input, interaction confirmations, authorizations, acknowledgements, prompts, selections, modifications, instructions, commands, etc. For example, the PVAM 130 may be used in allowing the user to obtain more information about, or cause action to be taken regarding, a presented notification, which may include a recommended adjustment. Furthermore, the PVAM 130 may be used in, e.g., interacting with the user regarding a determined adjustment, such as notifying the user about an implemented or expected adjustment, or obtaining user confirmation required before implementing adjustment. Furthermore, in some embodiments, the PVAM 130 may be used in prompting or allowing the user to input or select (e.g., from a menu or range) a specific action or adjustment (such as a specific adjustment amount).

[0113] In some embodiments, the system 101 may include other components, such as portable oxygen concentrator (POC) system(s), pressurized oxygen source(s), such as high- pressure oxygen (HPO2) source (s), e.g., a HPO2 source, such as a HPO2 tank. In various embodiments, the system 101, and / or components thereof, may be portable, such as hand portable, or non-portable. As depicted, the environment 100 also includes a user 148 of the portable ventilator 106, and may or may not include one or more additional local or remote users.

[0114] In various embodiments, various devices and systems may be communicatively coupled by wired and / or wireless connection to each other, the portable ventilator 106, and many different combinations of devices and systems, and roles for each, may be included or used. For example, in various embodiments, defibrillator(s) / CCM(s), such as may include an automated external defibrillator (AED), tablet computing device(s), other device(s), other medical device(s) and offsite computing platform(s) may be included. The PVA manager may be provided by a controller of any of these devices or platforms.

[0115] In various embodiments, particular sensing capabilities and components may be included and distributed in various ways between various devices or systems. These may include, for example, one or more electrodes, capnographic sensors, pulse oximeters, flow sensors, pneumotachometers, spirometers, pressure sensors, barometric sensors, humidity sensors, oxygen sensors, temperature sensors, electrical or magnetic sensors, light / electromagnetic spectrum / optical sensors, blood pressure monitors, heart rate monitors, electrocardiogram (ECG) sensors, and others. Sensing capabilities and components may relate, for example, to patient parameters and various patient systems, such as respiratory and circulatory systems. Sensing capabilities and components may also relate to parameters associated with devices and their operation, including, for example, the portable ventilator 106 or other ventilation system, defibrillator / CCM, electrotherapy system, or pacing system. In some embodiments, any of various sensing capabilities, such as those of the portable ventilator 106, defibrillator / CCM or tablet, may instead be provided by one or more other devices or systems, or may be distributed between multiple devices or systems. Also, in various embodiments, various sensing components, or all or part of the patient circuit 108, may or may not be considered to be part of a portable ventilator, or other device, even if they are connected thereto. Furthermore, in various embodiments, roles and functions of the various devices or systems may be distributed differently between the devices or systems.

[0116] The portable ventilator 106 (and / or one or more devices or systems) may include various sensing, measuring, computerized, electrical, mechanical, coupling and output components. As depicted, the portable ventilator 106 includes an oximetry sensor 1 14, such as a pulse oximeter or other sensor for providing a direct or indirect measurement, estimation or indication of oxygen saturation (SpO2) or other blood oxygen content or concentration related parameter, a capnographic sensor 116 or capnograph, which may measure ETCO2, and a blood pressure sensor / monitor 118, and may also include one or more flow sensors, pneumotachometers, or pressure sensors, among other things.

[0117] As depicted, the portable ventilator 106 includes a display and / or user interface 120 that may provide data relating to various patient physiological, respiratory and ventilation related parameters, and may include other output or presentation components, such as a speaker. In some embodiments, the display or user interface 120, or other output devices, may provide a display that is integrated to include data relating to operation of other coupled devices that may also be in use with the patient, such as, for example, a defibrillator / CCM. The display or user interface 120 may also allow user interaction, including to obtain or display data, change settings, such as, in some embodiments, a FIO2setting, accept suggested or recommended settings changes, or view or respond to alarms or alerts, among other things. The display or user interface 120 (or a display or user interface of one or more other devices or systems) may be used, for example, in presenting PVA root cause and mitigation notifications (which may include messages or alarms) and information regarding PVA mitigation adjustments, and in allowing or providing prompts for user interaction therewith, such as to provide acknowledgements or responses, obtain more information, modify alarms or alarms statuses, choose particular courses of action, implement adjustments, input or select adjustment amounts, or confirm adjustments to be implemented.

[0118] In some embodiments, the portable ventilator 106 is capable of providing physiologic closed-loop control (PCLC) 124 of one or more ventilation or patient related parameters, such as FIO2 (FIO2 PCLC) or Positive End-Expiratory Pressure or Baseline Airway Pressure PCLC (PEEP PCLC or BAP PCLC), and / or, e.g., to minimize a measure of PVA severity. In some embodiments, PCLC may include use of one or more algorithmic, mathematical, machine learning, artificial intelligence, or neural network related models or methods, which may utilize various types of input, which may use input data include training data. Input data may include, for example, the detected type of PVA. the measure of PVA severity, PVA severity related data, data relating to the patient (which may include, for example, patient age, sex, predicted body weight, respiratory mechanics data or other data), and may include historical measure of PVA data or time-based waveforms, for example.

[0119] In some embodiments, however, FIO2 PCLC, PEEP PCLC. or other PCLC may not be included, or its use may be user-selectable or otherwise selectable. In some embodiments, a user may select or input a FIO2 setting, and / or adjust the flow rate of oxygen or supplemental oxygen, based at least in part on current patient SpO2, which may be monitored by one or more devices. Furthermore, in some embodiments, a user may turn on or off PCLC, such as FIO2 PCLC, select whether PCLC is used, or select whether PCLC is used at a particular time or during a particular time period.

[0120] The setting of FIG. 1 may be a non-hospital setting, but, in various embodiments, systems are provided for use in hospital or non-hospital settings. In some embodiments, some or all of the components of the system 101, including the portable ventilator 106, may be transported to the setting to provide emergency care to a patient 104.

[0121] When delivering mechanical ventilation, the portable ventilator 106 may provide breathing gas to the patient 104 via a mechanical ventilation apparatus that includes a gas flow generator, such as a blower or a compressor, and a gas delivery apparatus including a patient circuit 108 that includes a facemask 110. However, in some embodiments,ventilation may be provided via intubation or other patient interface such as nasal cannula rather than via a facemask 110.

[0122] In some embodiments, FIO2 PCLC may be used in the control of the output of oxygen or oxygen enriched gas, e.g., from POC system(s) and / or pressured oxygen source(s), during the providing of mechanical ventilation. In some embodiments, control, including of FIO2 PCLC and / or PEEP PCLC, and / or other ventilatory parameters, may be provided internally by the portable ventilator 106 or by one or more other devices, such as one or more remote control devices for the portable ventilator 106, such as tablet(s) or defibrillator(s) / CCM(s).

[0123] In some embodiments, the portable ventilator 106 includes sensors such as one or more flow sensors, pneumotachometers or pressure sensors, which may include sensors disposed within the patient circuit 108 and / or within the portable ventilator 106, for sensing signals representative of gas flow or pressure within the gas delivery apparatus of the portable ventilator 106 and / or to the patient 104. In some embodiments, one or more of the sensors may be coupled with or part of one or more spirometers, for example. In some embodiments, a controller of the portable ventilator 106 receives signals representative of the gas flow or pressure. The controller may control aspects of mechanical ventilation provided by the portable ventilator 106 based at least in part on the signals representative of gas flow or pressure. Furthermore, in some embodiments, based at least in part on the signals representative of the gas flow or pressure, the controller may generate respiratory parameter data corresponding with at least one respiratory parameter of the patient, such as, for example, respiratory system compliance (Crs), respiratory' system resistance (Rrs), vital capacity' (VC), forced vital capacity (FVC), forced expiratory volume (FEV) at a timed interval such as FEV1. forced expiratory’ flow (FEF), or FEF25-75, peak expiratory’ flow rate (PEF or PEFR), maximal voluntary ventilation (MVV) or others. In some embodiments, a controller of the portable ventilator may' cause transmission of generated respiratory parameter data to be received (directly or via one or more intermediary entities) by other local or non-local devices or systems in the environment 100. e g., defibrillator(s) / CCM(s), the tablet(s), or elsewhere, such as for use in determining a respiratory status of the patient 104. However, in some embodiments, the portable ventilator 106 may determine, or participate in determining, a respiratory' status of the patient.

[0124] FIGs. 2-4 provide other examples of systems 200, 300, 400 including a PVAM, which may include devices or other systems in addition to a ventilator, and in which the PVAM be may included on or with any of various different devices or other systems, orcomponents of the PVAM may be distributed between multiple devices or systems. In some embodiments, the systems 200, 300. 400 may include other device(s) or other system(s) beyond those shown.

[0125] The system 200 of FIG. 2, in addition to including a ventilator / portable ventilator 206, includes a tablet 206 including a tablet display or graphical user interface (GUI) 208. The system 200 may also include an offsite computing platform 236 (e g., hospital, medical facility, or other platform) including a display or GUI 232, and an offsite user 234 is also depicted. Output of a PVAM 220 may include PVA mitigation notification(s), such as notification(s) 222, and / or PVA mitigation adjustments. The PVAM 220, or components or algorithms thereof, may be incorporated into the portable ventilator 202, the tablet 206, one or more devices of the remote computing platform 236, or a combination thereof, for example. A local user 214 and an offsite user 234 are shown. In some embodiments, one or more offsite users at the offsite computing platform may communicate with local users, such as to provide guidance or instructions relating to care, or, in some embodiments, may even remotely control aspects of operation of local medical devices.

[0126] A single user 214 may operate the ventilator 202 and the tablet 206, or the tablet 206 may be operated by a different tablet user, who may or may not also operate one or more other devices besides the tablet 206. In some embodiments, the tablet 206 (or several tablets, or other computing devices or portable computing devices) may provide tablet users with a convenient and practical device for obtaining, displaying, and inputting data, including with regard to PVA root cause and mitigation (e.g., PVA mitigation notifications and / or information about PVA adjustments, as described herein). For example, the tablet 206 may provide a large, sophisticated and highly organized GUI 208, allowing convenient input and display of a large amount of data, including PVA mitigation related data, and convenient user interaction therewith. Additionally, the GUI 208 of the tablet 206 may be organized to comprehensively obtain and display data relating to various aspects of care of the patient, and to various medical devices that may be included in the system 200. Furthermore, tablets may allow convenient sharing of data or communication between users. Still further, in some embodiments, tablets (or other computing devices or portable computing devices) may be used in remote control of aspects of operation of devices in the system 200. As depicted, the ventilator 202 may also include a display or GUI 204, and, in some embodiments, the display or GUI 208 of the tablet 206 and the display or GUI 204 of the ventilator 202 may operate in an integrated or coordinated fashion.

[0127] The system 300 of FIG. 3 includes a ventilator / portable ventilator 302, a tablet 306 and potentially a remote computing platform 336, and also includes a defibrillator / CCM 308 including a display or GUI 304 (which may include various controls). Output of PVAM 320 may include PVA mitigation notification(s), such as notification(s) 322, and / or PVA mitigation adjustments. As depicted, the PVAM 320, or components or algorithms thereof, may be incorporated into any, some, or all of the depicted devices or systems. In some embodiments, a defibrillator / CCM 308 may operate in an integrated way with devices including, e.g., the portable ventilator 302 and the tablet 306. For example, the defibrillator / CCM 308 may include a large and sophisticated display or GUI 304, which may integrate and display information relating to various aspects of patient treatment, which may include, e.g., ventilation, defibrillation and chest compressions (as well as potentially other treatment information, such as administration of drugs). Furthermore, in some embodiments, the defibrillator / CCM 308 may include or be coupled with various sensing and measurement components, e.g., capnography and pulse oximetry (as show n) and sensed data may be shared with other coupled devices, such as the ventilator 302.

[0128] While FIGs. 1-3 illustrate systems 101, 200. 300, of various devices and components, including PVAMs 130, 220, 320 implementing PVA mitigation notification related operations, in various embodiments, the depicted PVAMs 130, 220, 320 may also or alternatively be used in such systems 101, 200, 300 for implementing PVA adjustment related operations. Furthermore, in the depicted systems 101, 200, 300. each of the PVAMs 130, 220, 320, or various algorithms, components or aspects thereof, may be incorporated into any one of the depicted devices or systems, or may be distributed between two or more of the depicted devices or systems (and / or other devices or systems), which devices or systems may, for example, be coupled and configured so that the PVAM may operate in an integrated and unified fashion.

[0129] FIG. 4 illustrates an example emergency care system 400 including a PVAM 420, which may be incorporated into a portable ventilator 402, used in implementing PVA root cause and mitigation related operations, such as in obtaining input and determining output including PVA mitigation adjustment(s) 404, such as to one or more ventilatory parameters. In some embodiments, the depicted PVAM 420 may also be used in determining output including PVA notifications. In the depicted system 400, the PVAM 420 may be incorporated into the portable ventilator 402. However, in other embodiments, one or more other devices or systems may be included, and the PVAM, or components thereof, may be included in one or more of the devices or systems.

[0130] As depicted in FIG. 4, the PVAM 420 is used in obtaining input and determining output including determined adjustment(s) 404 to ventilatory’ parameter(s). In various embodiments, the determined adjustment(s) 404 may be implemented by, or communicated to and implemented by, the ventilator 402, whether by operation of the PVAM 420 or otherwise, and whether or not confirmation or other interaction is required by a user to implement the adjustment(s). In some embodiments, determining the adjustment(s) can be accomplished at least in part by. include use of, or be incorporated into use of one or more PCLC algorithms, as described herein.

[0131] In some embodiments, rather than a PVAM, or a PVAM exclusively, detecting a type of PVA, determining a magnitude of severity for the detected type of PVA, and / or determining adjustments to mitigate the detected type of PVA in connection with a patient, a care provider may instead do so or play a role in doing so. For example, in some embodiments, a care provider may determine the presence of a type of PVA. The PVAM may, for example, be used in presenting visual, audio or other data for use of the care provider in determining presence of the type of PVA. For example, the PVAM may cause display to the care provider of such information, such as on a display of the ventilator or another device, such as another medical device such as a defibrillator, or a tablet or other computing device of the care provider. The PVAM may cause display, for example, of patient airway pressure waveforms and / or patient airway flow rate waveforms, and / or other waveforms or other information, for use of the care provider in detecting the presence of the ty pe of PVA.

[0132] In some embodiments, the PVAM may cause display of one or more patient airway pressure waveforms and / or patient airway flow rate waveforms, such as for each of one or more patient breaths, using which the care provider may determine that a type of asynchrony is present. Furthermore, for example, the PVAM may cause display of additional information to facilitate the determination by the care provider, such as by causing display’ of one or more sample synchronous waveforms for visual comparison to the one or more asynchronous waveforms. In some embodiments, the PVAM may display additional information, such as by displaying an asynchronous waveform(s) superimposed over, or otherwise in association with, a synchronous sample waveform(s), and by highlighting or otherwise visually indicating the difference between the two, for use by the care provider in determining the presence of the type of asynchrony (or lack thereof). Furthermore, in some embodiments, the PVAM and the care provider may’ each play a role in the determination of the presence of the type of asynchrony. For example, the PVAM may cause display to thecare provider of a type of PVA that may or is likely to be present, or information suggesting that it may be present, for confirmation or rejection by the care provider (e.g., based on the care provider’s previous experience, the care provider’s knowledge of the patient or the patient’s history, or other experience, skill, information or background of the care provider).

[0133] Furthermore, in some embodiments, rather than a PVAM, or a PVAM exclusively, determining a measure of severity for a detected type of PVA, and / or determining a PVA mitigation adjustment to a ventilatory parameter, a care provider may do so, or play a role in doing so. For example, in some embodiments, based on visual other information presented to the care provider (e.g., as described above), the care provider may determine the measure of severity for the type of PVA determined to be present, and / or may determine or decide on one or more ventilatory parameters to be adjusted, and / or the specific adjustment and magnitude thereof (e.g., an increase or decrease of a specified amount to a ventilatory parameter), e.g., based on knowledge or experience of the care provider, as mentioned above. Furthermore, in some embodiments, the PVAM may cause display to the care provider of a tentatively determined measure of PVA severity and / or PVA mitigation adjustment, for confirmation(s) or rejection(s) by the care provider, or may present ranges for either, such as for the care provider to select a specific value from the ranges (e.g., as specific value for the measure of PVA severity and / or a specific adjustment amount for a specified ventilatory parameter).

[0134] Additionally, in some embodiments, the PVAM may cause the care provider to be presented with data (e g. displayed waveforms as mentioned above, etc.) relating to each of more than one patient breaths, such as a most recent patient breath as well as multiple previous patient breaths over a specified period, so that, for example, the care provider may determine, or play a role in determining, a t pe of PVA that is present, a magnitude of severity associated with each of the patient breaths, a measure of PVA severity for the period of time, and / or a PVA mitigation adjustment(s). For example, the PVAM may cause the care provider to be presented with information (e.g., pressure and flow waveforms) relating to each of the most recent and multiple previous patient breaths, from which the care provider may determine a magnitude of severity for some or all of the patient breaths, and may use this in determination of a measure of PVA severity for the specified period of time. Furthermore, in some embodiments, the PVAM may present integrated or combined data relating to an entire period of multiple patient breaths. The care provider may then, for example, determine a measure of PVA severity for the entire period, and may use that in determining a PVA mitigation adjustment.

[0135] In some embodiments, other forms of information or data may be provide to, presented to, or used by the care provider, such as in place of, or in addition to. information that is caused to be presented by the PVAM. For example, in some embodiments, other electronic or non-electronic forms of data may be used. In some embodiments, for example, phy sical or paper based information may be used, e.., to provide guidance to the care provider, such as one or more notecards with information (e.g., textual and / or image-based) on detecting presence of a specific type of PVA, determining a magnitude of PVA severity for one or more patient breaths, determining measure of PVA severity for one or more patient breaths or a period of time including one or more patient breaths, and / or determining an appropriate or optimal PVA mitigation adj ustment(s). For example, notecards may include descriptions of specific types of PVA and / or examples of patient airway pressure waveforms or patient airway flow rate waveforms, and / or sample synchronous waveforms, for comparison, may include textual information describing the types of PVA, guidance on determining a type of PVA that may be present, waveforms including indications of a type of PVA, guidance on determining a magnitude of severity for one or more patient breaths, guidance for determining a measure of PVA severity for one or more patient breaths or a period of time including one or more patient breaths, and / or other information. The notecards (or other electronic or non-electronic presentation types or format) may also provide, for example, appropriate or suggested specific PVA mitigation adjustments to one or more specific ventilatory parameters, and amounts thereof (e.g., increases or decreases), or ranges thereof.

[0136] FIG. 5 is a block diagram 500 illustrating an example of operation of a PVAM, e.g., of a controller 528 (which may include hardware and software aspects) coupled with a mechanical ventilation system. Block 502 represents examples of some types of data that may be used as input by a PVAM (e.g., whether determined by the PVAM or obtained from outside the PVAM). In various embodiments, the input data 502 may include one or more of: detected type of PVA data 504, ventilatory parameter data 506, patent characteristic data 508, and measure of PVA severity data 510 (e.g., numerical PVA severity score data), and may or may not include other data. In some embodiments, the input 502 may include, e.g., various types of PVA mitigation related data or training data for one or more machine learning or artificial intelligence models.

[0137] Block 512 represents an example of PVAM logic that may be included by or used by the PVAM. In some embodiments, the PVAM logic 512 includes comparison logic 514. In some embodiments, the comparison logic 514 may include logic to compare at least aportion of the input data 502 to one or more thresholds. For example, in some embodiments, whether the one or more thresholds are met may affect the PVA mitigation notification(s) or adjustment(s) output by the PVAM, examples of which are described herein. In some embodiments, the PVAM logic 512 may include, or may include use of, one or more algorithmic, mathematical, machine learning, artificial intelligence, or neural network related models or methods, which may utilize various t pes of training data.

[0138] Block 516 represents an example of output of the PVAM. which may include determined PVA mitigation notification(s) 518 and / or determined PVA mitigation adjustments 520, such as to ventilatory parameter(s). Finally, block 522 represents implementation by presenting the determined PVA mitigation notification(s) to a user 524 and / or implementing the determined PVA mitigation adjustment 526 (e.g., whether with or without required user confirmation). In various embodiments, implementation 522, or components thereof, may or may not be performed by the PVAM.

[0139] Additionally, some embodiments further include obtaining user input and / or interaction with provided PVA mitigation notification(s) 518 and / or notifications or messaging relating to determined PVA mitigation adjustment(s) 520 for implementation, and / or additional actions, which, in various embodiments, may or may not be performed by the PVAM. For example, in some embodiments, PVA mitigation notifications are provided on display(s) or GUI(s) such that a user can interact with them. This may include, for example, allowing the user to acknowledge the notifications, request and obtain additional related displayed data, interact with or modify operation of alarms (a type of notification), cause implementation of a recommended adjustment, confirm implementation of a specified determined adjustment, or confirm or specify details of adjustment(s), which may, in some embodiments, include PCLC or aspects of PCLC), e.g., input, selection or confirmation of an amount of an adjustment, or of time or frequency of implementation of one or a sequence of adjustments, etc.

[0140] In some embodiments, notifications may be provided to a user, such as on a GUI, relating to determined adjustment(s) for implementation. This could include notifying a user about past, current or future adjustments that have been, are. or will be automatically implemented, or to obtain user confirmation to allow implementation of adjustment(s). Furthermore, in some embodiments, additional user interaction may be provided for, such as to allow the user to obtain more information about an adjustment, e.g., before confirming implementation of the adjustment, or to allow the user to modify an adjustment prior to implementation, such as by increasing or decreasing an amount of the adjustment, or to allowthe user to select from a range, list or menu of adjustments and / or adjustment amounts, for example.

[0141] In some embodiments, incorporating the patient characteristic data 508 (e.g., patient sex, age, height, predicted body weight or other body weight related parameter, and / or respiratory mechanics data or other respiratory parameter data) into the PVA algorithms, including logic and comparisons, enables the determination of output 516 that is customized, individualized, personalized or optimized based on the characteristics of the patient being ventilated. This, in turn, may provide for more optimal PVA root cause and mitigation notifications and adjustments, which may provide for greater or more rapid PVA mitigation, thus helping optimize patient care and treatment outcomes.

[0142] Additionally or alternatively, in some embodiments, incorporating the measure of PVA severity data 510 into the PVA mitigation algorithms, including logic and comparisons, enables the determination of output 516 that is optimized with regard to mitigation of the detected type of PVA. This may include, for example, determining a type, amount(s) of a recommended adjustment to a specified ventilatory parameter (e.g., an adjustment recommended via PVA root cause and mitigation notification(s), or a determined adjustment(s) to be implemented to a ventilatory parameter) so as to, for example, minimize the measure of PVA severity' or most rapidly do so. This, in turn, may provide for more optimal PVA mitigation adjustments or other actions to be determined and / or implemented, and may provide for greater or more rapid PVA mitigation (e.g., PVA reduction or elimination), thus optimizing patient care and outcomes. In some embodiments, the logic 512 may be included within, or associated with use of, one or more algorithmic, mathematical, machine learning, artificial intelligence, or neural network related models or methods, which may utilize various types of input, examples of which are described above.

[0143] FIG. 6 is a block diagram 600 illustrating some example techniques for determining, calculating or estimating a measure of PVA severity' based at least in part on patient airway pressure 602, such as may include use of an airway pressure w aveform, for example. In various embodiments, the measure of PVA severity’ may be measured and tracked more or less frequently, whether regularly or irregularly, such as. for example, more than once per breath or for some breaths, or for not every breath (e.g., a specified portion or percentage of breaths, only some breaths or sometimes skipping some breaths, etc.). Techniques 604 may include, for example, determining a measure of PVA severity based on, as represented by block 606. patient work of breathing (WOB) a pressure-time product (PTP) relating to patient airway pressure 602, and / or based on a measured airw ay occlusionpressure (AOP), where PTP and AOP may be related to WOB. In various embodiments, breathing related parameters such as, e.g., WOB, PTP and / or AOP, may be used to determine the measure of PVA severity (which, in some embodiments, may be determined differently for different types of detected PVA) in various different ways, such as by corresponding linearly or non-linearly, or by use of one or more algorithmic, mathematical, machine learning, artificial intelligence, or neural network related models or methods, which may utilize various types of input, which may include training data.

[0144] In various embodiments, various ty pes of measures of PVA severity may be utilized, obtained and utilized, or determined and utilized. In some embodiments, a parameter determined using measured patient airway pressure, such as WOB, PTP and / or AOP, may be used in determining a measure of PVA severity. This may provide technical solutions to problems associated with, e.g., easily, safely or efficiently determining a measure of PVA severity during mechanical ventilation of a patient, and may also provide technical solutions to problems associated with PVA management, including in optimally determining or providing notifications or adjustments for PVA mitigation. This, in turn, may lead to substantially improved mechanical ventilation patient outcomes and reduced patient ill effects associated with PVA and / or associated extended mechanical ventilation. In various embodiments, the measure of PVA severity' may be determined and tracked over time during mechanical ventilation being provided to the patient. In various embodiments, the measure of PVA severity’ may be tracked periodically or on a continuing basis, and time-based measure of PVA severity waveforms may be generated and used.

[0145] During mechanical ventilation, patient airway pressure is routinely monitored and measured (which can include being approximately measured), e.g., by pressure sensor(s) within or coupled with the ventilator inspiratory and / or expiratory limbs and / or the patient circuit. In some embodiments, during mechanical ventilation, monitored and measured patient airway pressure, which is generally available during mechanical ventilation without additional arrangements, is leveraged by being used in determining a measure of PVA severity, such as on a continuing or breath by breath basis.

[0146] In some embodiments, the measure of PVA root cause and seventy (or. more generally, PVA severity) may be determined, such as by a PVAM, based at least in part on estimated patient WOB, where the WOB is estimated based at least in part on measured patient airway pressure 602. Conceptually, WOB may, e.g., represent or relate to the energy (or energy per unit time) expended / required to inhale and exhale, and may reflect, e.g.. the energy or work needed to overcome the elastic load imposed by the lungs and chest wall, aswell as the resistive load of the patient airway. It is typically expressed as work per unit volume (e.g.. Joules / L) or as a work rate (Joules / min), which is sometimes / altematively called power of breathing. Herein WOB may include, e.g., power of breathing, energy per volume expressions and energy per time expressions. During PVA, the patient may work harder and expend more energy to inhale and / or exhale, e.g., due to the mismatch between the patient's demand and the output of the ventilator. Therefore, during a detected type of PVA. greater WOB may be associated with greater severity of a detected type of PVA.

[0147] As such, in some embodiments, WOB may be used in determining a measure of PVA severity. WOB is sometimes measured using esophageal pressure measurement, which takes into account pressure generated by the patient’s inspiratory muscles, such as by use of an invasively inserted esophageal balloon. However, catheterization and insertion of an esophageal balloon can be difficult and problematic, and under-inflation or over-inflation of the esophageal balloon can occur and cause risk of injury or erroneous measurement. As such, in some embodiments, measured patient airway pressure may be used in estimating patient WOB. This may include, e g., use of one or more algorithmic, mathematical, machine learning, artificial intelligence, or neural network related models or methods, which may utilize various types of input, and / or which may' utilize input data, which may include training data. For example, in some embodiments, an artificial neural network may be used in estimating patient WOB based at least in part on measure patient airway pressure.

[0148] As described with reference to FIG. 7, use of patient airway pressure, along with use of an artificial neural network, to estimate WOB shows good agreement with WOB measurement using esophageal pressure measurement. In various embodiments, in addition to patient airw ay pressure, many additional inputs to the artificial neural network (or other model, algorithm, etc.) may also be used, such as. e.g., tidal volume, respiratory system compliance, inspiratory flow7rate, respiratory system resistance, spontaneous minute volume, auto-PEEP, trigger pressure depth, and inspiratory rise time, among others, or a combination thereof. Furthermore, in some embodiments, one or more of the inputs, or a combination thereof, could be used, e g., as a surrogate for WOB, which could serve as an estimate of WOB. Additionally, in some embodiments, one or more regression or logistical regression models or algorithms may be used in estimating WOB, or a surrogate for WOB, such as mayuse a combination of various weighted factors, and / or models or algorithms may be used in determining optimal inputs or weighting, for example, for use in estimating WOB or a surrogate for WOB. Unlike esophageal pressure, such as may be measured via inserted esophageal balloon, patient airway pressure is generally easily available, e.g., such as bybeing continuously measured and recorded during mechanical ventilation without any additional procedures, devices or sensing being required beyond those already being used in connection with the mechanical ventilation. As such, in some embodiments, measured patient airway pressure is used in estimating patient WOB, and the estimated patient WOB is used in determining the measure of PVA severity. It is noted, however, that, in other embodiments, esophageal pressure measurement, such as by use of an inserted esophageal balloon, may be used in, e.g., estimating WOB and / or in determining a measure of PVA severity.

[0149] In FIG. 6, as represented by block 608, in some embodiments, the measure of PVA severity may be determined, such as by a PVAM, based at least in part on a pressuretime product (PTP) using measured patient airway pressure. PTP may be defined, for example, as the product of (1) average inspiratory pressure, such as from onset of patient effort, and (2) the duration of inspiration, or may be calculated in other ways, and may provide a measure or indication of or associated with WOB, and may include, e.g., integration relating to pressure developed by respiratory muscles over time). The PTP is associated with patient effort needed until ventilation assistance is effective. Therefore, a lower PTP is associated with lower effort needed from the patient, and vice versa. Generally, the more severe the PVA, the more effort is required from the patient. As such, in some embodiments, PTP, which can be determined based on monitored and measured patient airway pressure over time, may be used in determining the measure of PVA severity.

[0150] As represented by block 610, in some embodiments, the measure of PVA severity may be determined, such as by a PVAM, based at least in part on airway occlusion pressure (AOP). For example, in some embodiments, the patient airway is occluded, and negative airway pressure is measured, for the first, e.g., 100 milliseconds (or more or less) of a patient inspiration. Generally, AOP shows good correlation with WOB. As such, in some embodiments, AOP may be used in determining the measure of PVA severity, such as, e.g., in some ways similarly to the methods described with regard to WOB and PTP. Use of AOP may provide advantages including that it is not dependent on gas flow and is therefore not affected by mechanical properties of the lungs and chest wall. Additionally, AOP is easily measured in both intubated and non-intubated patients. However. AOP may require that an occlusion maneuver be performed.

[0151] FIG. 7 illustrates an example of use of measured patient airway pressure in determining a measure of PVA severity for use in determining PVA mitigation notification(s) and / or adjustment(s). At step 676, patient airway pressure is measured, such as may include being monitored, measured and tracked over time during the providing of mechanicalventilation to the patient.

[0152] At step 678, the measured patient airway pressure is used in determining or estimating patient WOB (although, in other embodiments, as described above, other breathing related parameters may be used or also used, such as PTP or AOP, for example, and, in some embodiments, esophageal pressure may be measured, such as by use of an esophageal balloon, and used). This may include tracking estimated WOB over time during the providing of mechanical ventilation, and, as described above, may include use of one or more algorithmic, mathematical, machine learning, artificial intelligence, or neural network related models or methods.

[0153] At step 680, the estimated WOB is used in calculating a measure of PVA severity, which may include tracking the calculated measure of PVA severity over time during the providing of mechanical ventilation.

[0154] At step 682, the determined (e.g., determined and tracked over time) measure of PVA severity7is used in determining output, such as PVA mitigation message(s) and / or adjustment(s) to ventilatory parameter(s).

[0155] Plots 690 (published in Crit Care Med. 2006 Apr;34(4): 1052-9) are provided to show the good agreement between WOB (which may include power or breathing), in Joules / min, as measured (including being tracked over time) using an esophageal balloon (solid curves, e.g., curve 660) and calculated WOB as estimated using an artificial neural network with measured patient airway pressure as at least one of its inputs (dotted curves, e.g., curve 658). In particular, upper plots 662 correspond with a first patient, at various WOB ranges, and lower plots 670 correspond with a second patient, at various WOB ranges, where both patients were on pressure support mechanical ventilation. As can be seen, in each of the plots 662, 670, the solid curve shows good agreement with the dotted curve, showing good agreement between the two ways that WOB was measured, over WOB at various ranges.

[0156] Measures of PVA severity7that utilize, e.g., measured patient airway pressure (or. in other embodiments, measured esophageal or pleural pressure), and which may be determined on. e.g., a breath by breath basis, have been described. However, in various embodiments, various other measures of PVA severity may' be utilized, or determined and utilized separately or in combination. These may include measures of PVA severity' based at least in part on various ty pes of parameters, including, e.g., parameters that may relate to current patient conditions, to previous patient conditions, to patient conditions over previous periods (or a previous number of breaths), to statistical data concerning many patients, toPVA or ty pe of PVA frequency, or to conditions that may be otherwise associated, or statistically associated, with PVA or type of PVA (e.g., conditions such as longer duration of mechanical ventilation, poor treatment outcomes, etc.). Additionally, in some embodiments, a measure of PVA severity' may be based at least in part on parameters that are used in detecting PVA or ty pe of PVA, while, in other embodiments, a measure of PVA severity may be based at least in part on parameters that are not used in detecting PVA or type of PVA.

[0157] For example, in some embodiments, a measure of PVA severity may be based at least in part on one or more parameters relating to PVA frequency, or PVA frequency for a patient in a recent past period, such as an asynchrony index. For example, an asynchrony index may be used, which may be based on or represent, e.g., a frequency of PVA overall, or a frequency’ of a specific type of asynchrony. For example, in some embodiments, an asynchrony index is used as a measure of PVA severity, or is used in determining a measure of PVA severity (e.g., one or more algorithms may be used in determining a measure of PVA severity' based at least in part on the asynchrony index). The asynchrony index may be calculated as a percentage based on, for a recent past period a ratio of (a) the number of asynchrony events (including each asynchronous breath) to (b) the sum of the total number of ventilator cycles plus wasted efforts, multiplied by 100%, as given by the following equation:(Equation 1) Asynchrony Index = # of asynchrony events / (ventilator cycles + wasted efforts) * 100%

[0158] Generally, a higher asynchrony index (e.g., greater than 10%) may be associated with, e g., poor ventilation outcomes, greater duration of mechanical ventilation, greater mortality' rates, greater tracheotomy rates, reduced sleep quality7, reduced patient comfort, longer intensive care unit stays, and longer hospital stays.

[0159] In other embodiments, a measure of PVA severity may be determined in other w ays. For example, in some embodiments, with regard to each of various specific types of PVA, for a patient being mechanically ventilated, one or more parameters or thresholds may' be used (or may be used in particular algorithms or sets of criteria), such as may relate to a previous period or running period (e.g., the last 20 breaths, or more or less). In some examples, if a current value of the parameter is below a specified minimum or above a specified maximum (or, e.g., deviates sufficiently from a single threshold), then a specific ty pe of PVA may be determined to be present. Furthermore, a measure of PVA severity' for the detected type of PVA may be, e.g., based at least in part on the numerical distance or amount of deviation from, e.g., the threshold(s).

[0160] Thresholds (or criteria incorporating thresholds) may be calculated in variousways for different types of PVA. For example, for ineffective efforts during expiration (IEE) and autotriggering (AT) (as described further herein), theoretical or expected curve(s), such as a mono-exponential expiratory flow curve, may be used in setting minimum and maximum thresholds. For example, a threshold level of deviation from the theoretical curve could be used in detecting IEE or AT, and the magnitude of the deviation could be used in determining the associated magnitude of PVA severity.

[0161] For cycling asynchronies, such as short cycling and prolonged cycling (as described further herein) thresholds may relate to, e.g., for a number of previous breaths, average mean inspiratory time (Ti,avg) compared with machine inspiratory' time (Ti,vent). Short cycling may be detected if Ti, vent is sufficiently small relative to Ti,avg and an associated measure of PVA severity may be based at least in part on how much smaller. Prolonged cycling may be detected if Ti, vent is sufficiently large relative to Ti,avg, and an associated measure of PVA severity may be based at least in part on how much larger.

[0162] For double triggering (as further described herein), detection may be based at least in part on, e.g., for a previous period (e.g., last two effective cycles) expiratory time being sufficiently small relative to average inspiratory time, and two (or other number) consecutive inspirations occurring with no expiration negative flow before the second inspiration. An associated measure of PVA severity may be based at least in part on, e.g., how much smaller the expiratory time is than the average inspiratory time.

[0163] For autotriggering (as further described herein), in pressure support mode, detection may be based on, e.g., for a previous period (e.g., the previous 20 breaths), a ratio of the number of machine-triggered breaths to total number of breaths being above a threshold, and an associated measure of PVA severity may be based on how much higher the ratio is than the threshold.

[0164] In some embodiments, a detected type of asynchrony may be an input to a system, while, in other embodiments, a type of asynchrony may be detected by the system, or the system may detect or participate in detection of asynchrony. Furthermore, in some embodiments, periodic or continuous updating may be performed by the system, and / or input to the system, regarding a type of asynchrony that is present or not present (e.g., a detected ty pe of asynchrony may later be determined to no longer be present or vice versa). Furthermore, in various embodiments, a type of asynchrony may be algorithmically detected, manually detected, or a combination of both.

[0165] In some embodiments, detection of expiration and / or inspiration, or the start of end thereof, may be used in detection of types of asynchrony. In some embodiments, aninspiratory trigger may be set, e.g., by a care provider when mechanical ventilation is initiated, that is sensitive enough to minimize a patient’s efforts and to prevent autotriggering. In some embodiments, to detect inspiration, software searches for a positive flow value of at least 12 L / min (or more or less). This relatively high threshold may minimize the rate of false positives and false negatives. However, flow may not usually reach 12 L / min until several milliseconds after the beginning of inspiration. To find the exact point in which inspiration begins, an algorithm may be used that steps backward from this threshold using the first derivative of the flow wave. This backward stepping process identifies the point in which the airflow slope starts to change.

[0166] In some embodiments, standard cycling off criteria may be used, e.g., with an inspiratory time less than 1 second in volume and pressure controlled ventilation (PCV) and 25% of peak inspiratory flow in pressure support ventilation (PSV), as may be set by a user when mechanical ventilation is initiated. To detect expiration, the software may search for a negative flow value of, e.g., at least 5 L / min (or more or less) that remains negative for, e.g., at least 25 ms (or more or less). This criterion may also be achieved later than the true beginning of expiration. To find the exact point in which expiration begins, the software may apply an algorithm that steps backward using the first derivative of the flow wave. This backward stepping process identifies the point in which the flow wave reaches zero or flow remains constant for. e.g., at least 10 ms (or more or less).

[0167] In some embodiments, when a patient is in PSV and the mode detection algorithm identifies the mode as PSV, an algorithm to detect short and prolonged cycling is enabled. The software classifies as short cycles PSV breaths in which machine inspiratory time is, e.g., less than half the mean inspiratory time (or more or less) (running mean of, e.g., 20 consecutive breaths, or more or less). The software classifies as prolonged cycles PSV breaths in which the ventilator inspiratory time, e.g., doubles the mean inspiratory time (or more or less) (running mean of 20 consecutive breaths, or more or less).

[0168] In some embodiments, short cycling and prolonged cycling are calculated by using a running mean over, e.g., the last 20 inspiratory’ times (Ti) and short cycling is identified when Ti of the current breath is. e.g., 50% (or other factor) shorter than the averaged Ti, or more or less, and prolonged cycling is identified when it’s, e.g., 200% (or other factor) larger, or more or less.

[0169] In some embodiments, during PSV, software classifies breaths as machine- triggered (likely autotriggering) and patient-triggered. The proportion of auto-triggered breaths may be defined, e.g., as the number of machine-trigger breaths multiplied by 100, ormore or less, and divided by the total number of ventilator cycles (machine or patient triggered).

[0170] In some embodiments, ineffective efforts may be characterized or defied, e.g., as contractions of the inspirator}' muscles (primarily the diaphragm) that are unable to trigger the ventilator to deliver a breath, and which may occur both during the ventilator’s inspiratory time and during its expiratory time. Inspiratory’ muscle contraction may decrease alveolar pressure, resulting in an abrupt decrease in expiratory flow. When the inspiratory muscles relax, alveolar pressure may increase, resulting in an abrupt increase in expiratory flow. Software may be used that detects ineffective expiratory' efforts (IEE), e.g., whenever it identifies an abrupt increase in expiratory flow preceded by an abrupt decrease in expiratory flow. The system may detect abrupt decreases or increases in expiratory flow by. e.g., searching for a positive maximum value followed by a negative minimum value on the first arm of a flow curve and then evaluating the deviations of these values against a monoexponential curve representing a theoretical mean expiratory' flow. To ensure that an event is an IEE rather than an inspiratory effort that persists beyond the ventilator’s inspiratory time, the algorithm may. e.g., disregards events visible during the first, e.g., 0.1 seconds (or more or less) of the expiratory' time.

[0171] In some embodiments, airflow and / or airway pressure recordings, which may be represented as waveforms, or related data, may be used the system determining the start of inspiration and of expiration. For example, in some embodiments, when positive flow is detected, the system steps backward to find the patient trigger point, represented by a peak on the first derivative of the pressure curve. Depending on the angle set by the two vectors (one defined by the beginning of expiration and the patient trigger point, and the other defined by when positive flow is detected and the peak in the first derivative of the pressure curve), the system determines whether the breath is triggered by the ventilator or by the patient. In some embodiments, the start of expiration is found by, e.g., searching for the first negative value after the beginning of the inspiration on the flow' signal.

[0172] In some embodiments, expiratory’ airflow recordings or waveforms, or related data, may’ be used in detecting IEE. For example, positive and the negative thresholds may be determined by the first derivative of the theoretical mean expiratory' flow'. The first derivative of both the actual expiratory’ flow' and the theoretical mean expiratory' flow’ may be compared. When an increase followed by a decrease on the expiratory flow' is found and the deviation (calculated as the first derivative of the expiratory flow divided by the first derivative of the theoretical mean expiratory flow) is higher than the threshold, the system may determine thatan IEE has occurred.

[0173] In some embodiments, double triggering (DT) may be characterized or defined as sustained inspiratory effort that persists beyond the ventilator’s inspiratory time, triggering a second ventilator breath, which may or may not be followed by a short expiration, where all or part of the volume of the first breath is added to the second breath.

[0174] In some embodiments, detection of DT may be based on previously used mathematical calculations, such as, e.g.. when 1) expiratory time is, e.g., > 50% shorter (or more or less) than the averaged inspiratory time or, e.g., 2) when two consecutive inspiratory cycles (positive flow -zero flow-positive flow) are detected with no expiration (negative flow) before the second inspiratory' time.

[0175] In some embodiments, ineffective inspiratory efforts may be characterized by, e.g., contractions of the inspirator}' muscles, primarily the diaphragm, not followed by a ventilator breath. This may occur when the patient’s attempt to initiate a breath does not reach the ventilator’s trigger threshold; physiologically, it may be characterized by an increase in transdiaphragmatic pressure (decrease in esophageal pressure, increase in gastric pressure) and / or electrical activity of the diaphragm. Ineffective inspiratory efforts may result in the patient’s respiratory rate being higher than the ventilator’s rate; ineffective efforts usually occur during expiration (ineffective expiratory efforts, or IEE), but can also occur during the inspiratory phase.

[0176] In some embodiments, detection of IEE may include use of software that may, e.g., compute a theoretical mono-exponential expiratory flow curve and compare it with the actual ones by evaluating its percentage deviation (0%=no deviation; 100%=maximum deviation). The theoretical curve may be obtained by the averaging of, e.g.. the 20 (or more or less) previous normal expirations in which there are no deviations that could represent an IEE.In some embodiments, Short cycling (SC) may be characterized or defined as an inspiratory time, e.g., less than one-half (or more or less) the mean inspiratory' time, whereas prolonged cycling (PC) may be characterized or defined as an inspiratory time that is, e.g., greater than twice the mean inspiratory time (or more or less). The inspiratory time may be characterized or defined as, e.g., the time during which gas flow is positive, and mean inspiratory' time is calculated over the previous, e.g., 20 cycles (or more or less).

[0177] Furthermore, in some embodiments, measures of severity may take into account, or be based at least in part on, the respiratory system equation of motion:(Equation 2) Pmus + Pventwhere: Pmus = muscle pressure generated by the patient’s inspiratory effort, Pvent is the airway pressure generated by the ventilator, Ers is the patient’s respiratory system elastance, V is the volume change relative to the end-expiratory lung volume, Rrs is the patient’s respiratory system resistance, and Vdot is the gas flow into and out of the lungs. Generally, the respiratory sy stem equation of motion shows that there are two “pumps” that must work together: the ventilator (as represented by Pvent) and the patient’s ventilatory muscles (Pmus). If these two pumps do not work together correctly during mechanical ventilation, asynchrony may result

[0178] For illustration purposes, various of the following figures use modeled, rather than actual, patient data.

[0179] FIG. 8 includes graphs 800, 807 with curves 808, 806 illustrating an example of determining a PVA severity score for a period of time based on estimated work of breathing. The technique described with reference to FIG. 8, while not limited to the embodiment described with reference to FIG. 7, provides an example of one implementation of step 680 of FIG. 7, which includes calculating a measure of PVA severity using estimated WOB.

[0180] Specifically, graph 800 relates to the patient’s WOB (in J / min) during mechanical ventilation, estimated on a breath by breath basis over a 1 hour time period. The plotted points 805 reflect actual breath by breath estimates of the WOB. It is noted that, in plotted points 805. points are plotted for each breath. In the embodiment shown, to reduce the potential effect of noise on the measurements, smoothed curve 808 is included and reflects a calculated 1 minute moving average of the breath by breath WOB estimates. In some embodiments, a moving average may be used, such as to filter out potential noise or other inaccuracies. In some embodiments, however, a moving average may not be used, or other processing techniques may be applied to the breath by breath estimates, such as to filter out potential noise or other inaccuracies. Additionally, in embodiments in which a moving average is used, the period of the moving average could be other than 1 minute, such as, e.g., 30 seconds, 90 seconds, or 2-5 minutes, 5-30 minutes or 30-60 minutes.

[0181] Smoothed curve 808 is used in determining curve 806, which provides a PVA severity score scaled to between 0 and 1, with 1 being the most severe. In this embodiment, the breath by breath PVA severity7score provides a breath by breath measure of PVA severity, scaled to between 0 and 1. Specifically, PVA severity score curve 806 is proportional to the WOB curve 808. except that it is scaled to between 0-1. and it is bounded using upper threshold 801 and lower threshold 802, shown in graph 800 (although, in otherexamples, other measures of PVA severity may be used, and lower and / or upper bounding may not be used). With respect to bounding of PVA severity score curve 806, any WOB at or below 5 J / min (or, in other examples, between 3-5 J / min, 5-10 J / min or 10-20 J / min) is assigned a PVA severity score of 0 and any WOB at or above 20 J / min (or, in other examples, at or above between 15-25 J / min), 25-50 J / min or 50-100 J / min is assigned a PVA severity score of 1. In some examples, such bounding may provide a more accurate reflection of PVA severity, for example, if a WOB above the upper threshold 801 may not reflect a greater, or significantly greater, PVA severity than a WOB at the upper threshold, and if a WOB below the lower threshold 802 may not reflect a lesser, or significantly lesser, PVA severity than a WOB at the lower threshold. For example, an ideal WOB may be greater than zero. Reasons for this may include that a small or moderate amount of patient effort may be desirable in order to avoid, e.g., muscular atrophy, and to prepare the patient for when they are no longer on a ventilator and must breathe entirely on their own.

[0182] FIG. 9 illustrates an example of determining a measure of PVA severity (e.g., for a detected type of PVA) for a period of time of multiple breaths (breaths 1-X), using a determined magnitude of PVA severity for each breath 530. The example relates to a period of time during mechanical ventilation in which multiple breaths (breaths 1-X) are provided to a patient.

[0183] A magnitude of PVA severity 532 is determined for each of breaths 1-X. The determined magnitudes of severity for all of the breaths 1-X (or, in other examples, some of the breaths) in the period of time is used in determining an overall measure of PVA severity 533 for the period of time. The measure of PVA severity may be used in determining a quantified amount of adjustment to a ventilatory parameter for mitigation of the detected ty pe of PVA 534 (e.g., in various embodiments, this may include an amount of adjustment recommended to a care provider or automatically implemented by the mechanical ventilation system).

[0184] In various embodiments, a measure of PVA severity for a period of time 533 including multiple breaths 1-X may be determined in various ways. For example, a determined or calculated magnitude of PVA severity for each breath of the multiple breaths 1-X (or, in some embodiments, some of the breaths 1-X (e g., including regularly spaced or irregularly spaced representative breaths) may be used in determining or calculating the measure of PVA severity' for the period of time 533. For example, in some embodiments, the measure of PVA severity’ for the period of time 533 may be calculated as the average or mean of the magnitude of PVA severity for all of the multiple breaths 1-X. As another example, themeasure of PVA severity for the period of time 533 may be calculated as, or based on (e.g., given by. proportional to, or otherwise mathematically calculated based on) an area under a curve (e.g., determined by integration or other mathematical determination or estimation techniques) associated with or representing the magnitude of PVA severity for each of the breaths 1 -X over the period of time. In various embodiments, for example, the curve may be generated to include points representing the magnitude of PVA severity’ for each of the breaths 1-X, where adjacent points may be connected, for example, by straight lines or using a best fit curve or other technique, model or algorithm or artificial intelligence, or may be determined using a mathematically smoothed curve, moving average of such points or associated points, or in other w ays.

[0185] In various embodiments, a curve representative of a breath by breath magnitude of severity over a period of time (e.g., as may be associate with WOB, waveform deviation, or both) may be used in determining a measure of PVA severity over the period of time, or, in some embodiments, a curve associated with (but not necessarily representative of or entirely representative of) a breath by breath magnitude of PVA severity over a period of time may be used (e.g., a curve determined based at least in part on a breath by breath magnitude of PVA severity over a period of time) in determining the measure of PVA severity’ for the period of time. For example, in FIG. 8, PVA severity’ score curve 806 may be used in determining a measure of PVA severity for the one hour period of time, e.g., by determining the area under the curve 806 (including the portion bounded using upper threshold 801 and lower threshold 802), where the measure of PVA severity for the period of time may be calculated to be, e.g., proportional to or given by the determined area under the curve 806, or otherwise associated with or calculated based on curve 806. As an additional example, in FIG. 12, described in detail below, PVA severity score curve 826 could be used in determining a measure of PVA severity over the one hour period of time, such as using a calculated area under the curve 826 or in other ways based on curve 826.

[0186] FIG. 10 is a flow diagram illustrating a method 570 for determining a magnitude of PVA severity for a single patient breath, including comparison of an asynchronous patient airway pressure or flow waveform with a sample synchronous airw ay pressure or flow waveform.

[0187] In the embodiment of method 570, at step 572, a controller, e.g., coupled with a mechanical ventilation system providing mechanical ventilation to a patient, obtains a sample synchronous airway pressure or flow waveform associated with a synchronous breath (without any type of PVA, including the detected type of PVA for the patient). Next, at step574, the controller determines a patient airway pressure or flow w aveform, associated with an asynchronous patient breath during the mechanical ventilation, including the detected type of PVA.

[0188] Next, at step 576, the controller compares the synchronous sample airw ay or flow waveform with the asynchronous patient airway pressure or flow waveform to determine a deviation between them. Next, at step 578, based on the determined deviation, the controller determines the magnitude of PVA severity for the patient breath.

[0189] In various embodiments, the sample synchronous airway pressure or flow' waveform may be obtained in various ways. For example, it could represent an actual breath of a person (e.g., a synchronous breath of the patient, or of another person), or it could be generated computationally, without representing or being obtained from an actual breath. In various embodiments, it may be obtained in various ways, such as by being generated by the controller coupled with the mechanical ventilation system providing mechanical ventilation to the patient, or it may be input to the controller from another source. In some embodiments, it may be computationally generated based on Equation 2, as described above - Pmus + Pvent = Ers*V + Rrs*Vdot + PEEP, which represents the respiratory system equation of motion. Furthermore, in some embodiments, an initially obtained sample synchronous breath airway pressure or flow waveform may be computationally modified before use as the sample synchronous breath airway pressure or flow waveform, such as if modification may improve the accuracy of its use. for example, to be more customized to breathing or breathing waveforms of the patient. For example, in some embodiments, one or more synchronous breaths may be used to estimate parameters such as respiratory' system elastance (Ers) and respiratory system resistance (Rrs). Customized airway pressure or waveforms may then be generated, such as based on various factors, including factors that may change over time, including current ventilator setting or patient effort changes.

[0190] FIGs. 11A-B and 12 provide examples of determining amounts of waveform deviation between a sample synchronous airway pressure waveform (or, in some embodiments, several may be used) and asynchronous patient airway pressure waveforms over a period of time, and using determined deviations in determining a varying measure of PVA severity over the period of time. The waveforms, such as synchronous and asynchronous airw ay pressure and flow w aveforms, may change over time (and may be determined at various times) based on various factors, which may include, e.g.. ventilator settings and patient effort changes.

[0191] FIG. 11 A includes curve 950 and plotted points 952 illustrating an example ofdetermining amounts of waveform deviation between each of multiple asynchronous patient breaths, from a patient with detected insufficient flow (IF) PVA and a sample synchronous breath, for use in determining a varying measure of PVA severity for the IF over a period of time. Curve 950 shows patient airway pressure, in cm H2O, over a period of over approximately 2 minutes. For the same period of time, the plotted points 952 show determined varying waveform deviation, in cm H2O * second, on a breath by breath basis, between the patient airway pressure (which varies with each breath) and a sample synchronous patient airway pressure waveform (where, in some embodiments, the same sample synchronous waveform may be used for each breath). Although the sample synchronous waveform is not shown, FIG. 27 shows a normal or sample patient airway pressure waveform 3101, which may be an example of a sample synchronous waveform.

[0192] For each breath (or at least some breaths) over the period of time, the deviation betw een the waveforms is calculated, and a corresponding point is plotted in the plotted points 952. For interval 960, between approximately 0.9 minutes and 1.15 minutes, the patient airway pressure as shown in FIG. 11 A is shown in close-up in FIG. 1 IB.

[0193] FIG. 1 IB includes curve 814 and plotted points 815. with curve 814 showing patient airway pressure, in cm H2O, over interval 960, and plotted points 815 showing, on a breath by breath basis, waveform deviation, in cm H2O * second, over interval 960 (that is, the deviation between each asynchronous patient airway pressure waveform and the synchronous sample airway pressure waveform). Curve 814 shows a close-up of interval 960 of the patient airway pressure curve 950 of FIG. 1 1 A. Four patient airway pressure w aveforms 830-833 are showm, each relating to a patient breath. For each of the waveforms 830-833, a darkened area 840-843 is shown. Each darkened area 840-843 indicates an area of deviation, relative to a single breath sample synchronous airway pressure waveform. Dotted segment 839 indicates the portion of the plot as it would appear, or approximately as it would appear, in the sample synchronous airway pressure waveform. The area of deviation 840 is calculated as the area between segment 839 of the sample w aveform and portion 844 of the patient airway pressure waveform 830, which occurs between the times indicated by dotted lines 845 and 846. In some embodiments, segment 839 may be the actual corresponding portion of the sample airway pressure waveform. However, in some embodiments, a straight line or other curve, such as a curve algorithmically generated to approximate the portion of the sample airw ay pressure waveform, may be used to approximate the corresponding portion of the sample airway pressure waveform. As can be seen, area of deviation 840 is the largest of the four 840-843, with the remaining areas of deviation, listed in order of decreasing size,as area of deviation 842, area of deviation 843 and area of deviation 841. It is noted that, in some embodiments, deviation may be determined over the entire breath. However, in FIG. 1 IB, the waveforms only deviate over the period of time between dotted lines 845 and 846.

[0194] In FIG. 11B, the plotted points 815 represent the varying waveform deviation (in cm H2O) over interval 960, on a breath by breath basis, and includes points 835-838 corresponding with areas of deviation 840-843, respectively. In the embodiment depicted, each of the points 835-838 is plotted at the end of the associated breath. However, in other embodiments, other techniques may be used, such as plotting the points in the middle or the start of each associated breath. As can be seen, point 835 represents the largest area of deviation of the four breaths, corresponding with area of deviation 840. with the remaining points 836-838 reflecting the relative sizes of the remaining areas of deviation 841-843 for the other three breaths over the interval 960. Thus, more generally, waveform deviation may be determined based on determination of areas of deviation over two or more breaths over an interval.

[0195] FIG. 12 includes graphs 820, 825, illustrating an example of determining a PVA severity score for a period of time using a moving average of determined waveform deviations. In graph 820, the plotted points 823 represent the waveform deviation (in cm H2O * second), on a breath by breath basis, over the 1 hour time period (that is, the deviation between each of the asynchronous patient airway pressure waveforms and the synchronous sample airway pressure waveform). Smoothed curve 822 represents a 1 minute moving average of the waveform deviation. In the embodiment shown, to reduce the potential effect of noise on the measurements, smoothed curve 822 is included and reflects a calculated 1 minute moving average of the breath by breath waveform deviations. In some embodiments, however, a moving average may not be used, or other processing techniques may be applied, such as to filter out noise or other potential inaccuracies. Additionally, in embodiments in which a moving average is used, the period of the moving average could be other than 1 minute, such as, e.g., one or more breaths, 30 seconds, 90 seconds, or 2-5 minutes of 5-30 minutes.

[0196] In graph 825. curve 826 provides a varying (breath by breath) calculated PVA severity score for the 1 hour time period. In this example, waveform deviation of between 0 and 2 cm H2O is converted to a breath by breath PVA severity score of between 0 and 1, with 1 being the most severe.

[0197] In this embodiment, the breath by breath PVA severity score provides a breath by breath measure of PVA severity, scaled to between 0 and 1 or otherwise normalized.Specifically, in plot 825, PVA severity score curve 826 is proportional to the WOB smoothed curve 822, except that it is scaled to between 0-1, and, if applicable, may be bounded an upper threshold 821 and by a lower threshold 824, shown in plot 820 (although, in various examples, lower and / or upper bounding may not be used). With respect to bounding of PVA severity' score curve 826, any waveform deviation at or below the lower threshold 824 is assigned a PVA severity score of 0. In the example shown, the lower threshold 824 is set at 0 cm H2O * second, so effectively does not apply. However, in other examples, the lower threshold may be set above 0 cm H2O * second and would apply to any waveform deviation at or below that lower threshold. Furthermore, in the example shown, and any waveform deviation at or above 2 cm H2O * second (or, in other examples, at or above between 1.5, 1.75, 5 or 10 cm H2O * second) is assigned a PVA severity score of 1. In the period of time shown, however, the waveform deviation does not reach the upper threshold, so the upper threshold does not operate in this example. In some examples, such bounding may provide more accurate reflection of PVA severity', for example, if a waveform deviation above the upper threshold may not reflect a greater, or significantly greater, PVA severity than a waveform deviation at the upper threshold, and if a waveform deviation below the lower threshold may not reflect a lesser, or significantly lesser, PVA severity than a waveform deviation at the lower threshold. Reasons for this may include, e.g., to minimize any effect of noise / inaccuracy, such as may result from, e.g., slight differences between the sample and measured waveforms due to measurement noise, normal slight breath-to-breath variations, etc., which slight differences would not correspond to asynchronies.

[0198] FIG. 13 is a block diagram 3050 illustrating an example of calculation of a measure of PVA severity' 3060 based on elements relating to work of breathing 3052 and waveform deviation 3054. Block 3052 represents a WOB related element (element one) and block 3054 represents a waveform deviation related element (element two), where element 2 may, e.g., relate to a deviation between a sample waveform and an asynchronous waveform, examples of which are described herein.

[0199] Block 3056 represents a mathematically expressed relationship between element one 3052 and element two 3054, which may be used in calculation 3058 of a measure of PVA severity' 3060, which may, for example, include use of one more expressions, formulas, equations, terms or mathematical or numerical structures. For example, the calculation 3058 may include use of one or more computational models, templates, algorithms, programs, or machine learning or artificial intelligence, or associated models, such as may include use of one or more neural networks, decision trees, trainingmodels, features or feature sets, such as may be associated with the relationship between element one 3052 and element two 3054. In some embodiments, the calculation 3058 may make use of obtained or historical data associated with element one 3052, element two 3054, or the relationship between the two, such as may include collected data relating to past ventilation of patients, which may include WOB, waveform deviation, or calculated measure of severity data, as well as measured patient physiological data or ventilatory parameter data. In some embodiments, such historical data may be used in forming feature sets or models for use with one or more artificial intelligence models or algorithms. In some embodiments, the calculation 3058 may include multiple steps or one or more iterative steps, determination of one or more maximum or minimum values, use of thresholds, use of one or more asymptotes, or may include determination, estimation or integration of an area under a curve, such as may be associated with the relationship 3056 between element one 3052 and element one 3054, such as over a period of time for which the measure of PVA severity is being calculated.

[0200] In various embodiments, the measure of PVA severity 3058 may be calculated in various ways, utilizing a WOB related element and the waveform related element, potentially among other things. For example, a mathematical formula 3056 may be used. In some embodiments, each of element one 3052 and element two 3054 may correspond with one or more portions or terms of the mathematical formula. In various embodiments, in the mathematical formula 3056, each of element one 3052 and element two 3054 may be weighted equally, or may be weighted unequally, such as based on one or more of various factors. For example, if using unequal weighting, a coefficient, such as a coefficient between 0 and 1, where both coefficients added together equal 1, may be associated with a term in the formula reflecting each element. For example, in some embodiments, the w eighting of each element 3052, 3054 may be based at least in part on a confidence level associated with each element 3052, 3054. For example, the confidence level may express a degree of confidence in the degree of accuracy and / or reliability of the element, such as based on factors including, e.g., stored historical data relating to previous associated outcomes and accuracy as relates to each element 3052, 3054. For example, in some embodiments, a formula generally taking the form of Equation 3, as follows, may be used.

[0201] (Equation 3) Measure of PVA severity = (coefficient 1) (element 1 variable) + (coefficient 2) (element 2 variable)

[0202] In Equation 3, the element 1 variable may be given (e.g., by determination or calculation) a value associated with element one 3052 and the element 2 variable may be given a value associated with element two 3054. Furthermore, coefficient 1 and coefficient 2may weight each of the two variables, such as, e.g., based on confidence level, and / or one or more other factors. For example, if it is determined or calculated that there is a certain amount of greater confidence in the waveform deviation element (element 1) than the WOB element (element 2), then coefficient 1 may be determined to be greater than coefficient 2 (e.g., coefficient 1 may be calculated to be 0.7 and coefficient 2 may calculated to be 0.3). In some embodiments, a maximum and / or minimum of the two elements 3052, 3054 may be taken into account, such as by weighting the maximum or minimum less (or not at all), such as if the maximum or minimum may present greater risk of being inaccurate. Furthermore, in some embodiments, it may be considered that waveform deviation may present a very direct measure of PVA severity, which may lead to an increase in weighting of element two.

[0203] FIG. 14 includes graphs 3020, 3022 illustrating an example of determination and use of PVA clusters in determining a measure of PVA severity for a period of time, including use of a PVA severity index. In some embodiments, a PVA severity index for a period of time may be used as a measure of PVA severity for the period of time. In some examples, the PVA severity index may include aspects that are similar to the asynchrony index, as described above. However, rather than utilizing a binary model, where all asynchrony events are treated the same, the PVA severity index may take into account a determined magnitude of PVA severity7for each asynchronous breath, and therefore provide a more accurate measure of PVA severity for the period of time. For example, in some embodiments, the PVA severity index may be calculated according to the following equation:

[0204] (Equation 4) PVA severity’ index = (sum of PVA severity scores, scaled from 0 to 1, for each asynchrony event) / (ventilator cycles + wasted efforts)

[0205] In some examples, in Equation 4, the PVA severity' score for each asynchrony event (e.g.. each asynchronous breath) is scaled to between 0 and 1, with 1 being most severe (examples of calculation of a PVA severity score are provided herein, including with reference to FIGs. 8 and 12). For example, using Equation 4, if, for an applicable period of time, all breaths were synchronous, then the PVA severity' index would have a value of 0.0. Furthermore, if all of the breaths were asynchronous and have a PVA severity score of 1.0, then the PVA severity index would have a value of 1.0. However, for example, if ventilator cycles + wasted efforts (which includes asynchronous breaths) = 50, and there were 5 asynchrony events (e.g., asynchronous breaths) with an average PVA severity score of 0.5, then, according to Equation 4, the PVA severity score may be calculated as: [(5 * 0.5) / 50] = 0.05. As such, the PVA severity index for the period of time would be 0.05. In various embodiments, however, the PVA severity index may be calculated differently. For example,instead of ventilator cycles + wasted efforts, just ventilator cycles (e.g., ventilator provided breaths) may be used. Also, a PVA severity index scaled to other than 0-1, or a different measure of PVA severity entirely, may be used, which would result in different ranges of values for the PVA severity index.

[0206] In FIG. 14, the plotted points 3001 show the calculated PVA severity index (which may, for example, be calculated as described above with reference to Equation 4), on a breath by breath basis, over a period of time of 1 hour during the providing of mechanical ventilation to a patient. Smoothed curve 3004 shows a 1 minute moving average of the calculated PVA severity index. In some embodiments, a moving average may be used, such as to filter out potential noise. In some embodiments, however, a moving average may not be used, or other processing techniques may be applied to the breath by breath estimates, such as to filter out potential noise or other inaccuracies. Additionally, in embodiments in which a moving average is used, the period of the moving average could be other than 1 minute, such as, e.g.. 30 seconds, 90 seconds, 2-5 minutes, 5-30 minutes or 30-60 minutes.

[0207] In the 1 hour period shown in FIG. 14, two cluster periods 3006, 3016 are identified. A cluster period may be identified as a period in which, for at least a specified amount of time, a specified parameter associated with PVA severity (e.g., in FIG. 14, the PVA severity index) is at or above a specified threshold. In some embodiments, in determining a measure of PVA severity for the period of time as a whole (e.g., the 1 hour period in FIG. 14). cluster periods (e.g., cluster periods 3006. 3016) may be treated differently, or factored in or weighted more heavily, than PVA non-cluster periods (e.g., any period that is not identified as a PVA cluster period). This may be in accordance with evidence that, for a given period of time, presence of PVA cluster periods may be associated with worse mechanical ventilation patient outcomes.

[0208] In the example shown in FIG. 14, cluster periods are identified as any interval over the 1 hour period during which the PVA severity' score remains continuously at or above a threshold of 0.05 (or, e.g., 0.0-0.3, 0.03-0.07, 0.07-0.25, or 0.25-0.50), as shown by dotted line 3002, for at least 1 minute (or. e.g., 30 seconds-3 minutes, 3 minutes-15 minutes, or 15 minutes -30 minutes). As shown, cluster period 3006 is identified as lasting 26.3 minutes, and cluster period 3016 is identified as lasting 7.2 minutes.

[0209] Cluster power curve 3025 of graph 3022 shows what is termed herein to be the cluster pow er over the 1 hour period. For any interval that is not identified as a cluster period, the cluster power is set to 0. For any interval this is defined as a cluster period, the cluster power, for the entire interval, is set to the total area under curve 3025 for that cluster period.In curve 3025, the cluster power 3014 associated with cluster 3006 is calculated to be 3.38 and the cluster power 3012 associated with cluster period 3016 is calculated to be 0.52. In some embodiments, a measure of PVA severity for the 1 hour period as a whole may be determined using the cluster power curve 3025. For example, the measure of PVA severity for the 1 hour period as a whole may be calculated as the total area under cluster power curve 3025.

[0210] FIG. 15 includes plots 851-853 illustrating an example of use of ranges of determined PVA severity scores, e.g., for a period of time including multiple patient breaths, in determining amounts of adjustments to ventilatory parameters to mitigate a detected type of PVA. In some embodiments, the techniques described with reference to FIG. 15 may provide examples of adjustment or quantification of a ventilatory parameter based on a measure of PVA severity', such as indicated in feature 1 142 of FIG. 20. Furthermore, the techniques, or variations thereof, including using a discrete set of adjustments to a ventilatory parameter that apply to different ranges of a measure of PVA severity, can be applied with regard to other types of detected PVA or other contexts (e.g., as examples of feature 1240 of FIG. 22, feature 1742 of FIG. 23, feature 1340 of FIG. 24, feature 1442 of FIG. 25, feature 1540 of FIG. 26, feature 1640 of FIG. 28, and feature 682 of FIG. 7 including adjustments based on a measure of severity determined using WOB).

[0211] In each of the plots 851-853, each of a series of specified ranges for a parameter associated with a measure of PVA severity (here, PVA severity scores scaled from 0 to 1) are associated with a single ventilatory parameter adjustment amount. This may be particularly advantageous given that it may only be possible to change ventilatory' parameters in discrete amounts. In the example of FIG. 15, the higher the range of PVA severity score, the greater the amount of the adjustment.

[0212] Specifically, in plot 851, each of multiple ranges of the PVA severity score are associated with a specific change in trigger sensitivity' (e.g., an increase or decrease amount, as may be called for, e.g., to mitigate a specific detected type of PVA). In the example shown, specifically, the ranges of PVA severity score and associated changes in trigger sensitivity are: PVA severity range over zero and up to 0-0.2 calls for a 0.5 cm H2O change; PVA severity' range over 0.2 and up to 0.4 calls for a 1.0 cm H2O change; PVA severity range over 0.4 and up to 0.6 calls for a 1.5 cm H2O change; PVA severity range over 0.6 and up to 0.8 calls for a 2.0 cm H2O change; and PVA severity range over 0.8 and up to 1.0 calls for a 2.5 cm H2O change.

[0213] In plot 852, each of multiple ranges of the PVA severity score are associatedwith a specific change in inspiratory or expiratory time (e.g., an increase or decrease amount, as may be called for. e.g., to mitigate a specific detected type of PVA). In the example shown, specifically, the ranges of PVA severity score and associated changes in inspiratory or expiratory time are: PVA severity range over zero and up to 0. 1 calls for a 0.5 second change; PVA severity7range over 0.1 and up to 0.2 calls for a 0.1 second change; PVA severity7range over 0.2 and up to 0.4 calls for a 0.2 second change; PVA severity range over 0.4 and up to 0.6 calls for a 0.3 second change; PVA severity range over 0.6 and up to 0.8 calls for a 0.4 second change; and PVA severity7range over 0.8 and up to 1.0 calls for a 0.5 second change.

[0214] In plot 853, each of multiple ranges of the PVA severity score are associated with a specific change in respiratory rate (e.g., an increase or decrease amount, as may be called for, e.g., to mitigate a specific detected type of PVA). In the example shown, specifically, the ranges of PVA severity score and associated changes in respiratory rate are: PVA severity7range over zero and up to 0.2 calls for a 1 breath per minute (BPM) change; PVA severity7range over 0.2 and up to 0.4 calls for a 2 BPM change; PVA severity range over 0.4 and up to 0.6 calls for a 3 BPM change; PVA severity range over 0.6 and up to 0.8 calls for a 4 BPM change; and PVA severity range over 0.8 and up to 1.0 calls for a 5 BPM change.

[0215] FIG. 15A includes plots 870-872 illustrating an example of use of determined categories of PVA severity for a detected type of PVA, e.g., for a period of time including multiple patient breaths, in determining and presenting a recommended measure to mitigate the detected t pe of PVA. In some embodiments, amounts of adjustments to ventilatory parameters may be based on categories of PVA severity. While not shown in FIG. 15A, in some examples, each category may be defined as including a specified range of a measure of PVA severity7. For example, a mild category may include a lowest range, a moderate category may include a middle range, and a severe category may7include a highest range. Based on the category7, a specified amount of an adjustment to a ventilatory parameter may be indicated.

[0216] For example, in plot 870. a mild category7PVA severity 863 calls for a 0.5 cm H2O change in trigger sensitivity7(e.g., an increase or decrease depending on the detected ty pe of PVA), a moderate category7PVA severity 862 calls for a 1.5 cm H2O change in trigger sensitivity7, and a severe category7PVA severity7861 calls for a 2.5 cm H2O change in trigger sensitivity7. In plot 871, a mild category7PVA severity 866 calls for a 0.1 second change in inspiratory or expiratory time (e.g., depending on the detected type of PVA), a moderate category PVA severity7865 calls for a 0.3 second change in inspiratory orexpiratory' time, and a severe category PVA severity' 864 calls for a 0.5 second change in inspiratory or expiratory time. Finally, in plot 872, a mild category PVA severity 869 calls for a 1 BPM change in respiratory rate (e.g., an increase or decrease, depending on the type of PVA), a moderate category PVA severity 868 calls for a 3 BPM change in respiratory rate, and a severe category' PVA severity' 867 calls for a 5 BPM change in respiratory' rate.

[0217] Based on a determined appropriate mitigation measure (which may depend, for example, on the detected type of PVA and other factors), a recommended mitigation measure may be displayed to a care provider, such as on a tablet or other device (e.g., a computing device display, medical device display, ventilator display, defibrillator and / or patient monitor display, or a display of another device). For example, as shown in FIG. 15A, based on detected ineffective efforts during expiration (IEE) determined to be of a severe category 861, a display 871 (which may be a notification such as, e.g., a message, alert or alarm, or part of one) is provided on a display of a tablet 870 of a care provider, notifying the care provider that severe IEE has been detected and recommending increasing trigger sensitivity by 2.5 cm H2O.

[0218] FIG. 16 is a flow diagram illustrating an example method 550 for providing recommendations for mitigation of PVA severity, which may' include a series of recommended measures.

[0219] In method 550, at step 552. a controller, e.g., coupled with a mechanical ventilation system, provides a recommendation to implement a fist measure (e.g., the measure determined to be optimal to take first) to mitigate a detected type of asynchrony (e.g., an adjustment to a ventilatory' parameter, adjustment of some other parameter relating to patient care, administration of a drug to the patient, or some other measure). At step 554, the first measure is implemented by a care provider. At step 556, the controller monitors a determined measure of PVA severity' over a period of time following implementation of the first measure.

[0220] At step 558, if the measure of PVA severity' has decreased (or, in other examples, has decreased beyond some threshold), then a second measure (e.g., the measure determined to be optimal to take next) to mitigate the PVA is not currently warranted (e.g., the first measure is indicated as being effective), and, as such, at step 562, the controller does not provide a recommendation to implement of another measure to mitigate the PVA. However, at step 558, if the measure of PVA severity has not decreased (e.g., the first measure is indicated as ineffective or insufficiently effective), then, at step 560, the controller determines that a second measure to mitigate the PVA is warranted and provides arecommendation to implement a second measure to mitigate the PVA. At step 564. the second measure to mitigate the PVA is implemented by the care provider. It will be appreciated that in some examples, only one of the two branches from step 558 may be implemented or the other of the two branches may differ from the example given.

[0221] At step 566, the controller continues to monitor the measure of PVA severity over time. Similar to the above, if and when appropriate based on continued monitoring, the controller may provide a recommendation to implement a third measure to mitigate the PVA (e.g., the measure determined to be optimal to take next), etc.

[0222] FIG. 17 illustrates an example of use of a method for providing recommendations for mitigation of PVA severity. In some embodiments, an example of which may be provided by method 550 of FIG. 16, during mechanical ventilation of a patient, following implementation of a first measure taken to mitigate a detected type of asynchrony (e.g., the measure determined to be optimal to take first, if needed), a breath by breath measure of PVA severity is determined and monitored over time. If and when certain specified conditions are met. a recommendation may be triggered to implement a second PVA mitigation measure (e.g.. the measure determined to be optimal to take next, if needed). For example, the conditions may include that the measure of PVA severity has not decreased for a specified period of time following implementation of the first measure. In some examples, this may reflect that, if the first measure is effective (or effective enough) in mitigating the PVA, then it may be optimal not to implement another measure at a certain time; however, if the first measure is not effective (or not effective enough) in mitigating the PVA, then it may be optimal to recommend, at a certain time (or, in some embodiments, automatically implement) a second measure to mitigate (or further mitigate) the PVA.

[0223] In FIG. 17, curve 881 shows the determined PVA severity score (scaled from 0 to 1) over a 1 hour time period. In the example shown, starting shortly before the 10 minute mark, ineffective expiratory efforts (IEE) PVA 885 is detected. A controller coupled with the mechanical ventilation system recommends intervention 1 882 (e.g., a first mitigation measure), which is determined to be optimal as a first intervention for mitigating the IEE and, at approximately time = 12 minutes, a care provider implements intervention 1 882 (e.g.. intervention 1 882 may be to increase trigger sensitivity by 1 cm H2O). During wait period 884, the controller monitors the PVA severity7score. At the end of the wait period 884, if IEE is still present and the PVA severity score has not decreased (or not decreased more than a predetermined amount) from the start of the wait period, then the controller recommends a different intervention - intervention 2 883 (e.g., to decrease sedation by a specified amount),which is determined to be the optimal next intervention. This may be an example of use of a prioritized list of measures, one example of which is shown in feature 1122 of FIG. 20. At approximately time = 22 minutes, the care provider implements intervention 2 883 (where, e.g., implementation of measure 2 883 may be automatically detected by the system, or the care provider may input to the system that the measure has been implemented). Following implementation of intervention 2 883, the controller continues to monitor the measure of PVA severity, and the measure of PVA severity decreases steadily until the IEE is determined to no longer be present at approximately time=29 minutes, and no additional intervention is determined to be recommended.

[0224] FIG. 18 is a block diagram illustrating an example of types of data 750 utilized by a PVAM, and operation of a PVAM. Some or all of the types of data 750 may be used in various embodiments. Various data may be used as input to an example PVAM, whether, e.g., determined by the PVAM and / or obtained by the PVAM from outside of the PVAM. Various types of sensor data 902 (including, e.g., sensed pressure, flow data, oxygen, carbon dioxide, and / or others) may be used, including in obtaining or determining other data. Signal processing 916 may be applied to ventilation related waveforms 904 (e.g., airway pressure and flow waveforms obtained using sensed pressure and flow data), to determine processed (e.g., subject to digital signal processing) ventilation related waveforms 918. Other utilized data may include various ventilation related measurements 906, ventilation breath related data 908. ventilation settings 910, and ventilation related alarms, including active alarms 912. Furthermore, various types of data regarding the patient 914 may be used, such as patient age, sex, height, body weight related parameter(s) (e.g., predicted bodyweight), estimated patient respiratory parameter(s) such as respiratory mechanics parameter(s) or any of various respiratory system properties or characteristics, and / or patient medical history or health condition data.

[0225] In some embodiments described herein, a detected type of PVA is provided as input to a PVAM, although, in other embodiments, the PVAM itself may detect a ty pe of PVA that is present (or, e.g., likely, suspected, or potentially present), or the PVAM may participate in the detection. Block 922 represents a detected type of PVA 922, e.g.. detected using one or more PVA detection algorithms 920 (although, in some instances or embodiments, a type of PVA may be detected manually or partially manually), which may be provided as input to a PVAM.

[0226] Block 924 represents algorithm(s) of the PVAM, which may be used in applying logic and conditions 926, e.g., to obtained data, in determining output. In someembodiments, the PVAM obtains (whether, e.g., determined by the PVAM or obtained by the PVAM from outside of the PVAM) PVA severity related data, such as measure of PVA severity data 925, which may be used by the PVAM in determining the output. However, in some embodiments, PVA severity related data or measure of PVA severity data may not be obtained or used.

[0227] The output may include PVA root cause and mitigation notification(s) 928, which may be presented, such as displayed 932 to a user (and, in some embodiments, interacted with), and / or PVA mitigation adjustment(s) 930, such as to ventilation parameter(s) (which, in some embodiments, may relate to PCLC of one or more ventilatory parameters), which, in various embodiments, may be implemented with or without required user confirmation or other interaction.

[0228] FIG. 19 is a table 900 of some example types of PVA (includes published material in Minerva Anestesiol. 2019;85(6):676-688), for which, in some embodiments, PVA mitigation output may be determined. PVA types (without limitation to such types) may include what may be referred to as, e.g., trigger asynchronies (e.g., related to breath initiation), cycle asynchronies (e.g., related to breath termination) and flow or volume related asynchronies (e.g., related to a mismatch between the ventilator’s inspiratory flow or volume setting and the patient’s demand).

[0229] Trigger asynchronies include, e.g., ineffective expiratory efforts (IEE) 1002 (also sometimes called ineffective triggers, missed triggers, failed triggers or wasted efforts), autotriggering 1004, and double triggering 1006 (also sometimes called breath-stacking).

[0230] IEE is the most frequently occurring type of PVA during invasive ventilation, both during early course and prolonged mechanical ventilation. IEE occurs, e.g., when, during the ventilator’s expiratory’ phase, the patient makes an inspiratory effort that is not followed, or sufficiently quickly followed by (that is, does not trigger) a ventilator breath. Airway pressure 1016 and flow 1018 waveforms provide example waveforms that may be associated with IEE. IEES typically result in an upward concavity 1017 in the pressure waveform 1016, and a downward concavity 1019 in the flow waveform 1018. In a clinical setting, esophageal pressure (Pes) and / or electrical activity of the diaphragm (EaDi) can be used to show inspiratory effort from the patient without the ventilator responding with support, thus indicating an IEE. Trigger delay asynchronies (not shown in FIG. 9) are a type of PVA that is similar to IEE. In a trigger delay asynchrony, the patient inspiratory effort does trigger a ventilator breath, but there is considerable delay between the respiratory muscle activation and the initiation of the ventilator breath.

[0231] In connection with various potential IEE mitigation measures, it is first noted that, generally, ventilators use. e.g., a detected drop in airway pressure (e.g., beyond a specified threshold) and / or a detected increase in flow / flow signal (e.g., beyond a specified threshold) to trigger a ventilator breath. As such, increasing trigger sensitivity may include reducing the associated specified threshold(s), so that less of a drop in pressure and / or less of an increase in flow is required for triggering), and decreasing trigger sensitivity generally includes increasing the associated specified threshold(s), so that more of a drop in pressure and / or more of an increase in flow is required for triggering. Additionally, since flow triggering is typically more sensitive than pressure triggering, in some cases, changing from pressure to flow triggering may be used to increase trigger sensitivity and, conversely, changing from flow to pressure triggering may be used to decrease trigger sensitivity. IEES and trigger delay asynchronies can result from a variety of causes, including, for example: inadequate trigger sensitivity (e.g., greater trigger sensitivity may be required when patient inspiratory effort is weak, or the detected drop in pressure or flow is reduced by other conditions); excessive inspiratory assistance (e.g.. which can lead to less of a drop in pressure or less of an increase in flow caused by patient inspiratory effort), air trapping / dynamic hyperinflation (auto-PEEP) (e.g., which can reduce the detected drop in pressure relative to the PEEP level or less of an increase in flow), weak patient inspiratory effort (e.g., which can cause less of a drop in pressure or less of an increase in flow) and excessive sedation levels (e.g.. which can reduce patient neural drive and consequent patient inspiratory effort, resulting in less of a drop in pressure or less of an increase in flow). In various circumstances, appropriate mitigation measures may include, for example, increasing trigger sensitivity (e.g., to detect IEE based on smaller drops in pressure or flow), decreasing inspiratory time (since too long an inspiratory time may cause auto-PEEP, which can cause IEE), reducing pressure support (which may reduce / resolve auto-PEEP) applying additional baseline airway pressure (BAP) (which may reduce / resolve auto-PEEP), and / or reducing or discontinuing sedatives and / or neural drive depressants (to result in greater patient neural drive and inspiratory effort).

[0232] FIGs. 20. 22-26 and 28 illustrate example flow diagrams 1000-1600 relating to PVAM determination of output (e.g., PVA mitigation notifications and / or adjustments) for various types of detected PVA. In particular, FIG. 20 relates to IEE (and, in some embodiments, trigger delay asynchronies).

[0233] In FIG. 20, at step 1104, IEE is detected (e.g., whether by the PVAM or otherwise). As described previously, in some embodiments, the detected type of PVA (here,IEE) is input to the PVAM (although, in some embodiments, the type of PVA may be at least in part detected by the system or the PVAM). Additionally, in some embodiments, the detected presence or determined lack of presence of the type of PVA is continuously or periodically monitored and updated, and associated updates may be input to (or determined by) the PVAM.

[0234] Various of the remaining blocks of FIG. 20 (as well as various blocks of FIGs. 22-26 and 28) relate to portions or components of PVA mitigation notifications (e.g.. messages) that may, e.g., be presented to a user in relation to the detected type of PVA, whether together, separately, or in groups. As described further below, block 1122 then provides an example of an integrated PVA root cause and / or mitigation message with a number of message components (which may alternatively represent or be described as an integrated set of messages). It is noted that the described messages may be in addition to other notifications, such as, e.g., alarms.

[0235] Furthermore, while FIGs. 20, 22-26 and 28 specifically describe PVA root cause and / or mitigation notifications, in some embodiments, similar methods may be used wi th regard to determined and implemented PVA adjustments. For example, in FIGs. 20,22- 26 and 28, many of the message components relate to the root cause of the PVA, and a recommended (e.g., suggested, indicated, advised, etc.) adjustment (e.g., an increase or decrease) of one or more ventilatory parameters. However, in some embodiments, such adjustments may be determined and implemented, e.g., by the PVAM, with or without associated notifications to a user, or with or without required user confirmation or other interaction.

[0236] Additionally, in various embodiments, adjustments may be recommended (or determined and implemented) in various ways. For example, in some embodiments, a specific amount (or range of possible or recommended amounts) may be included (or implemented). However, as described above, in some embodiments (whether with regard to notifications or adjustments to be implemented) user interaction may be used, or prompted for and used, such as to select, specify or input a particular adjustment, or to cause or direct implementation of an adjustment. Additionally, in some embodiments, rather than an adjustment amount (e g., to a parameter) being determined or presented, a parameter incorporating the adjustment amount (i.e., an adjusted parameter) may be determined or presented.

[0237] Furthermore, in FIGs. 20, 22-26 and 28, examples of specific message content and formatting are provided. However, in various embodiments, various different messagecontent and formatting may be used (e.g., different text, wording or sentences, different forms of presentation of adjustment amounts, different combinations of message components, etc.). It will be appreciated that only some of the steps described in FIGs. 20, 22-26 and 28 may be implemented in some examples.

[0238] In FIG. 20, at step 1124, based at least in part on the detected type of PVA (IEE), the PVAM determines message components to include a recommendation to increase trigger sensitivity (e.g., by 0.3-2.5 cm H2O or 0.5-2 cm H2O) and to consult a physician (although, in some embodiments, notifications to consult a physician may be omitted, or modified, such as, e.g., to consult or contact a health care professional or medical professional, or other variations). Additionally, in some embodiments, an amount (or, e.g., quantity, magnitude, value, increment, percentage, etc.) of the recommended trigger sensitivity increase may be included. Furthermore, in some embodiments, as represented by block 1142, the amount of the recommended trigger sensitivity increase may be determined, e.g., based at least in part on a current or most recent determined measure of PVA severity (e.g., the determined PVA severity score for the last breath, or the last breath for which a measure of PVA severity was determined). As a simple example, a particular recommended increase or decrease (here, an increase in trigger sensitivity) may be smaller for a smaller measure of PVA severity, and larger for a larger measure of PVA severity. However, as described herein, in some embodiments, adjustments (e.g., increases or decreases to a particular parameter) may be optimally determined using one or more algorithmic, mathematical, machine learning, artificial intelligence, or neural network related models or methods.

[0239] As described herein, in some embodiments, the measure of PVA severity may be determined based in part on the detected type of PVA, as well as other factors. Furthermore, in some embodiments, additional measure of PVA severity related data may be used (e.g., at step 1124). For example, in some embodiments, PVA time-based waveform or forecasting data may be used, such as to determine likely future trending. Additionally, in some embodiments, of one or more algorithmic, mathematical, machine learning, artificial intelligence, or neural network related models or methods, which may utilize various types of input, which may include training data, may be used in connection with, or to obtain, the input measure of PVA severity' related data, which may be used in determining or recommending adjustments (e.g., specific amounts of increases or decreases), such as based at least in part on measure of PVA severity related data.

[0240] The increase in trigger sensitivity, as show n in block 1124, may mitigate IEE,since weak patient efforts, or other factors causing a lower detected drop in pressure or smaller increase in flow, may require increased trigger sensitivity in order to detect patient effort to make an inspiration, to cause the triggering of the ventilator breath.

[0241] The method 1000 then proceeds to steps 1114-1120, at which the PVAM applies other logic in connection with the determination of other message components 1126- 1130. As represented by block 1140, in some embodiments, similar to use of block 1142 in connection with block 1124. as descnbed above, various notification or message components, such as amounts of adjustments (e.g., as shown in blocks 1126-1130, specific increases in PEEP / BAP setting (such as, e.g., by 0.8-7 cm H20 or 1-5 cm H20), increase in expiratory time, decrease in inspiratory time, decrease in respiratory rate, decrease in sedation, or reduction in tidal volume (Vt)) may be based at least in part on an input measure of PVA severity 1140, and / or associated data (as described above). In some embodiments, suggested increases or decreases in respiratory rate may be, e.g., by 1-7 breaths per minute (BPM) or 1- 5 BPM. In some embodiments, suggested increases or decreases in sedation may be in relation to the manner in which sedation is measured or characterized (e.g.. Ramsay Sedation Scale, Richmond Agitation Sedation Scale (RASS). or Riker Sedation-Agitation Score (SAS)), such as an increase or decrease selected to achieve a particular value or range according to a scale being used.

[0242] Specifically, if auto-PEEP is detected 1114, then message components are determined that include one or more of, e.g.: apply additional PEEP (e.g., with or without a specified amount included), increase expiratory time (with or without a specified amount included), decrease inspiratory' time (e.g., with or without a specified amount included), and decrease respiratory rate (e.g., with or without a specified amount included). In some embodiments, increases or decreases in expiratory time may be, e.g., by 0.03-0.7 s or 0.05- 0.5 s, or by 1.5-20% or 2-15% of expiratory time. As described above, in some embodiments, specific amounts, e.g., of increases or decreases, may be included, which, in some embodiments, may be based at least in part on measure of PVA severity' data. Auto-PEEP may be caused by the progressive accumulation of air (air trapping), such as due to incomplete expiration prior to the initiation of the next breath. Auto-PEEP can occur, for example, when expiration is limited by airway narrowing or obstruction, or when expiratory time is limited. Auto-PEEP may make IEE more likely because, in order for a patient to trigger a breath, the patient may have to generate inspiratory' muscle-based pressure equal to the auto-PEEP plus the trigger threshold. That is because, the trigger threshold may be set to a specified pressure decrease below the set PEEP, to reach the trigger threshold, auto-PEEPmust be overcome to reach the set PEEP, in addition to reaching the pressure decrease threshold below the set PEEP).

[0243] At step 1116, if the PVAM determines that patient inspiratory muscle effort is less than a specified (or determined and specified) threshold (such as, e.g., a power of breathing threshold, such as, e.g., 0.8-12 J / min or 1-10 J / min), and if, at step 1118, if it is determined that measured respiratory rate is less than the set respiratory rate, then the PVAM determines that message components should include, as shown in block 1128: decrease sedation (e g., with or without a specified amount included). As described above, sedation can reduce neural drive, leading to less patient effort, which can lead to IEE. Therefore, decreasing sedation may increase neural drive, leading to greater patient effort, and may thereby mitigate IEE.

[0244] Blocks 1106-1108 provide input into block 1110, which in turn provides input to step 1120 (and, in some embodiments, may also be used as input to other blocks, or in other ways). Specifically, in the embodiment illustrated, patient sex 1106 and height 1108 is used in determining a patient predicted body weight (PBW) 1112 (which may not be required to correspond with the actual body weight of the patient). The PBW (or actual body weight in other examples) may be used in determining the appropriate tidal volume (Vt) setting, which may in turn be compared with the measured Vt 1110, which are inputs to step 1120. At step 1120, if the PVAM determines that measured Vt is greater than 8 ml / kg (or, e.g., 6 to 10 ml / kg) of predicted body weight, then the PVAM determines that message components should include, as shown in block 1 130: reduce pressure support to deliver a tidal volume (Vt) between a minimum value (e.g., 4, 5, or 6 ml / kg of predicted body weight) and a maximum value (e.g., 8, 9, or 10 ml / kg of predicted bodyw eight (in some embodiments, a specific tidal volume, or range thereof, is indicated, which, as previously described generally, may be based at least in part on measured of PVA severity data).At step 1132, some or all of the various determined message components (e.g., at steps 1124, 1126, 1128 and 1130) may be combined (e.g., integrated, concatenated, merged or condensed, grouped and presented together, etc.), and the detected type of PVA (here, "‘Ineffective Efforts During Expiration7’) may also be included, such as in a header or top portion of the combined message 1122, which also includes some or all the various message components determined at steps 1124-1130. In various embodiments, however, notification or message components may be combined in various ways, or particular message components (which message components may. in some cases, constitute messages) may be presented separately or in groups, for example.

[0245] Additionally, alarms or alarm components may be included and presented (e.g., visual or lighting alarms, audio alarms, tactile alarms, alarm notifications, alarm messages). In some embodiments, one or more criteria or algorithms may be used in determining which message components to include in the combined message 1122, and in what priority or order to display them. Furthermore, in some embodiments, various message components may be displayed in different ways, e.g., font, color, etc., such as based on importance, urgency or priority, for example. Additionally, in some embodiments, display aspects may be included, e.g., to draw attention or indicate urgency relative to relevant parameters or parameter values (e.g., which may be suggested to be adjusted to mitigate an asynchrony), which may include, e.g., a flashing or otherwise visually highlighted parameter or parameter value.

[0246] FIG. 21 includes plots illustrating an example of determining a category of PVA severity based on determined waveform deviation, for IEE. As described previously (e.g., with reference to FIGs. 11 A-B), in some embodiments, an amount or area of a determined waveform deviation (between a sample synchronous breath and an asynchronous patient breath) may be used in determined a magnitude of PVA severity for the patient breath, where greater deviations are associated with greater magnitude of PVA severity. As also described herein (e.g. with reference to FIG. 15), in some embodiments, categories of PVA severity (e.g., mild, moderate or severe) may be used in connection with specified ranges of the measure of PVA severity. In FIG. 21, set of curves 1050 and set of curves 1051 provide examples of categories of PVA severity for IEE, based on patient airway pressure, in cm FEO (in set of curves 1050), and based on patient airway flow, in L / min (in set of curves 1051). With IEE, the patient airway pressure may decrease for a portion of time during the breath, resulting in a dip or dropped area relative to a sample synchronous breath, while the patient airway flow waveform may increase for a portion of time during the breath, resulting in a raised area relative to the sample synchronous breath.

[0247] In set of curves 1050, curve 1052 shows a synchronous patient breath (with no IEE), which, in this example, may be used as a sample synchronous breath for purposes of calculating deviations relative to asynchronous patient breaths. Curve 1054 shows a patient breath including IEE where the area of deviation 1053 (of 0. 1 cm H2O * second) falls into a range associated with the mild IEE category, curve 1056 shows a patient breath including IEE where the area of deviation 1055 (of 0.4 cm H2O * second) falls into a range associated with the moderate IEE category, and curve 1058 shows a patient breath including IEE where the area of deviation 1057 (of 1.3 cm H2O * second) falls into a range associated with thesevere IEE category. As would be expected, it can be seen that area of deviation 1053, associated with the mild IEE category, is smaller than area of deviation 1055, associated with the moderate IEE category, which is in turn smaller than area of deviation 1057, associated with the severe IEE category. In various embodiments, the mild IEE category can include deviations in airway pressure, in cm H2O * second, of, e.g., 0.08-0.12 or 0.01-1.00, the moderate category of IEE can include deviations in airway pressure, in cm H2O * second, of, e.g., 0.3-0.5 or 0.1-2.0. and the severe category of IF can include deviations in airway pressure, in cm H2O * second, of, e.g., at or above 1.3 or 0.5-10.0.

[0248] In set of curves 1051, curve 1066 shows a synchronous patient breath, with no IEE, which, in this example, may be used as a sample synchronous breath for purposes of calculating deviations relative to asynchronous patient breaths. Curve 1067 shows a patient breath including IEE where the area of deviation 1060 (of 0.4 L / min * second) falls into a range associated with the mild IEE category', curve 1068 shows a patient breath including IEE where the area of deviation 1062 (of 1.4 L / min * second) falls into a range associated with the moderate IEE category, and curve 1069 shows a patient breath including IEE where the area of deviation 1064 (of 3.8 L / min * second) falls into a range associated with the severe IEE category. As would be expected, it can be seen that area of deviation 1060, associated with the mild IEE category', is smaller than area of deviation 1062, associated with the moderate IEE category, which is in turn smaller than area of deviation 1064, which is associated with the severe IEE category. In various embodiments, the mild IEE category can include deviations in airway flow, in L / min * second, of, e g., 0.3-0.5 or 0.1 -2.0, the moderate category of IEE can include deviations in airway pressure, in L / min * second, of, e.g., 0.1.3- 1.5 or 0.3-10.0, and the severe category of IF can include deviations in airw ay pressure, in L / min * second, of, e.g., at or above 3.8 or 1.0-20.0.

[0249] Returning to FIG. 19, another type of trigger asynchrony is autotriggering (AT) 1004. AT occurs, e.g., when the ventilator delivers an assisted breath that is not initiated (e.g., triggered) by the patient. For example, patient circuit or system leaks or condensate (which can cause drops in detected airway pressure or increases in flow) or patient cardiac oscillations (which can lead to transmission of airway pressure and / or flow oscillations) can cause detected / signaled pressure or flow- to drop below a trigger threshold, thus triggering a breath that is not initiated by the patient’s efforts. Additionally, too sensitive of a trigger threshold can lead to AT. Airw ay pressure 1020 and flow 1022 waveforms provide example waveforms associated with AT. The lower peak 1021 in the flow waveform 1022 is associated with an autotriggered breath. AT is more common in patients with low respiratory'drive and breathing frequency and has been found to be the most common type of asynchrony during noninvasive ventilation. AT can cause repeated / extra breaths and can cause hyperventilation.

[0250] AT mitigation measures may include, e.g., reducing trigger sensitivity, fixing / removing a patient circuit or system leak, or removing condensate in the patient circuit or system.

[0251] FIG. 22 is a flow diagram 1100 relating to PVAM determination of output (e.g., PVA mitigation notifications and / or adjustments) for detected AT. At step 1204, AT is detected (e.g., whether by the PVAM or otherwise). At step 1210, the PVAM determines message components to include a recommendation to decrease trigger sensitivity (e.g., with or without a specified amount included) and to consult a physician. In some embodiments, as represented by block 1240, the amount of the recommended trigger sensitivity increase may be determined based at least in part on a current or most recent determined measure of PVA severity, or related data. Generally, decreasing the trigger sensitivity may reduce risk of AT from drops in detected airway pressure or flow not caused by patient efforts (e.g., caused in leaks or condensate, or patient cardiac oscillations).

[0252] At step 1206, the PVAM determines whether a system (which can include patient circuit) leak is detected, such as by determining whether the inspiratory volume (VI) minus the expiratory volume (VE) is greater than a specified or determined threshold (such as, e.g., 8-240 ml, 10-200 ml, 0.8-60% of VI, 1-50% of VI). If so, then, at step 1212. the PVAM determines message components to include: check for loose circuit connections, check exhalation valve, check tube placement / cuff (endotracheal cuff), and replace patient circuit.

[0253] Additionally or alternatively, at step 1208, the PVAM determines whether condensate is detected in the ventilator / patient circuit. If so, then, at step 1214, the PVAM determines a message component of: remove condensate from ventilator circuit.

[0254] While not shown in FIG. 22, the PVAM may, e.g., combine some or all of the message components that may have been determined at steps 1210, 1212 and 1214 (or, e.g., select some and combine, prioritize, etc.) and present them as a combined message to a user, which combined message may include an indication, such as a top line or header, indicating that autotriggering has been detected.

[0255] Returning to FIG. 19. another type of trigger asynchrony is double triggering (DT) 1006. DT occurs when, e.g., the ventilator's inspiratory time is shorter than the patient’s inspiratory time, and patient effort of a first breath triggers a second ventilator breath. Inassist-control ventilation, DT has been found to result in a nearly doubling of the delivered tidal volume, which can result in hyperventilation and lung injury. DT is often a result of high respiratory drive. During pressure support ventilation, DT often occurs in patients with low respiratory system compliance, especially if the expiratory trigger is set too low.

[0256] Causes of DT include, e.g., ventilator inspiratory time is too short relative to neural inspiratory’ time (which can cause patient inspiratory effort to continue after cycling is triggered, which can trigger a double / incorrectly triggered breath), and. in volume controlled ventilation, that the tidal volume is too low (which can also lead to patient inspiratory effort continuing after cycling, which can trigger a double / incorrectly triggered breath).

[0257] Mitigation measures for DT may include, e.g., an increase in inspiratory' time (in volume controlled and pressure controlled ventilation), which can reduce the chance of a patient’s inspiratory effort continuing after cycling, a decrease in flow cycling threshold percentage (in pressure support ventilation) (e.g., by 3-25% or 5-20%), which can lead to later cycling and thereby reduce the risk of the patient’s inspiratory effort continuing after cycling, an increase in sedation / neuromuscular blockages in early severe acute respiratory distress syndrome (ARDS), which can reduce patient inspiratory effort, and thereby reduce the risk of double triggering, or changing to a mode that allows variation in tidal volume (e.g., pressure controlled ventilation), which can reduce the risk of a mismatch between the patient’s inspiratory time and the ventilator inspiratory time.

[0258] Airway pressure 1040 and flow 1042 waveforms provide example waveforms associated with DT. In DT, the patient may still be inhaling while the ventilator cycles the first breath, so that the continuing patient effort may cause the airway pressure to drop below a cycling trigger threshold and trigger a second (double triggered) breath. In the airway pressure 1040 and flow 1042 waveforms, the second peak in each set of two peaks (e.g., peak 1041) indicates a second (double triggered) breath, with the first peak representing a short cycled first breath.

[0259] FIG. 23 is a flow diagram 1200 relating to PVAM determination of output (e.g., PVA mitigation notifications and / or adjustments) for detected DT. At step 1702, DT is detected (e.g., whether by the PVAM or otherwise). At step 1712, the PVAM determines message components to include a recommendation to increase inspiratory time (e.g., with or without a specified amount included) and to consult a physician. In some embodiments, as represented by block 1742, the amount of the recommended inspiratory time increase may be determined based at least in part on a current or most recent determined measure of PVA severity, or related data.

[0260] If, at step 1704, the PVA determines that the targeting scheme is volume (volume controlled), then, at step 1706, the PVA determines whether the tidal volume (Vt) is less than a determined or specified threshold (such as, e.g., 1.6-8 ml / kg * PBW or 2-6 ml / kg * PBW). If so, then, at step 1714, the PVAM determines PVA mitigation message components to include: increase sedation (e.g., with or without a specified amount included) or neuromuscular blockages (e.g., with or without a specified amount included), and change to a pressure targeting scheme. In some embodiments, as represented by block 1740, the amount of the recommended increase in sedation or neuromuscular blockages may be determined based at least in part on a current or most recent determined measure of PVA severity, or related data. It is noted, however, that, in some embodiments, whether the targeting scheme is, e.g.. volume (volume controlled) or pressure / pressure support (e.g., see FIGs. 12, 15 and 16) may not require a determination by the system; rather, the targeting scheme (e.g., volume / volume controlled or pressure / pressure support) may be a constant or assumed parameter under specific circumstances and may therefore not require a determination. In such embodiments, while boxes, such as boxes 1606 and 1608 may be included, these may or may not require an actual determination or active step by the system (e.g., they may represent determinations or just represent flow based decision boxes)

[0261] If, at step 1708, the PVAM determines that the ventilation mode is pressure support, then, at step 1710, the PVAM determines whether the patient’s respiratory compliance (Crs) is less than a specified or determined threshold (DT is more common in patients with low Crs). If so, then, at step 1716, the PVAM determines a message component to include: decrease the flow cycling threshold percentage (e.g., with or without a specified amount included). In some embodiments, as represented by block 1740, the amount of the recommended decrease in the flow cycling threshold percentage may be determined based at least in part on a cunent or most recent determined measure of PVA severity, or related data.

[0262] While not shown in FIG. 23, the PVAM may, e.g., combine (or, e.g., select some and combine, prioritize, etc.) the message components that may have been determined at steps 1712,1714 and 1716, and present them as a combined message to a user, which combined message may include an indication, such as a top line or header, indicating that double triggering has been detected.

[0263] Returning to FIG. 19, cycling asynchronies include prolonged cycling (PC) 1008 (sometimes also called late cycling, delayed cycling or delayed termination) and short cycling (SC) 1010. PC occurs when, e.g., the ventilator’s inspiratory time exceeds the patient’s (neural) inspiratory time. PC may therefore be considered the opposite of SC. PC ismost common in patients with high lung resistance (Rl) and normal or high lung compliance (Cl), such as in patient with chronic obstructive pulmonary disease (COPD) and asthma (high lung resistance may cause a slower decrease in flow and therefore later cycling). PC is frequently observed during non-invasive pressure support ventilation because air leaks prevent the ventilator from cycling from inspiration to expiration (if the leak exceeds the set flow cycling threshold). PC may be caused by inappropriate cycling settings on the ventilator, such as the ventilator inspiratory time being set too high relative to patient’s neural inspiratory time (which can cause a delay in cycling) or the flow cycle threshold set too low (e.g., requiring too much time for flow to decrease to the set threshold), air leaks in the circuit (which can prevent the ventilator from cycling from inspiration to expiration if the leak exceeds the set flow cycling threshold)), or patient obstructive respiratory mechanics (e.g.. COPD or asthma).

[0264] PC mitigation measures may include, e.g., one or more of: a decrease in ventilator inspiratory time (e.g., so that it does not exceed the patient's inspiratory7time), an increase in the flow cycle threshold, a decrease in pressure support (excessive pressure support may result in a longer time before cycling occurs), and checking for and resolving system (e.g., circuit) leaks.

[0265] Airway pressure 1024 and flow 1026 waveforms provide example waveforms associated with PC. As can be seen in the pressure waveform 1024. after patient (neural) inspiration ends, pressure continues to rise, as shown by feature 1050, as ventilator inspiration continues, while, as can be seen in the flow waveform 1 26, flow slowly decreases, as shown by feature 1052, until the expiratory trigger is reached.

[0266] FIG. 24 is a flow diagram 1300 relating to PVAM determination of output (e.g., PVA mitigation notifications and / or adjustments) for detected PC. At step 1302, PC is detected (e.g., whether by the PVAM or otherwise). At step 1306, the PVAM determines PVA mitigation message components to decrease ventilator inspiratory7time (e.g., with or without a specified amount included), increase flow cycle threshold (e.g., with or without a specified amount included), and consult physician. In some embodiments, as represented by block 1340, the increment of the recommended decrease in ventilator inspiratory time and the increase in flow cycle threshold may be determined based at least in part on a current or most recent determined measure of PVA severity7, or related data.

[0267] If, at step 1304, the PVAM determines that there is a system leak, such as bydetermining that inspiratory volume (VI) minus expiratory volume (VE) is greater than a specified or determined threshold, then, at step 1308, the PVAM determines PVA root causeand mitigation message components to include one or more of: check for loose circuit connections, check exhalation valve, check (endotracheal) tube placement / cuff, and replace circuit.

[0268] If, at step 1305, the PVAM determines that patient obstructive respirator}7mechanics are present, such as by determining that respiratory7resistance is greater than a specified or determined threshold (such as, e.g., 2-30 cm H2O or 3-25 cm H2O), then, at step 1310, the PVAM determines message components to include one or more of: increase flow cycling threshold (e g., by 3-25% or 5-20%), decrease pressure support (such as, e.g., by 0.8- 7 cm H20 or 1-5 cm H2O), and decrease rise time (such as, e.g., if a dimensionless number between 1 (fastest) and 10 (slowest), by 1-3 or 1-2, or, if in ms, by 8-120 ms or 10-100ms, or by 0.8-25% of inspiratory time or 1-20% of inspiratory time) (since reaching peak pressure or flow faster leads to a decrease in inspiratory time). In some embodiments, as represented by block 1342, the amounts of the recommended increase in flow cycling threshold, decrease in pressure support, and decrease in rise time may be determined based at least in part on a current or most recent determined measure of PVA severity, or related data.

[0269] While not shown in FIG. 24. the PVAM may, e.g.. combine (or. e.g., select and combine, prioritize, etc.) some or all of the message components that may have been determined at steps 1306, 1308 and 1310, and present them as a combined message to a user, which combined message may include an indication, such as a top line or header, indicating that prolonged cycling has been detected.

[0270] Returning to FIG. 19, another cycling asynchrony is short cycling (SC) 1010 (also sometimes called premature cycling or premature termination). SC occurs when, e.g., the patient's inspiratory7time is greater than the ventilator’s inspiratory time. In SC, the ventilator ends flow delivery while the patient’s inspiratory effort continues. SC often causes the patient to experience ‘‘air hunger” or “air starvation.” Short cycling can often result in a double-triggering asynchrony if the patient’s effort exceeds the trigger threshold and therefore triggers another breath.

[0271] Causes for SC may include that the ventilator inspiratory7time is set too short relative to the patient’s (neural) inspiratory time, the flow cycle threshold is set too high, and restrictive respiratory' mechanics (e.g., pulmonary fibrosis).

[0272] SC mitigation measures may include, e.g., one or more of: an increase in ventilator inspiratory time, a decrease in the flow cycling threshold, and an increase in pressure support (higher pressure support may increase inspiratory time).

[0273] Airway pressure 1028 and flow 1030 waveforms provide example waveformsassociated with SC. In the expiratory flow waveform 1030, SC may start with a negative peak, as shown by feature 1056, which rapidly returns towards zero flow, while the pressure waveform 1028 rapidly depresses toward the PEEP setting, as shown by feature 1054, due to active patient inspiratory' muscle effort depressurizing the system.

[0274] FIG. 25 is a flow diagram 1400 relating to PVAM determination of output (e.g., PVA mitigation notifications and / or adjustments) for detected SC. At step 1404, SC is detected (e.g., whether by the PVAM or otherwise). At step 1408, the PVAM determines PVA mitigation message components to include one or more of: increase ventilator inspiratory time (e.g., with or without a specified amount included), decrease flow' cycle threshold (e.g., with or without a specified amount included), and consult physician. In some embodiments, as represented by block 1442, the amount of the recommended decrease in ventilator inspiratory time and the increase in flow cycle may be determined based at least in part on a current or most recent determined measure of PVA severity, or related data.

[0275] If, at step 1406, the PVAM determines that patient restrictive respiratory mechanics are present, such as by determining that respiratory’ compliance (Crs) is less than a specified or determined threshold (such as, e.g.. 40-120 ml / cm H2O or 50-100 ml / cm H2O), then, at step 1410, the PVAM determines PVA mitigation message components to include: increase pressure support (such as, e.g., by 0.8-7 cm H2O or 1-5 cm H2O). In some embodiments, as represented by block 1440, the amount of the recommended increase in pressure support may’ be determined based at least in part on a current or most recent determined measure of PVA severity, or related data. In some embodiments, detecting restrictive or obstructive patient respiratory' mechanics may utilize measured patient respiratory compliance or respiratory' resistance. However, in some embodiments, such patient conditions may be detected in other ways, such as, e.g., by use of pulmonary function testing (PFT) and / or via manual input by a user.

[0276] Returning to FIG. 19, one type of flow' related asynchrony is insufficient flow (IF) 1012. IF occurs, e.g., when the ventilator's peak or average inspiratory flow is below' the patient's desired peak or average flow rate. Inspiratory flow mismatch may manifest as either a convex or concave inspiratory airway pressure waveform. Airway pressure 1062 and flow 1064 w aveform plots provide example waveforms to illustrate IF. At feature 1068 in the pressure curve associated with IF (solid curve), it can be seen that the pressure substantially drops as a result of the patient's inspiratory effort associated with IF. In feature 1066 in the pressure waveform without IF (dotted curve), it can be seen that the pressure does not substantially drop. With severe IF, the pressure waveform during inspiration can even dropbelow the baseline airway pressure. IF can result in an excessive load being placed on the respiratory muscles, and can result in lung injury due to excessive tidal volume, and regional lung overdistension.

[0277] IF can be caused by, in volume controlled ventilation, too low a flow setting (so that not enough flow is providing during patient inspiration); in pressure controlled / support ventilation, too low of an applied pressure or too slow a rise time (which can result in not enough flow being providing during patient inspiration); or excessive ventilatory demand / high neural drive (which can lead to the ventilator not providing sufficient flow to meet the high demand).

[0278] IF mitigation measures may include, e.g., one or more of: in volume controlled ventilation, an increase in inspiratory flow (to meet the patient’s inspiratory flow demand); in pressure controlled / support ventilation, an increase in applied pressure or an decrease in rise time (to lead to greater flow to meet the patient’s inspiratory flow demand), and reduction of metabolic demand and neural drive (to reduce the patient's inspiratory7flow demand), e.g., by the user attempting to calm the patient such as by instructing the patient to relax as much as possible.

[0279] FIG. 26 is a flow diagram 1500 relating to PVAM determination of output (e.g., PVA root cause and mitigation notifications and / or adjustments) for detected IF. At step 1502, IF is detected (e.g., whether by the PVAM or otherwise). At step 1518, the PVAM determines a PVA mitigation message component to consult a physician.

[0280] At step 1506, if the PVAM determines that the targeting scheme is volume controlled, then, at step 1512, the PVAM determines whether the inspiratory flow setting is less than a specified threshold (such as, e.g.. 8-50 1 / min or 10-40 1 / min), and, if so, then, at step 1520, the PVAM determines a message component to include: increase inspiratory flow (such as, e.g., by 0.8-25 1 / min or 1-20 1 / min, or by 3-50% or 5-40% of the inspiratory flow rate). In some embodiments, as represented by block 1540, the amount of the recommended increase in inspiratory7flow may be determined based at least in part on a current or most recent determined measure of PVA severity, or related data.

[0281] At step 1508. if the PVAM determines that the targeting scheme is pressure controlled / support, then, at step 1514, the PVAM determines whether the pressure setting (e.g., peak inspiratory pressure (PIP) or pressure support (PS) setting in pressure support ventilation mode) is less than a specified threshold (such as, e.g., PIP of 8-25 cm H2O or 10- 20 cm H2O, or a PS of 3-20 cm H2O or 5-15 cm H2O). and, if so, then at step 1522 the PVAM determines a message component to include: increase pressure. At step 1516, thePVAM determines whether the pressure rise time is greater than a specified threshold (or in some embodiments, e.g.. whether a specified patient pressure waveform characteristic or feature is present or reaches a specified threshold), such as, e.g., when the setting is to a dimensionless number from 1 (fastest) to 10 (slowest), ranges of, e.g., 2-8 or 3-7, or, when the setting is in ms (which is sometimes called "P-ramp" and may range from, e.g., 0-2000 ms), ranges of, e.g., 40-250 ms, 50-200 ms, 4-25% of inspiratory time, or 5-20% of inspiratory time. It is noted that, in some instances, rise time may be dependent on the patient’s airway resistance, where adult patients may have high resistance and may require shorter rise times, such as, e.g., 2-5 or 3-4 (on the dimensionless scale), while patients with lower airway resistance and / or young / infant patients may benefit from longer rise times, such as, e.g., 7-10 or 8-10). In some examples, the PVAM may be configured to receive information indicative of, or otherwise determine, whether the patient is adult or young for determining said rise times. If so, then, at step 1524, the PVAM determines a message component to include: decrease rise time. In some embodiments, as represented by block 1540, the amount, if any, of the recommended increase in pressure and / or decrease in rise time may be determined based at least in part on a current or most recent determined measure of PVA severity, or related data.

[0282] At step 1510, the PVAM determines whether excessive ventilatory demand and / or neural drive is detected (such as, e.g., power of breathing > a threshold, such as, e.g., 8-25 J / min or 10-20 J / min), and. if so then, at step 1526, the PVAM determines message components to include: reduce metabolic demand, and / or reduce neural drive (e g., via sedation and / or paralysis). In some embodiments, as represented by block 1540, the amount of the recommended reduction in metabolic demand and / or the amount of the reduction in neural drive may be determined based at least in part on a current or most recent determined measure of PVA severity, or related data.

[0283] FIG. 27 includes curves 3101-3104 illustrating an example of determining a category7of PVA severity based on determined waveform deviation for IF. As described previously (e.g., with reference to FIGs. 11A-B), in some embodiments, an amount or area of a determined waveform deviation (between a sample synchronous breath and an asynchronous patient breath) may be used in determined a magnitude of PVA severity for the patient breath, where greater deviations are associated with greater magnitude of PVA severity. As also described herein (e.g. with reference to FIG. 15), in some embodiments, categories of PVA severity (e.g.. mild, moderate or severe) may be used in connection with specified ranges of the measure of PVA severity. In FIG. 21, curves 3101-3104 provideexamples of categories of PVA severity for insufficient flow (IF), based on patient airway pressure (in cm H2O). With IEE. the patient airway pressure may decrease for a portion of time during the breath, resulting in a dip or dropped area relative to a sample synchronous breath.

[0284] Curve 3101 shows a synchronous patient breath (with no IF), which, in this example, may be used as a sample synchronous breath for purposes of calculating deviations relative to asynchronous patient breaths. Curve 3102 shows a patient breath including IF where the area of deviation 3105 (of 0.6 cm H2O * second) falls into a range associated with the mild IF category, curve 3103 shows a patient breath including IEE where the area of deviation 3106 (of 2.2 cm H2O * second) falls into a range associated with the moderate IEE category, and curve 3104 shows a patient breath including IEE where the area of deviation 3107 (of 7.9 cm H2O * second) falls into a range associated with the severe IEE category. As would be expected, it can be seen that area of deviation 3105, associated with the mild IEE category, is smaller than area of deviation 3106, associated with the moderate IEE category, which is in turn smaller than area of deviation 3107, which is associated with the severe IEE category. In vanous embodiments, the mild IF category can include deviations in airway pressure, in cm H2O * second, of, e.g., 0.4-0.8 or 0.1-5.0, the moderate category of IF can include deviations in airway pressure, in cm H2O * second, of, e.g., 2.0-2.4 or 1.0-10.0, and the severe category of IF can include deviations in airway pressure, in cm H2O * second, of, e.g., at or above 7.9, or 2.0-20.0.

[0285] Returning to FIG. 19, another type of flow related asynchrony is excessive flow (EF) 1014. EF occurs when the ventilator’s peak inspiratory flow rate is much greater than the patient’s flow demand. EF can be caused by, in volume controlled ventilation, a flowsetting that is too high, and, in pressure controlled / support ventilation, an applied pressure that is too high or a rise time that is too short. Mitigation measures for EF may include one or more of, in volume controlled ventilation, a decrease in the flow setting, and, in pressure controlled / support ventilation, a decrease in the applied pressure and an increase in the rise time.

[0286] Airway pressure 1070 and flow 1072 waveforms provide example waveforms to illustrate EF. In the pressure waveform 1070, feature 1074 represents a breath without EF, while feature 1076 represents a breath with EF. As can be seen, feature 1076 show s a greater concavity than feature 1074, which is associated with EF.

[0287] FIG. 28 is a flow diagram 1600 relating to PVAM determination of output (e.g., PVA mitigation notifications and / or adjustments) for detected EF. At step 1602, EF isdetected (e.g., whether by the PVAM or otherwise). At step 1618, the PVAM determines a PVA mitigation message component to consult a physician.

[0288] At step 1606, if the PVAM determines that the targeting scheme is volume controlled, then, at step 1612, the PVAM determines whether the inspiratory flow is greater than a specified threshold (such as, e.g., 25-75 1 / min or 30-60 1 / min), and, if so, then, at step 1620, the PVAM determines a message component to include: decrease inspiratory’ flow (such as, e.g., by 0.8-25 1 / min or 1-20 1 / min. or by 3-50% or 5-40% of the inspiratory flow rate, or by, for example, increasing the inspiratory time). In some embodiments, as represented by block 1640, the amount of the recommended increase in inspiratory' flow may be determined based at least in part on a current or most recent determined measure of PVA severity, or related data.

[0289] At step 1608, if the PVAM determines that the targeting scheme is pressure controlled / support, then, at step 1614, the PVAM determines whether the pressure setting (e.g., peak inspiratory' pressure (PIP) or pressure support (PS) setting in pressure support ventilation mode) is greater than a specified threshold (such as, e.g., PIP of 12-40 cm H2O or 15-30 cm H2O, or a PS of 8-30 cm H2O or 10-25 cm H2O), and. at step 1622. the PVAM determines a message component to include: decrease pressure. At step 1616, the PVAM determines whether the pressure rise time is less than a specified threshold (or in some embodiments, e.g.. whether a specified patient pressure waveform characteristic or feature is present or reaches a specified threshold), such as, e.g., when the setting is to a dimensionless number from 1 (fastest) to 10 (slowest), ranges of, e.g., 1-7 or 2-6, or, when the setting is in ms, ranges of, e.g., 15-200 ms, 20-150 ms, 1-20% of inspiratory' time, or 2-15% of inspiratory' time). If so, then at step 1624, the PVAM determines a message component to include: increase rise time (such as. e.g., if a dimensionless number as mentioned above, by 1-3 or 1- 2, or, if in ms, by 8-120 ms or 10-100ms, or by 0.8-25% of inspiratory time or 1-20% of inspiratory' time). In some embodiments, as represented by block 1640, the amount of the recommended decrease in pressure and / or increase in rise time may be determined based at least in part on a current or most recent determined measure of PVA severity, or related data.

[0290] At step 1610. the PVAM determines whether low ventilatory demand and / or neural drive is detected (such as, e.g., power of breathing < s threshold, such as, e g., 0.8-15 J / min or 1-10 J / min), and, if so then, at step 1626, the PVAM determines message components to include: induce metabolic demand, and / or increase neural drive. In some embodiments, as represented by block 1640, the amount of the recommended increase in metabolic demand and / or the amount of the increase in neural drive may be determined basedat least in part on a current or most recent determined measure of PVA severity, or related data.

[0291] As described with reference to various figures herein, in various embodiments, alarms may be presented to a user, such as via one or more displays / GUIs of one or more devices (e.g., ventilator, defibrillator / CCM, other medical device, computing device, or portable computing device). The alarms may include, e.g., low, medium and high priority alarms (e.g., based on urgency and / or risk presented to the patient), each of which may be associated with particular features and protocols. The alarms may be associated with various specific alarm details and protocols, and each of which may be associated with particular GUI features (e.g., a color such as red color for high priority alarms, yellow for medium and / or low priority alarms, etc.). In various embodiments, notifications (such as messages) can be or include alarms, such as, e.g., low priority alarms, or low priority alarms with certain variations or specific features. However, in some embodiments, PVA related alarms (or some of them, or PVA related alarms fitting certain criteria, such as may include the type of detected PVA or an associated PVA severity related data, such as a measure of PVA severity) may be, e.g.. medium or high priority alarms, or medium or high priority alarms with certain variations or specific features.

[0292] In some embodiments, PVA related alarms may be determined and / or provided, whether separately or integrated with other types of alarms, e.g., as based at least in part on, and / or upon detection of, detected types of PVA, and / or PVA severity related data, such as a measure of PVA severity, associated therewith. In some embodiments, an alarm may include guidance, such as user-interactive guidance, that may guide the user in, e.g., obtaining more information about or mitigating the detected ty pe of PVA. For example, in some embodiments, such guidance may include any of various messages described with reference to previous figures.

[0293] In some embodiments, PVA related alarms may include features such as a mute or cancel user option (e.g., by selection or pressing of a physical or virtual button or feature), such as to reduce potential nuisance or distraction to a user that may be created in some situations or for some users. For example, in some embodiments, a presented alarm may include a GUI-based user option to mute or suppress the PVA related alarm (e.g., a visual and / or audio alarm, with or without an associated textual message or recommendation), such as for a short period of time (e.g., 30 seconds or more or less) while the user may, e.g., make or confirm settings adjustments or changes to mitigate a detected ty pe of PVA, which may cause the alarm to no longer be active or presented. In someembodiments, multiple presses or selections of the button or feature (e.g., two rapidly consecutive presses) may implement other features, such as, e.g., longer or permanent muting, suppressing or clearing of the alarm (however, in some embodiments, such features are not provided). In some embodiments, particular alarm features may be configurable by, e.g., the user or another entity7, e.g., owner or manager of the device, etc.

[0294] As described further herein, PVA related alarms may include information about the detected type of PVA. which may include, for example, one or more of: the root cause of the PVA, the detected type of PVA, a measure of severity or other PVA severity related data, and recommendations or guidance. In some embodiments, recommendations, guidance, or further information may be provided, such as if the user interacts to request such (e.g., by pressing a button, making a GUI selection from a menu, etc.).

[0295] Furthermore, in some embodiments, a user may have the option of accessing additional information relating to a PVA related alarm by, e.g., a source outside of the device or system, such as, e.g., a non-electronic source, which may include, e.g.. printed sources such as laminated cards, for example, such as may be included with or near the ventilator. In some embodiments or circumstances, notifications, e.g., alarms, may direct a user to consult such sources, such as to obtain more detailed or other information.

[0296] In some embodiments, a ventilator, such as, e.g., portable ventilator 106 as shown in FIG. 1 (or a mechanical ventilation system), which may include a PVAM or aspects thereof, may be internally controlled, remote controlled (e.g.. by a local or non-local device, system or platform other than the portable ventilator 106), or may be capable of both, or of switching or being switched between them. In some embodiments, the portable ventilator 106 is capable of being remote controlled by or from, e.g., a defibrillator / CCM, medical or monitoring device, computing device, portable computing device, tablet, smartphone, system, facility, offsite medical care or monitoring facility, or hospital. Such remote control may include, e.g., control or adjustment of any of various ventilatory parameters, such as ventilator settings or ventilator operating parameters.

[0297] In some embodiments, whether internal or remote control is permitted or used may depend on whether a fault mode condition is determined to exist, or determined to exist at a specific time or during a specific time period. A fault mode may be or include, e.g., a fault or potential fault that may be associated with remote control or potential remote control of the ventilator. For example, a fault mode condition may include lack of, or insufficient, communication between the ventilator and the remote control device. Other fault mode conditions may relate, for example, to other conditions that may render potential remotecontrol problematic or unsafe, such as, e.g., one or more of: lack of authentication of the remote control device or associated communications, errors in communications, errors in communicated commands, or errors or unsafe values or ranges in communicated adjustments or changes to one or more ventilator settings or ventilator operating parameters.

[0298] In some embodiments, a controller of the ventilator (or ventilation system) may determine whether a fault mode condition exists relative to a device configured for remote control of the mechanical ventilation system. If no fault mode condition is found, then the controller may allow the remote control of the ventilator (or ventilation system) by the remote control device. However, if a fault mode condition is found, then the ventilator (or ventilation system) may control itself (e g., by using last received valid settings, or store backup setting, for example), and remote control may not be permitted.

[0299] FIG. 29 is a block diagram illustrating example software related aspects 300 that may be used in a ventilation system including a PVAM, which software aspects may include FIO2 control with ventilation and PEEP control including physiologic closed-loop control (PCLC). For example, a PVAM may be used in one or more aspects of PVA management that may be integrated with or into PCLC. and / or in providing PCLC that includes one or more aspects of PVA management, such as may include minimization of a measure of PVA severity, for example.

[0300] An initiation mode 302, a test breaths mode 304 and an active mode 306 are shown. During active mode, implemented using an active mode central control 308, the controller may cause delivery of ventilation to the patient, such as, for example, via a gas delivery apparatus and using a facemask, coupled with the patient, or intubation, using ventilation parameters that are continuously updated, which may include FIO2 and PEEP. In some embodiments, however, the particular exemplary modes of FIG. 17 may not be used.

[0301] In initiation mode 302, the user may be prompted to enter the patient’s sex and height, which may be used in determining a body weight associated with the patient, or “predicted” body weight (PBW). Based on the determined patient’s PBW, several ventilation parameter settings may be determined for use in test breaths mode 304.

[0302] In various embodiments, in initiation mode 302. various calculations may be utilized to determine test breaths parameter settings. In one example, in porcine use and studies, tidal volume (Vt) (in ml) may be calculated as 10 * actual weight in kg, minute volume (in ml / min) as actual weight in kg * 140, and respiratory rate (RR) as minute volume / Vt, where RR may be rounded to the next higher integer value. However, various other calculations and values may be used in various embodiments.

[0303] In another example, in human use, the following calculations may be utilized. The PBW (in kg) may be calculated as C + D * (height in cm - E), where C may be, e.g., between 35-60, D may be, e.g., between 0.80 and 1.00, and E may be, e.g., between 140-160, and where, in some embodiments, at least C may be slightly lower for a female than for a male. For Vt greater than or equal to, e.g., a value between 150 to 250 ml, rounding may be performed to, e.g. the nearest 1-10 ml. 3 to 7ml, and for Vt less than or equal to, e.g.. a value between 15 to 200 ml, rounding is performed to, e.g.. the nearest 0.5 to 5 ml. 0.5 to 3 ml. However, various other calculations and values may be used in various embodiments, such as various calculations that are based on, or functions of, height, gender and / or one or more other patient physical or health-related characteristics.

[0304] In some embodiments, some or all of the results of the calculations made in initiation mode 302 are displayed to the user. The user may, for example, be prompted to accept the determined parameters and then couple the patient to the ventilator.

[0305] In some embodiments, following completion of initiation mode 302, the system 300 proceeds to test breaths mode 304 (although, in some embodiments, test breaths mode 304 may be skipped / not included, and the system proceeds to active mode 306). In test breaths mode 304, the system 300 may deliver one or several ventilation test breaths to the patient, based on which the system 300 may determine one or more patient parameters and / or particular ventilator settings to utilize at the start of active mode 306. For example, in some embodiments, in the test breaths mode 304, a patient respiratory system compliance (Crs), such as an estimated patient Crs, is determined and used in determining a peak inspiratory pressure (PIP(0)) ventilator setting. In some embodiments, Crs may be calculated or estimated using patient respiratory dynamics data and the equation of motion for the respiratory system. Furthermore, in some embodiments, in test breaths mode 304, the following particular parameter settings may be utilized: inspiratory: expiratory ratio (I:E) = 1 :3, PEEP = 5 cm H2O, FIO2 = 0.5 (or 50%). However, in various embodiments, other initial settings may be used.

[0306] Following successful completion of the test breaths mode 304 and determination of the patient Crs. the system 300 may proceed to active mode 306. In some embodiments, the system may proceed to active mode 306 even if patient Crs cannot be determined. For example, in some embodiments, if Crs cannot be determined, a default Crs value may be utilized, such as a Crs value of 50-150 ml / cm H2O, 70-130 ml / cm H2O, 80-120 ml / cm H2O, 90-110 ml / cm H2O, for example. In some embodiments, the default value may be determined to be relatively high, since, in some embodiments, that will result in relativelysmall changes in a PIP correction value thus providing a relatively conservative approach.

[0307] In some embodiments, during active mode 306, the system 300 delivers continuously adjusted ventilation to the patient, which may include closed loop control of one or more patient and / or ventilation related parameters.

[0308] In some embodiments, during active mode 306, a number of ventilation parameters are continuously adjusted, including FIO2 and PEEP. Other parameters that are adjusted during ventilation mode 308 in a continuous manner may include one or more of: PIP, Vt, Ve, RR and I:E. In some embodiments, FIO2 is continuously adjusted based on a target patient oxygenation level, such as an SpO2 of 94%. In some embodiments, FIO2 starts at 21% (or, e g., 21-25%) or the current FIO2 setting. However, some embodiments, a user may select an initial FIO2 setting, such as between 21% to 100%. for example.

[0309] On a continuous basis, central control 308 may provide data to ventilation control 310, including current EtCO2 as well as pressure (P) and volume (V) waveforms, and / or parameters derived at least in part from the P and V waveforms. Ventilation control 310 may use that data, potentially in addition to other data, in determining values for one or more of: Vt, Ve. PIP, RR and I:E, which it then sends to central control 308. Central control 308 may then implement any appropriate adjustments to Vt, Ve, PIP, RR and I E settings based at least in part on the sent values.

[0310] Furthermore, on a continuous basis, central control 308 may provide data to FIO2 control 312, including current patient oxygen saturation (SpO2). FIO2 control 310 may use that data, potentially in addition to other data, in determining a value for FIO2, which it then sends to central control 308. Central control 308 may then implement any appropriate adjustment to the FIO2 setting based at least in part on the sent values. Adjustment of the FIO2 setting may be accomplished via appropriate actuation of an oxygen source valve, or by adjusting an oxygen concentration from an oxygen supply, by adjusting the flow rate from a portable oxygen concentrator (POC), or in other ways.

[0311] Still further, on a continuous basis, central control 308 may provide data to PEEP control 314, including the current FIO2 and current PEEP. PEEP control 314 may then use that data, potentially in addition to other data, in determining an updated value for PEEP. Central control 308 may then implement any appropriate adjustment to the PEEP setting based at least in part on the sent value. Adjustment of the PEEP setting may be accomplished via appropriate actuation of an exhalation valve, for example.

[0312] It is to be understood that, while central control 308. ventilation control 310, FIO2 control 312 and PEEP control are described separately and communication with eachother, in some embodiments, some or all of these may be combined or integrated, or may function independently. In such embodiments, communication between combined or integrated components may be less or may be unnecessary.

[0313] FIG. 30 is a block diagram of an example portable ventilator 902 (although, in other embodiments, non-portable ventilators may be used and the general blocks depicted here may be similar) that can be used in a ventilation system including a PVAM. The portable ventilator 902 includes a mechanical ventilation apparatus 908 and at least one controller 918. The controller 918 includes at least one processor 920 and at least one memory 916 for storing data, and may also include software that may be stored in the at least one memory 916, such as may include, or may include aspects of, a PVAM 922, embodiments of which are previously described herein (however, in some embodiments, a PVAM, or components thereof, may be included in another devices, or may be distributed between multiple devices, which may or may not include a ventilator or portable ventilator). The portable ventilator 902 may include or be connected to supplemental oxygen sources 905, which may include, for example, one or more POC systems and / or one or more high pressure oxygen sources 905. In some embodiments, the system 900 also includes an 02 sensor 922 (or 02 sensing component including an 02 sensor), as described with reference to FIG. 1, that may be, e.g., coupled with the inspiration limb of the patient circuit, or elsewhere.

[0314] In some embodiments, the portable ventilator 902 is capable of sensing signals representative of gas flow that can be used in determining at least one patient respiratory parameter, such as may include use or one or more pressure sensors 914, Plow sensors, pneumotachometers or spirometers, that may, in some embodiments, be included as part of, or within or partially within, the mechanical ventilation apparatus 908, or may be separate but communicatively coupled by wired or wireless connection. In various embodiments, one or more spirometers may be included within the portable ventilator 902 and / or may be coupled (physically and / or communicatively by wired or wireless connection) with the portable ventilator 902. Although depicted in a separate box from the patient circuits 913, it is to be understood that the pressure sensors 914 and / or other components such as flow sensors, pneumotachometers or spirometers, may be included within or partially within the patient circuits 913, such as a patient inspiratory' circuit and / or a patient expiratory7circuit. In some embodiments, the pressure sensor(s) 914 and other associated components may be used in sensing or obtaining respiratory parameter data, which respiratory7parameter data may be used in determining or generating a respiratory status (which can include associated data).The respiratory parameter data and respiratory status data may also be used in connection with control of operation of the portable ventilator 902, such as in control of mechanical ventilation providing by the portable ventilator 902, whether such control is internally provided by the portable ventilator 902, remotely provided, or with aspects of both.

[0315] FIG. 31 illustrates aspects of an example of the mechanical ventilation apparatus for a ventilation system such as a portable ventilator, used in the providing mechanical ventilation to a patient or patients, including a PVAM. As depicted, the controller 2750 includes a PVAM 2751, embodiments of which are previously described herein (however, in other embodiments, the PVAM, or components thereof, may be included in one or more other devices).

[0316] The patient interface 2744 may include an appropriate gas delivery device, such as an intubation tube, mask, nasal cannula, etc. The mechanical ventilation apparatus 2740 further includes an expiratory line 2745 and an exhalation valve 2748 and a heat and moisture exchanger. Both the inspiratory line 2743 and the expiratory line 2745 include sensors 2747. The sensors 2747 may include, for example, but not limited to. the pneumotachometer 275, the airw ay pressure sensor 274, and a spirometer. Sensors 2747 enable the ventilation system to measure the patient’s respiratory efforts as well as the performance of the ventilation system when providing mechanical respiratory' assistance to the patient. The sensors 2747 may generate and provide the data including but not limited to flow rate, tidal volume and minute volume, respiratory mechanics (e.g., resistance and compliance) and spirometry, and may include, for example, forced vital capacity (FVC), forced vital capacity at 1 second (FEV1) and peak expiratory flow' rate (PEF or PEFR). In addition, a defibrillator / CCM or another medical device may provide, for example, capnography and / or oximetry data, such as oxyhemoglobin and carboxyhemoglobin saturation and mainstream or other capnographic data such as end-tidal CO2 (EtCO2). This data may allow for example, for calculation of CO2 elimination rate and volumetric capnography, which may include using flow' data from the ventilation system.

[0317] FIG. 32 is an illustration of a simplified example (e.g. portable) ventilator and display, which can be used in a ventilation system including a PVAM. It will be appreciated that the ventilator and / or display may include only some of the features or functions shown. As shown, the portable ventilator 2000 includes features such as a fresh gas / emergency air intake 2002, handle 2006, power switch 2009 (which, in other embodiments, could be, e.g., a soft button), battery compartment 2010, user selection dial 2011, control panel 2012. manual breath / plateau pressure button 2013, menu button 2019, oxygen inlet 2017, which may beused in coupling of an high-pressure oxygen (HPO2) source (or other pressurized oxygen source), and display or user interface 2016. In some embodiments, the handle 2006, and other features of the portable ventilator 2000, such as controls and display features and placement, are configured to enable single-handed operation of the portable ventilator 2000. In some such embodiments, for example, a user may grasp the handle of the ventilator 2000 with one or more fingers of a single hand (e.g., one or more of the index finger, middle finger, ring finger and pinkie) while simultaneously operating controls using one or more fingers of the single hand (e g., one or more of the thumb, index finger and middle finger).

[0318] The fresh gas / emergency air intake 2002 provides a gas path and allows ambient air into the device’s internal compressor. Built-in filters are used to protect the compressor and patient from particulate matter. The intake 2002 also acts as an anti-asphyxia path that enables the patient to breathe ambient air, should the ventilator 2000 fail. The intake 2002 further contains a particulate filter and permits the user to connect either a bacteria / viral or a chemical / biological filter, depending on ambient conditions. Furthermore, an oxygen reservoir bag assembly may be connected to the intake 2002 to allow for low flow oxygen use with the ventilator 2000 in order to provide a source of supplemental oxygen to patients during ventilation. For example, low-flow oxygen sources can be obtained based on a flow meter or an oxygen concentrator. Oxygen may be delivered through the intake 2002 when the ventilator’s internal compressor cycles deliver breaths.

[0319] A top panel of the ventilator 2000 may have components including, in addition to the intake 2002 and the pulse oximeter connector 2001 , a high-pressure oxygen input, a gas output, a power cord connector for external AC / DC power, a USB port, an exhalation valve port, an exhaust valve and a pressure transducer port. A pulse oximeter, which may provide continuous non-invasive monitoring of SpO2 and pulse rate. Additionally, an oxygen sensing module 2034 including an 02 sensor, may couple with the inspiratory limb of the patient circuit connected to a port on the top panel of the ventilator 2000.

[0320] The portable ventilator 2000 may be operable using external AC / DC power or a battery, such as an internal (e.g. lithium ion) battery. Furthermore, the ventilator 2000 may include at least one display or user interface 2016. which may, for example, include a liquid crystal display (LCD). Among other things, the display or user interface 2016 may provide a user with data relating to patient parameters and ventilation parameters, including current ventilator settings, which may be continuously updated. Furthermore, the display or user interface 2016 may include, among other things, various graphical user interface (GUI) aspects, allowing user interaction, such as to access particular data, change ventilatorselections or settings, confirm suggested displayed changes to ventilator settings, receive and respond to alarms, etc. In particular, as shown, the display or user interface 2016 includes parameter and alarm indicators 2007, an alarm message center / waveform window' 2018, parameter buttons 2008 and / or auxiliary parameter boxes 2014.

[0321] In some embodiments, the display or user interface 2016 may be divided into a number of sections. For example, as depicted, the top left area of the display or user interface 2016 may include airway pressure, flow, volume, capnography and plethysmography (pleth) waveform plots. This section may include displayed plots for airway pressure as well as, when a pulse oximeter is connected, the pleth waveform, and when a CO2 sensor is connected, the capnogram. When a plot is useful to facilitate a parameter adjustment by the user, a message area may display both the plot and a context menu that the user may use to make selections to obtain displayed context relating to the parameter.

[0322] The display or user interface 2016 also includes a menu display section in the top left area. This section may be used to display a menu after the user presses a menu button on the ventilator's control panel, and may be used to display context menus associated with particular parameters.

[0323] The display or user interface 2016 also includes an alarm message center / waveform window 2018 in the upper left area, in which visible alarms may at times be displayed. Some alarms may instruct the user to consult a phy sician, for example. In some embodiments, alarms may be categorized into different levels of priority, such as based on the level and / or urgency of the risk that the particular alarm condition may pose to the patient. Multiple alarms, along with their priorities, that have occurred recently may be available for display to a user, where the user may view the recent alarms by scrolling in a GUI, for example. In some embodiments, if the FIO2 setting is increased by a certain amount, such as 10% (or, e.g., 5 to 15%) during a predetermined period of time, such as 10 minutes (or, e.g., 5 to 15 minutes), an alarm may be generated (if FIO2 PCLC is active, for example). Furthermore, in some embodiments, certain alarms may cause some or all PCLC aspects, such as may include FIO2 PCLC and / or PEEP PCLC, to pause until the user clears the alarm. In some embodiments, during the time of the pause and before the alarm is cleared, the ventilator 2000 may operate using current parameter values, such as for FIO2 and PEEP, that were being used at the time that the pause was initiated.

[0324] The display or user interface 2016 (such as under the control of a controller or PAVM) may also include pop-up windows that may provide a user with context-sensitive guidance, such as in connection with manual adjustment of parameter values, for example.The display or user interface 2016 also includes various parameter windows on the right side. Displayed parameters may include, e.g.. SpO2, EtC02, FIO2, PEEP, PIP, Vt, BPM and blood pressure, for example. Each parameter window may display a primary parameter as well as secondary parameters, such as parameters that may be related to the primary parameter or with associated alarm limits. In some embodiments, solid text may be displayed for primary and secondary parameter values that can be adjusted by the user, while outlined text may be used for patient-dependent parameters, for example. Primary parameters may also include mode, which may include a user selectable mode of operation including assist / control (AC), SIMV (Synchronized Intermittent Mandatory Ventilation), continuous positive airway pressure (CPAP) and bilevel (BL).

[0325] Furthermore, the mode parameter may be associated with secondary’ parameter choices including volume targeting / volume targeted mode and pressure targeting / pressure targeted mode. Volume targeting (V) may aim to deliver a constant volume to the patient in the inspiratory time using an approximately constant flow rate. During volume targeting, the measured PIP parameter is displayed or highlighted. Pressure targeting (P) may aim to produce a constant airway pressure for the duration of the inspiratory time. During pressure targeting, the measured Vt parameter is displayed or highlighted.

[0326] The display or user interface 2016 also includes device-related icons section in the lower left area. This section may include icons that represent, and may provide status information on, for example, one or more of the ventilator’s power source (which may indicate whether the ventilator is operating on external power or its battery), a battery charging status icon, an oxygen supply attachment icon, and may include an icon that indicates whether audible alarms or permitted or muted. Examples of particular icons are described with reference to FIG. 35.

[0327] The display or user interface 2016 may also include an auxiliary parameter boxes section 2014, which may be located toward the bottom. This section may display parameter boxes that allow the user to adjust a particular parameter using a context menu associated with the parameter. In some embodiments, a user may take the following steps in setting up. The patient circuit may be attached to the ventilator’s top panel. A high-pressure oxygen supply, if it is to be used, is attached. The user inspects the fresh gas / emergency air intake filters and may attach other items, such as an oxygen reservoir bag, and biological and chemical filters. The user may choose a power source, such as an external or internal power source. The user may connect the power supply to the ventilator. Once preliminary steps are completed, the user may power on the ventilator 2000 using the ventilator’s power switch orbuton. Once powered on, the ventilator 2000 may perform a self-check, to check for potential alarm conditions as well as the operation of the pneumatic system, power system and internal communications system. During normal start-up, the ventilator’s alarms may be muted for a predetermined time, e.g., 2 minutes, to allow the user to connect items including the patient circuit and pulse oximeter, and to perform operational tests.

[0328] In some embodiments, after powering on the ventilator 2000, the user may choose from the settings defaults, such as adult, pediatric, mask CPAP. custom (includes use of saved settings values), and last setings (includes use of last-used setings values). The user may select one of the defaults, in which case ventilation will be initiated using the default setings associated with the selection. Alternatively, the user may manually set setings using parameter butons 2008. Furthermore, the user may select a mode of operation, including, as described briefly above, AC, SIMV, CPAP or BL. In AC, the patient receives either controlled or assisted breaths. When the patient triggers an assisted breath, the patient receives a breath, and either a pressure target or a volume target is utilized. In SIMV, the patient receives controlled breaths based on the setting of the breathing rate as well as assisted and spontaneous breaths. When a patient triggers a breath, the breath is either assisted or spontaneous based on whether the trigger occurs within a defined synchronization window. In CPAP, the patient receives a constant positive airway pressure while breathing spontaneously. Spontaneous breaths may be either unsupported demand flow or supported using pressure support PS). In Bilevel (BL) mode, the ventilator 2000 provides two pressure setings to assist the patient in breathing spontaneously, including a higher inspired positive airway pressure (IPAP) and a lower expiratory positive airway pressure (EPAP).

[0329] The parameter and alarm indicators 2007 may include information specifying current parameter values, and may also display other related information such as alarm threshold values 2028, which may indicate, for example, threshold beyond which an alarm may be triggered. The parameter and alarm indicators 2007 may include one or more of an SpO2 parameter display aspect 2003, a FIO2 parameter display aspect 2004 and a PEEP parameter display aspect 2005. In the SpO2 parameter display aspect 2003, the current patient SpO2 is displayed as "‘95”. meaning 95%. A target symbol 2035, along with the displayed numbers “94” and “88” indicate that the SpO2 target is set to 94% and the desaturation threshold of SpO2 is set to 88%. In the FIO2 parameter display aspect 2004, the current FIO2 is display ed as “99”, meaning 99%. Double arrows 2015 (which, in some embodiments, may be animated as displayed), indicate that FIO2 closed loop control is currently turned on and operating. The displayed “02 use” text indicates the current flow ofhigh-pressure oxygen consumed (in L / min) with the current FIO2 setting and patient minute volume.

[0330] In some embodiments, various alerts or warnings may be displayed to the user on the display or user interface 2016 before the user initiates FIO2 PCLC. For example, the user may be warned not to use FIO2 PCLC if the user suspects that pulse oximetry may not operate correctly or may not be available, or if the patient has carboxyhemoglobin poising (i.e., carbon monoxide poisoning), in which case the user may be advised to follow the local standard of care. The user may also be warned not to use FIO2 PCLC for patients with a core temperature of less than 35 degrees Celsius. Furthermore, in some embodiments, pulse oximetry and a high-pressure oxygen source and / or POC may be required for FIO2 PCLC, and failure of availability of either of these resources may cause FIO2 PCLC to be paused and may cause an associated alarm to be displayed to the user. In the PEEP parameter display aspect 2005, the current measured PIP is displayed as 28, meaning 28 cm H2O, and the current PEEP setting is displayed as 5. meaning 5 cm H2O. PIP alarm threshold levels are also displayed as 10 and 35, meaning 10 and 35 cm H20.

[0331] FIG. 33 illustrates example aspects of patient circuits 1700 that can be used in an example ventilation system 2364 including a PVAM, embodiments of which are described herein. Depicted components include an adult circuit 2362, including an inspiratory line 2352 and an expiratory line 2556, and an infant / pediatric circuit 2560, including an inspiratory line 2554 and an expiratory line 2358.

[0332] FIG. 34 illustrates an example of components of various devices described with reference to prior figures. The components 2808, 2810, 2812, 2814, 2816, and 2818 are communicatively coupled (directly and / or indirectly) to each other for bi-directional communication. Similarly, the components 2820, 2822. 2824, 2826, and 2828 are communicatively coupled (directly and / or indirectly) to each other for bi-directional communication.

[0333] In some implementations, the components 2808, 2810, 2816, and / or 2818 of the therapeutic medical device 2802 may be combined into one or more discrete components and components 2816 and / or 2818 may be part of the processor 2808. The processor 2808 and the memory 2810 may include and / or be coupled to associated circuitry in order to perform the functions described herein. Additionally, the components 2820, 2822, and 2828 of companion device 2804 may be combined into one or more discrete components and component 2828 may be part of the processor 2820. The processor 2820 and the memory 2822 may include and / or be coupled to associated circuitry in order to perform the functionsdescribed herein.

[0334] In some implementations, the therapeutic medical device 2802 may include the therapy delivery control module 2818. For example, the therapy delivery control module 2818 may be an electrotherapy delivery circuit that includes one or more high-voltage capacitors configured to store electrical energy for a pacing pulse or a defibrillating pulse. The electrotherapy delivery circuit may further include resistors, additional capacitors, relays and / or switches, electrical bridges such as an H-bridge (e.g., including a plurality of insulated gate bipolar transistors or IGBTs), voltage measuring components, and / or current measuring components. As another example, the therapy delivery control module 2818 may be a compression device electro-mechanical controller configured to control a mechanical compression device. As a further example, the therapy delivery control module 2818 may be an electro-mechanical controller configured to control drug delivery, temperature management, ventilation, and / or other type of therapy delivery.

[0335] The therapeutic medical device 2802 may incorporate and / or be configured to couple to one or more patient interface devices 2830 and patient interface devices that may be coupled with a patient 2849. The patient interface devices 2830 may include one or more therapy delivery component(s) 2832a and / or one or more sensor(s) 2832b. Similarly, the companion device 2804 may be adapted for medical use and may incorporate and / or be configured to couple to one or more patient interface device(s) 2834. The patient interface device(s) 2834 may include one or more sensors 2836. The sensor(s) 2836 may be substantially as described herein with regard to the sensor(s) 2832b.

[0336] The sensor(s) 2832b and 2836 may include one or more of: sensing electrodes (e.g., the sensing electrodes 2838), ventilation and / or respiration sensors (e g., the ventilation and / or respiration sensors 2830), temperature sensors (e.g., the temperature sensor 2842), chest compression sensors (e.g., the chest compression sensor 2844), etc. In some implementations, the information obtained from the sensors 2832b and 2836 can be used to generate information displayed at the therapeutic medical device 2802 and simultaneously at the display views at companion device 2804 and described above. In one example, the sensing electrodes 2838 may include cardiac sensing electrodes. The cardiac sensing electrodes may be conductive and / or capacitive electrodes configured to measure changes in a patient’s electrophysiology' to measure the patient’s ECG information. The sensing electrodes 2838 may further measure the transthoracic impedance and / or a heart rate of the patient. The ventilation and / or respiration sensors 2830 may include spirometry sensors, flow sensors, pressure sensors, oxygen and / or carbon dioxide sensors such as, for example, one ormore of pulse oximetry sensors, oxygenation sensors (e.g., muscle oxygenation / pH), 02 gas sensors and capnography sensors, impedance sensors, and combinations thereof. The temperature sensors 2842 may include one or more of an infrared thermometer, a contact thermometer, a remote thermometer, a liquid crystal thermometer, a thermocouple, a thermistor, etc. and may measure patient temperature internally and / or externally. The chest compression sensor 2844 may include one or more motion sensors including, for example, one or more accelerometers, one or more force sensors, one or more magnetic sensors, one or more velocity sensors, one or more displacement sensors, etc. The chest compression sensor 2844 may provide one or more signals indicative of the chest motion to the therapeutic medical device 2802 via a wired and / or wireless connection. The chest compression sensor 2844 may be, for example, but not limited to, a compression puck, a smart-phone, a handheld device, a wearable device, etc. The chest compression sensor 2844 may be configured to detect chest motion imparted by a rescuer and / or an automated chest compression device (e.g., a belt system, a piston system, etc.). The chest compression sensor 2844 may provide signals indicative of chest compression data including displacement data, velocity data, release velocity data, acceleration data, force data, compression rate data, dwell time data, hold time data, blood flow data, blood pressure data, etc. In an implementation, the defibrillation and / or pacing electrodes may include or be configured to couple to the chest compression sensor 2844.

[0337] In various implementations, the sensors 2832b and 2836 may include one or more sensor devices configured to provide sensor data that includes, for example, but not limited to ECG, blood pressure, heart rate, respiration rate, heart sounds, lung sounds, respiration sounds, end tidal CO2, saturation of muscle oxygen (SMO2), oxygen saturation (e.g., SpO2and / or Pat ), cerebral blood flow, point of care laboratory measurements (e.g.. lactate, glucose, etc.), temperature, electroencephalogram (EEG) signals, brain oxygen level, tissue pH, tissue fluid levels, images and / or videos via ultrasound, laryngoscopy, and / or other medical imaging techniques, near-infrared spectroscopy, pneumography, cardiography, and / or patient movement. Images and / or videos may be two-dimensional or three- dimensional. such a various forms of ultrasound imaging.

[0338] The one or more therapy delivery components 2832a may include one or more of: electrotherapy electrodes (e.g., the electrotherapy electrodes 2838a), ventilation device(s) (e.g., the ventilation devices 2838b). intravenous device(s) (e.g., the intravenous devices 2838c), compression device(s) (e.g.. the compression devices 2838d), etc. For example, the electrotherapy electrodes 2838a may include defibrillation electrodes, pacing electrodes, andcombinations thereof. The ventilation devices 2838b may include a tube, a mask, an abdominal and / or chest compressor (e.g., a belt, a cuirass, etc.), etc. and combinations thereof. The intravenous devices 2838c may include drug delivery devices, fluid deliver}' devices, and combinations thereof. The compression devices 2838d may include mechanical compression devices such as abdominal compressors, chest compressors, belts, pistons, and combinations thereof. In various implementation, the therapy deliver ’ component(s) 2832a may be configured to provide sensor data and / or be coupled to and / or incorporate sensors. For example, the electrotherapy electrodes 2838a may provide sensor data such as transthoracic impedance, ECG, heart rate, etc. Further the electrotherapy electrodes 2838a may include and or be coupled to a chest compression sensor. As another example, the ventilation devices 2838b may be coupled to and / or incorporate flow sensors, gas species sensors (e.g., oxygen sensor, carbon dioxide sensor, etc.), etc. As a further example, the intravenous devices 2838c may be coupled to and / or incorporate temperature sensors, flow sensors, blood pressure sensors, etc. As yet another example, the compression devices 2838d may be coupled to and / or incorporate chest compression sensors, patient position sensors, etc. The therapy delivery control modules 2818 may be configured to couple to and control the therapy delivery component(s) 2832a, respectively.

[0339] The one or more sensor(s) 2832b and 2836 and / or the therapy delivery’ component(s) 2832a may provide sensor data. The patient data provided at the display screens of the therapeutic medical device 2802 and companion device 2804 may display at least some or all of the sensor data and / or different sensor data. For example, the therapeutic medical device 2802 may process signals received from the sensor(s) 2832b and / or the therapy delivery component(s) 2832a to determine the sensor data. Similarly, the companion device 2804 may process signals received from the sensor(s) 2836 and / or sensor data from the sensors 2832b received via the therapeutic medical device 2802to determine the sensor data.

[0340] Some embodiments include providing mechanical ventilation in connection with, for example, a patient experiencing respiratory’ distress. Respiratory distress may include, for example, any form of respiratory or breathing difficulty’, impairment or problem, including respirator}' failure. Respiratory parameter data can include data relating to respirator}’, pulmonary or lung related parameters, as may, for example, be associated with respiratory, pulmonary or lung related characteristics, attributes or functions. Respirator}' parameter data can include, for example, one or more of data related to pulmonary associated pressure, floyv, volume or capacity related parameters, including parameters that may bemeasured using one or more flow sensors, pneumotachometers or spirometers. Respiratory parameter data can include, for example, one or more of data relating to respiratory- mechanics, such as respiratory compliance (Crs), respiratory elastance) and respiratory resistance (Rrs). Respiratory parameter data can also include, for example, parameters such as vital capacity- (VC), forced vital capacity- (FVC), forced expiratory- volume (FEV) at timed intervals (e.g., between 0.5 and 10 seconds, less than 0.5 second, 0.5 second, 1.0 second or FEV1, 2.0. 3.0 seconds, 4.0 seconds or 5.0 seconds), forced expiratory flow (FEF) at, e.g.. 10%-90% capacity^, such as 25%-75% or FEF25-75, peak expiratory flow rate (PEF or PEFR) and maximum breathing capacity' (sometimes called maximal voluntary- ventilation or MW). Respiratory- parameter data may be presented in various different ways or forms, such as may include, for example, in raw terms, such as liters or liters per second, or as percentages. Respiratory- parameter data may also be presented, for example, as “predicted” values, such as percent predicted, which can, for example, include results as a percentage value in connection with a reference, average, or other “predicted” value. “Predicted” values may, in some cases, be associated with patients or hypothetical patients of one or more similar characteristics.

[0341] A respiratory status may, for example, relate to, identify, or indicate the presence or absence of, one or more respiratory, pulmonary- and / or lung related conditions, and may include respiratory distress. A condition may include a state, disease, or problem, or several thereof, or a type, group or category thereof. A respiratory status may include an etiology relating to a respiratory condition or a non-respiratory condition (e.g., respiratory distress associated with acute heart failure). A non-respiratory' condition may, for example, be associated with one or more non-respiratory systems, e.g., cardiac, endocrine, exocrine, circulatory, immune, lymphatic, nervous, muscular, renal, skeletal, or others. Additionally, a respiratory status may include one or more determined, assessed, estimated, probable or possible conditions, or determined, assessed, probable or possible associated etiologies. A respiratory- status may also include data associated any of the foregoing, which may be called respiratory status data.

[0342] Ventilators, such as portable ventilators, and ventilation systems, may include, for example, devices or systems capable of delivering ventilation, whether such delivered ventilation is controlled internally, remotely, or with aspects of both.

[0343] Various ventilation parameter related terms or abbreviations, including fraction of inspired oxygen (FIO2), positive end-expiratory pressure (PEEP) and others, refer to ventilation related settings, even though the word “setting” may or may not be stated.Furthermore, reference to a ventilation parameter, parameter setting, or setting may be used to refer to the parameter in a conceptual or definitional sense, or the value associated with a particular setting. A user may include an individual operating, supervising or in whole or in part responsible for operation of a device such as a portable ventilator, even if, during a particular period of time while the device is operating, the user may not be interacting with the device.

[0344] Ventilation related settings may include, for example, any setting, such as a current, selected, set or entered ventilation parameter, parameter value, or other setting relating to ventilation, any aspect of ventilation, operation of a ventilator, such as a portable ventilator, in association with pr...

Claims

WHAT IS CLAIMED IS:

1. A system for managing patient ventilator asynchrony (PVA) during mechanical ventilation therapy of a patient, the system comprising: a mechanical ventilation system for providing mechanical ventilation to the patient; and at least one controller, communicatively coupled with the mechanical ventilation system, the at least one controller configured to: obtain input comprising:PVA data identifying a type of PVA, ventilatory parameter data identifying one or more ventilatory parameters relating to the mechanical ventilation being provided to the patient, and a measure of PVA severity relating to the type of PVA, and determine output based at least in part on the obtained input, the output comprising at least one of: one or more PVA mitigation notifications and at least one adjustment relating to the mechanical ventilation being provided to the patient.2 . The system of claim 1, wherein obtaining the measure of PVA severity7comprises determining a magnitude of PVA severity for each of one or more patient breaths.

3. The system of claim 2, wherein determining the magnitude of PVA severity for each of the one or more patient breaths comprises analyzing at least one of: a patient airway pressure waveform and a patient airway flow waveform, associated with a period of time during which the type of PVA is present.

4. The system of claim 3, wherein the patient airway7pressure waveform is obtained using measured patient airway pressure.

5. The system of claim 3. wherein the patient airway flow waveform is obtained using measured patient airway flow.

6. The system of claim 2, wherein determining the magnitude of PVA severity7for each of one or more patient breaths comprises at least one of: comparing a patient airway pressure waveform to an airway pressure waveform for which the type of PVA is not present, andcomparing a patient airway flow waveform to an airway flow waveform for which the type of PVA is not present.

7. The system of claim 6, wherein the measure of severity is based at least in part on an estimated work of breathing of the patient, wherein the estimated work of breathing of the patient is based at least in part on measured patient airway pressure.

8. The system of claim 2, wherein determining the magnitude of PVA severity for the type of PVA for each of the one or more patient breaths comprises: obtaining a first airway pressure waveform associated with a breath for which the ty pe of PVA is not present, obtaining a patient airway pressure waveform associated with a patient breath of the one or more breaths for which the type of PVA is present, comparing the first airway pressure waveform to the patient airway pressure waveform to determine a deviation between the first airway pressure waveform and the patient airway pressure waveform. determining a magnitude of the deviation, and based at least in part on the magnitude of the deviation, determining the magnitude of PVA severity.

9. The system of claim 8, wherein determining the deviation comprises: determining, for the patient airway pressure waveform, a quantified graphical area associated with the type of PVA, and determining the magnitude of the deviation based on the quantified graphical area.

10. The system of claim 1, wherein providing the at least one of: one or more PVA mitigation notifications and at least one adjustment relating to the mechanical ventilation being provided to the patient comprises determining an adjustment relating to the mechanical ventilation being provided to the patient, the adjustment comprising an amount of a change to at least one of the one or more ventilatory parameters.

11. The system of claim 1, wherein the type of PVA is a detected type of PVA.

12. The system of claim 1, wherein the at least one controller is configured to determinethe measure of PVA severity.

13. The system of claim 1, wherein the at least one adjustment is for implementation to mitigate the type of PVA.

14. The system of claim 1, wherein the at least one adjustment is to at least one ventilatory parameter of the one or more ventilatory parameters and for mitigation of the type of PVA.

15. The system of claim 1. wherein the measure of PVA severity is based at least in part on measured patient airway pressure.

16. The system of claim 15, wherein the measure of severity is based at least in part on an estimated work of breathing of the patient, wherein the estimated work of breathing of the patient is based at least in part on the measured patient airway pressure.

17. The system of claim 16, wherein the estimated work of breathing is estimated based at least in part on the measured patient airway pressure for at least a portion of an inspiration and estimated using an artificial neural network.

18. The system of claim 15, wherein the measure of severity is based at least in part on a calculated pressure-time product relating to the measured patient airway pressure for at least a portion of an inspiration.

19. The system of claim 1, wherein the measure of severity is based at least in part on airway occlusion pressure measured during at least a portion of an inspiration.

20. The system of claim 1. wherein the measure of PVA severity comprises a numerical PVA severity' score, wherein a magnitude of the PVA severity score corresponds with PVA severity.

21. The system of claim 1. wherein the at least one controller is configured to: monitor the measure of PVA severity over a period of time, and determine the output based at least in part on the monitored measure of PVA severityover the period of time.

22. The system of claim 1, wherein the at least one controller is configured to: forecast the measure of PVA severity over a future period of time based at least in part on the measure of PVA severity as monitored over the period of time, and determine the output based at least on the forecasting.

23. The system of claim 21, wherein the at least one controller is configured to: determine a level of improvement of a patient condition over the period of time, based at least in part on the monitored measure of PVA severity, and determine at least one intervention recommendation, for providing to a care provider, based at least in part on the determined level of improvement of the patient condition.

24. The system of claim 1, wherein the input comprises patient data identifying one or more patient characteristics comprising at least one of: patient sex, patient age, patient height, a patient body weight related characteristic, and a patient respiratory mechanics parameter.

25. The system of claim 1, wherein the at least one controller is configured to: compare at least a portion of the input to at least one specified threshold, the at least a portion of the input comprising at least a portion of the ventilatory parameter data, and determine the output based at least in part on the comparison.

26. The system of claim 1, wherein the type of PVA comprises at least one of: a trigger related asynchrony, a cycling related asynchrony, a flow related asynchrony, ineffective efforts during expiration, autotriggering, prolonged cycling, short cycling, insufficient flow, excessive flow, and double triggering.

27. The system of claim 1, wherein the one or more ventilatory parameters comprise at least one of: one or more measured ventilation related parameters, one or more ventilation relating settings, a ventilation mode, a pressure support level, a respiratory rate, a pressure rise time, a ventilation targeting scheme, a baseline airway pressure (BAP), an inspiratory time, an expiratory time, a trigger sensitivity, a tidal volume, a flow cycle threshold, and active ventilation related alarm data.

28. The system of claim 1, wherein the one or more PVA mitigation notifications comprise one or more PVA mitigation messages.

29. The system of claim 28, wherein the one or more PVA mitigation messages comprise one or more recommendations regarding one or more adjustments to one or more ventilatoryparameters or one or more alarm messages that are at least one of: capable of being muted by a care provider, capable of being interacted with by the care provider to display additional PVA mitigation information, and capable of being interacted with by the care provider to implement at least one of measure to mitigate the type of PVA.

30. The system of claim 29, wherein the one or more PVA mitigation notifications comprise at least one of: one or more user actions, performing a check relating to the mechanical ventilation system, administering at least one drug to the patient, and one or more adjustments to one or more ventilation parameters.

31. The system of claim 1. wherein the system comprises at least one display incorporated into at least one of: the mechanical ventilation system, a portable computing device and a defibrillator, and wherein the one or more PVA mitigation notifications are presented on the at least one display.

32. The system of claim 1 , wherein the at least one adjustment is automatically implemented, and wherein the automatic implementation comprises implementation without user confirmation.

33. The system of claim 1, wherein the at least one controller is configured to implement the at least one adjustment after receiving confirmation from a care provider to implement the at least one adjustment.

34. The system of claim 1. wherein the output comprises at least one adjustment comprising a plurality of periodic adjustments, and wherein the at least one controller is configured to: monitor a set of one or more ventilatory parameters during a period of the mechanical ventilation being provided to the patient, and determine the plurality of periodic adjustments such that the monitored one or moreventilatory parameters are maintained within one or more specified ranges during the period.

35. The system of claim 1, wherein the output comprises at least one adjustment comprising a plurality of periodic adjustments, and wherein the at least one controller is configured to: monitor the measure of PVA severity during a period of the mechanical ventilation being provided to the patient, and determine the plurality of periodic adjustments such that the measure of PVA severity is minimized during the period.

36. The system of claim 1. wherein at least a first controller, of the at least one controller, is incorporated into at least one device communicatively coupled with the mechanical ventilation system.

37. The system of claim 1, wherein the mechanical ventilation system comprises at least a first controller of the at least one controller.

38. The system of claim 37, wherein the at least a first controller of the mechanical ventilation system obtains the PVA data from at least one communicatively coupled device.

39. The system of claim 37, wherein the at least a first controller of the mechanical ventilation system determines the PVA data.

40. The system of claim 37, wherein the at least a first controller of the mechanical ventilation system obtains the measure of PVA severity from at least one communicatively coupled device.

41. The system of claim 37, wherein the at least a first controller of the mechanical ventilation system determines the measure of PVA severity.

42. The system of claim 37, wherein the at least a first controller of the mechanical ventilation system determines the output.

43. The system of claim 37, wherein the at least a first controller of the mechanicalventilation system obtains the output from at least one communicatively coupled device.

44. The system of claim 1, wherein the type of PVA comprises ineffective efforts during expiration, and wherein the at least one controller is configured to determine the output to relate to an increase in trigger sensitivity.

45. The system of claim 1. wherein the type of PVA comprises ineffective efforts during expiration, and wherein the at least one controller is configured to: detect whether auto-positive end expiratory pressure (auto-PEEP) is present, and if auto-PEEP is detected, determine the output to relate to at least one of: an increase in PEEP, an increase expiratory time, a decrease in inspiratory time, and a decrease in respiratory rate.

46. The system of claim 1, wherein the ty pe of PVA comprises ineffective efforts during expiration, and wherein the at least one controller is configured to: determine whether patient respiratory muscle effort, associated with the ineffective efforts during expiration, is less than a specified threshold, determine whether respiratory rate is not equal to a set respiratory' rate, and if the patient respiratory muscle effort is less than the specified threshold, and if the respiratory rate is not equal to the set respiratory’ rate, then determine the output to relate to a decrease in sedation.

47. The system of claim 1, wherein the type of PVA comprises ineffective efforts during expiration, and wherein the at least one controller is configured to: determine whether measured tidal volume is greater than a specified tidal volume threshold, and if the measured tidal volume is greater than the specified tidal volume threshold, then determine the output to relate to a reduction in pressure support.

48. The system of claim 47, wherein the specified tidal volume threshold is a value between 6 and 10 ml / kg of a body weight associated with the patient, and wherein the body weight is predicted or obtained based at least in part on patient sex and patient height.

49. The system of claim 47, wherein the at least one controller is configured to:if the measured tidal volume is greater than the specified tidal volume threshold, then determine the output to relate to a reduction in pressure support to deliver a tidal volume of between a minimum value and a maximum value.

50. The system of claim 1, wherein the ty pe of PVA comprises autotriggering, and wherein the at least one controller is configured to determine the output to relate to a decrease in trigger sensitivity.

51. The system of claim 1, wherein the type of PVA comprises autotriggering, and wherein the at least one controller is configured to: determine whether a gas leak is detected relating to the mechanical ventilation system, and if the gas leak is detected, then determine the one or more PVA mitigation notifications to include at least one of a message to: check the mechanical ventilation system for loose connections, check exhalation valve, check placement of an endotracheal tube, check placement of a tracheotomy cuff, check a mask or other noninvasive patient-interface accessary replace a portion of a patient circuit, and replace the patient circuit.

52. The system of claim 51, wherein the at least one controller is configured to determine whether the gas leak is detected based at least in part on whether a difference between an inspiratory volume and an expiratory volume is greater than a specified threshold.

53. The system of claim 1, wherein the type of PVA comprises autotriggering, and wherein the at least one controller is configured to: determine whether condensate is detected in a patient circuit of the mechanical ventilation system, and if the condensate is detected, then determine the one or more PVA mitigation notifications to include a message to remove condensate from the patient circuit.

54. The system of claim 1, wherein the type of PVA comprises prolonged cycling, and wherein the at least one controller is configured to determine the output to relate to at least one of: a decrease in inspiratory time, and an increase in flow cycle threshold.

55. The system of claim 1, wherein the at least one controller is configured to:determine whether a gas leak relating to the mechanical ventilation system is detected, and if the gas leak is detected, then determine the one or more PVA mitigation notifications to include at least one of a message to: check the mechanical ventilation system for loose connections, check exhalation valve, check placement of an endotracheal tube, check placement of a tracheotomy cuff, check a mask or other noninvasive patient-interface accessary, replace a portion of a patient circuit, and replace the patient circuit.

56. The system of claim 55, wherein the at least one controller is configured to determine whether the gas leak is detected based at least in part on whether a difference between an inspiratory volume and an expiratory volume is greater than a specified threshold.

57. The system of claim 1, wherein the at least one controller is configured to: determine whether an obstructive respiratory mechanics condition is detected, and if the obstructive respiratory mechanics condition is detected, then determine the output to relate to at least one of: an increase in a flow cycling threshold, a decrease in pressure support, and a decrease in pressure rise time.

58. The system of claim 57, wherein the at least one controller is configured to determine whether the obstructive respiratory mechanics condition is detected based at least in part on whether the patient’s respiratory resistance is greater than a specified respiratory resistance threshold.

59. The system of claim 1, wherein the type of PVA comprises short cycling, and wherein the at least one controller is configured to determine the output to relate to at least one of: an increase in inspiratory time, and a decrease in flow cycle threshold.

60. The system of claim 1, wherein the type of PVA comprises short cycling, and wherein the at least one controller is configured to: determine whether a restrictive respiratory mechanics condition is detected, and if the restrictive respiratory’ mechanics condition is detected, then determine the output to relate to an increase in pressure support.

61. The system of claim 60, wherein the at least one controller is configured to determinewhether the restrictive respiratory mechanics condition is detected based at least in part on whether the patient's respiratory compliance is less than a specified respiratory compliance threshold.

62. The system of claim 1, wherein the ty pe of PVA comprises insufficient flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is volume targeted, determine whether an inspiratory flow is below a specified inspiratory' flow threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the inspiratory flow is below the specified inspiratory flow threshold, then determine the output to relate to an increase in the inspiratory flow.

63. The system of claim 1, wherein the type of PVA comprises insufficient flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is pressure targeted, determine whether a patient airway pressure is below a specified airway pressure threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the pressure is below the specified airway pressure threshold, then determine the output to relate to an increase in the patient airway pressure.

64. The system of claim 1, wherein the type of PVA comprises insufficient flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is pressure targeted, determine whether a patient airway pressure rise time is above a specified rise time threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the patient airway pressure rise time is above the specified rise time threshold, then determine the output to relate to a decrease pressure rise time.

65. The system of claim 1, wherein the type of PVA comprises insufficient flow, and wherein the at least one controller is configured to: determine whether at least one of: excessive ventilator demand and excessive neural drive is detected, and if at least one of: the excessive ventilator demand, and the excessive neural drive is detected, then determine the output to relate to at least one of: a reduction in metabolic demand, and a reduction in the neural drive.

66. The system of claim 1, wherein the type of PVA comprises excessive flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is volume targeted, determine whether an inspiratory flow is above a specified inspirator}' flow threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the inspiratory flow is above the specified inspiratory flow threshold, then determine the output to relate to a decrease in the inspirator}' flow.

67. The system of claim 1, wherein the type of PVA comprises excessive flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is pressure targeted, determine whether a patient airway pressure is above a specified airway pressure threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the patient airway pressure is above the specified airway pressure threshold, then determine the output to relate to a decrease in the patient airway pressure.

68. The system of claim 1. wherein the type of PVA comprises excessive flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is pressure targeted, determine whether a patient airway pressure rise time is below a specified rise time threshold, andif the mechanical ventilation being provided to the patient is pressure targeted, and if the patient airway pressure rise time is below the specified rise time threshold, then determine the output to relate to an increase the patient airway pressure rise time.

69. The system of claim 1, wherein the ty pe of PVA comprises excessive flow, and wherein the at least one controller is configured to: determine whether at least one of: low ventilator demand, and low neural drive is detected, and if at least one of: the low ventilator demand, and the excessive neural drive is detected, then determine the output to relate to at least one of: a reduction in metabolic demand, and a reduction in the neural drive.

70. The system of claim 1, wherein the type of PVA comprises double triggering, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is volume targeted, determine whether tidal volume is below a specified tidal volume threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the tidal volume is below the specified tidal volume threshold, then determine the one or more PVA mitigation notifications to include at least one of a message to: increase sedation or neuromuscular blockages, and change to pressure targeted mechanical ventilation.

71. The system of claim 1, wherein the type of PVA comprises double triggering, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is volume targeted, determine whether patient respiratory7compliance is below a specified respiratory- resistance threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the patient respiratory compliance is below a specified respiratory' compliance threshold, then determine the output to relate to a decrease in a flow cycling threshold.

72. A system for managing patient ventilator asynchrony (PVA) during mechanical ventilation therapy of a patient, the system comprising:a mechanical ventilation system for providing mechanical ventilation to the patient; and at least one controller, communicatively coupled with the mechanical ventilation system, the at least one controller configured to: obtain input comprising:PVA data identifying a type of PVA, ventilatory parameter data identifying one or more ventilatory parameters relating to the mechanical ventilation being provided to the patient, patient data identifying one or more patient characteristics comprising at least one of: patient sex, patient age, patient height, a patient bodyweight related characteristic, and a patient respiratory mechanics parameter, and compare at least a portion of the obtained input to at least one specified threshold, the at least a portion of the obtained input comprising at least a portion of the ventilatory parameter data, and determine output based at least in part on the obtained input and the comparison, the output comprising at least one of: one or more PVA mitigation notifications to provide guidance relating to one or more measures to be taken to mitigate the type of PVA, and at least one adjustment to at least one ventilatory parameter.

73. The system of claim 72, wherein the type of PVA is a detected type of PVA.

74. The system of claim 72, wherein the at least one adjustment is for implementation to mitigate the type of PVA.

75. The system of claim 72, wherein the one or more PVA mitigation notifications are for presentation to a care provider.

76. The system of claim 72, wherein the one or more PVA mitigation notifications comprises root cause information associated with the type of PVA.

77. The system of claim 72, wherein the one or more patient characteristics further comprise at least one past or present medical related or health related condition of the patient.

78. The system of claim 72, wherein the at least one specified threshold is associated withat least one specified magnitude or at least one waveform characteristic.

79. The system of claim 78, wherein the at least one specified magnitude comprises at least one specified numerical magnitude.

80. The system of claim 72, wherein the comparison comprises determining whether at least one ventilatory parameter, of the one or more ventilatory parameters, meets the at least one specified threshold.

81. The system of claim 72, wherein the at least one specified threshold comprises a plurality of thresholds.

82. The system of claim 72, wherein the type of PVA comprises ineffective efforts during expiration, and wherein the at least one controller is configured to determine the output to relate to an increase in trigger sensitivity.

83. The system of claim 72, wherein the type of PVA comprises ineffective efforts during expiration, and wherein the at least one controller is configured to: detect whether auto-positive end expiratory pressure (auto-PEEP) is present, and if auto-PEEP is detected, determine the output to relate to at least one of: an increase in PEEP, an increase expiratory time, a decrease in inspiratory time, and a decrease in respiratory rate.

84. The system of claim 72, wherein the type of PVA comprises ineffective efforts during expiration, and wherein the at least one controller is configured to: determine whether patient respiratory muscle effort, associated with the ineffective efforts during expiration, is less than a specified threshold, determine whether respiratory rate is not equal to a set respiratory rate, and if the patient respiratory muscle effort is less than the specified threshold, and if the respiratory rate is not equal to the set respiratory rate, then determine the output to relate to a decrease in sedation.

85. The system of claim 72, wherein the type of PVA comprises ineffective efforts during expiration, and wherein the at least one controller is configured to:determine whether measured tidal volume is greater than a specified tidal volume threshold, and if the measured tidal volume is greater than the specified tidal volume threshold, then determine the output to relate to a reduction in pressure support.

86. The system of claim 85, wherein the specified tidal volume threshold is between 6 and 10 ml / kg of a body weight associated with the patient, and wherein the body weight is predicted or obtained based at least in part on patient sex and patient height.

87. The system of claim 85, wherein the at least one controller is configured to: if the measured tidal volume is greater than the specified tidal volume threshold, then determine the output to relate to a reduction in pressure support to deliver a tidal volume of between 4 and 10 ml / kg of body weight.

88. The system of claim 72, wherein the type of PVA comprises autotriggering, and wherein the at least one controller is configured to determine the output to relate to a decrease in trigger sensitivity.

89. The system of claim 72, wherein the type of PVA comprises autotriggering, and wherein the at least one controller is configured to: determine whether a gas leak is detected relating to the mechanical ventilation system, and if the gas leak is detected, then determine the one or more PVA mitigation notifications to include at least one of a message to: check the mechanical ventilation system for loose connections, check exhalation valve, check placement of an endotracheal tube, check placement of a tracheotomy cuff, check a mask or other noninvasive patient-interface accessary , replace a portion of a patient circuit, and replace the patient circuit.

90. The system of claim 89, wherein the at least one controller is configured to determine whether the gas leak is detected based at least in part on whether a difference between an inspiratory volume and an expiratory' volume is greater than a specified threshold.

91. The system of claim 72, wherein the type of PVA comprises autotriggering, and wherein the at least one controller is configured to:determine whether condensate is detected in a patient circuit of the mechanical ventilation system, and if the condensate is detected, then determine the one or more PVA mitigation notifications to include a message to remove condensate from the patient circuit.

92. The system of claim 72, wherein the type of PVA comprises prolonged cycling, and wherein the at least one controller is configured to determine the output to relate to at least one of: a decrease in inspiratory time, and an increase in flow cycle threshold.

93. The system of claim 72, wherein the at least one controller is configured to: determine whether a gas leak relating to the mechanical ventilation system is detected, and if the gas leak is detected, then determine the one or more PVA mitigation notifications to include at least one of a message to: check the mechanical ventilation system for loose connections, check exhalation valve, check placement of an endotracheal tube, check placement of a tracheotomy cuff, check a mask or other noninvasive patient-interface accessary, replace a portion of a patient circuit, and replace the patient circuit.

94. The system of claim 93, wherein the at least one controller is configured to determine whether the gas leak is detected based at least in part on whether a difference between an inspiratory volume and an expiratory volume is greater than a specified threshold.

95. The system of claim 72, wherein the at least one controller is configured to: determine whether an obstructive respiratory’ mechanics condition is detected, and if the obstructive respiratory mechanics condition is detected, then determine the output to relate to at least one of: an increase in a flow cycling threshold, a decrease in pressure support, and a decrease in pressure rise time.

96. The system of claim 95, wherein the at least one controller is configured to determine whether the obstructive respiratory' mechanics condition is detected based at least in part on whether the patient’s respiratory' resistance is greater than a specified respiratory compliance threshold.

97. The system of claim 72, wherein the ty pe of PVA comprises short cycling, andwherein the at least one controller is configured to determine the output to relate to at least one of: an increase in inspiratory time, and a decrease in flow cycle threshold.

98. The system of claim 72, wherein the type of PVA comprises short cycling, and wherein the at least one controller is configured to: determine whether a restrictive respiratory mechanics condition is detected, and if the restrictive respiratory mechanics condition is detected, then determine the output to relate to an increase in pressure support.

99. The system of claim 98, wherein the at least one controller is configured to determine whether the restrictive respiratory mechanics condition is detected based at least in part on whether the patient’s respiratory compliance is less than a specified respiratory compliance threshold.

100. The system of claim 72, wherein the type of PVA comprises insufficient flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is volume targeted, determine whether an inspiratory flow is below a specified inspiratory flow threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the inspiratory flow is below the specified inspiratory flow threshold, then determine the output to relate to an increase in the inspiratory flow.

101. The system of claim 72, wherein the ty pe of PVA comprises insufficient flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is pressure targeted, determine whether a patient airway pressure is below a specified airway pressure threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the pressure is below the specified airway pressure threshold, then determine the output to relate to an increase in the patient airway pressure.

102. The system of claim 72, wherein the type of PVA comprises insufficient flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is pressure targeted, determine whether a patient airway pressure rise time is above a specified rise time threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the patient airway pressure rise time is above the specified rise time threshold, then determine the output to relate to a decrease the patient airway pressure rise time.

103. The system of claim 72, wherein the type of PVA comprises insufficient flow, and wherein the at least one controller is configured to: determine whether at least one of: excessive ventilator demand and excessive neural drive is detected, and if at least one of: the excessive ventilator demand, and the excessive neural drive is detected, then determine the output to relate to at least one of: a reduction in metabolic demand, and a reduction in the neural drive.

104. The system of claim 72, wherein the type of PVA comprises excessive flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is volume targeted, determine whether an inspiratory' flow is above a specified inspiratory' flow threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the inspiratory flow is above the specified inspiratory flow threshold, then determine the output to relate to a decrease in the inspiratory flow.

105. The system of claim 72, wherein the type of PVA comprises excessive flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is pressure targeted, determine whether a patient airway pressure is above a specified airway pressure threshold, andif the mechanical ventilation being provided to the patient is volume targeted, and if the patient airway pressure is above the specified airway pressure threshold, then determine the output to relate to a decrease in the patient airway pressure.

106. The system of claim 72, wherein the ty pe of PVA comprises excessive flow, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is pressure targeted, determine whether a patient airway pressure rise time is below a specified rise time threshold, and if the mechanical ventilation being provided to the patient is pressure targeted, and if the patient airway pressure rise time is below the specified rise time threshold, then determine the output to relate to an increase the patient airway pressure rise time.

107. The system of claim 72, wherein the type of PVA comprises excessive flow, and wherein the at least one controller is configured to: determine whether at least one of: low ventilator demand, and low neural drive is detected, and if at least one of: the low ventilator demand, and the low neural drive is detected, then determine the output to relate to at least one of: a reduction in metabolic demand, and a reduction in the neural drive.

108. The system of claim 72, wherein the type of PVA comprises double triggering, and wherein the at least one controller is configured to: determine whether the mechanical ventilation being provided to the patient is volume targeted, determine whether tidal volume is below a specified tidal volume threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the tidal volume is below the specified tidal volume threshold, then determine the one or more PVA mitigation notifications to include at least one of a message to: increase sedation or neuromuscular blockages, and change to pressure targeted mechanical ventilation.

109. The system of claim 72, wherein the type of PVA comprises double triggering, and wherein the at least one controller is configured to:determine whether the mechanical ventilation being provided to the patient is volume targeted, determine whether patient respiratory resistance is below a specified respiratory resistance threshold, and if the mechanical ventilation being provided to the patient is volume targeted, and if the patient respiratory resistance is below a specified respiratory resistance threshold, then determine the output to relate to a decrease in a flow cycling threshold.

110. A method for managing patient ventilator asynchrony (PVA) during mechanical ventilation therapy of a patient, the method comprising at least one controller, communicatively coupled with a mechanical ventilation system for providing mechanical ventilation to the patient: obtaining a detected type of PVA; determining a measure of PVA severity for the detected type of PVA. comprising determining a magnitude of PVA severity for each of one or more patient breaths; and providing, based at least in part on the detected type of PVA and the measure of PVA severity, in relation to one or ventilatory' parameters relating to the mechanical ventilation being provided to the patient, at least one of: one or more PVA mitigation notifications and at least one adjustment relating to the mechanical ventilation being provided to the patient.

111. The method of claim 110, wherein the one or more patient breaths comprises multiple patient breaths.1 12. The method of claim 111, wherein determining the measure of PVA severity comprises determining the measure of PVA severity for a period of time including the multiple patient breaths.

113. The method of claim 110, comprising obtaining, by the at least one controller, patient data identifying one or more patient characteristics comprising at least one of: patient sex, patient age, patient height, a patient body weight related characteristic, and a patient respiratory mechanics parameter, and comprising providing the at least one of: one or more PVA mitigation notifications and at least one adjustment relating to the mechanical ventilation being provided to the patient based at least in part on the patient data.

114. The method of claim 110, wherein determining the magnitude of PVA severity for each of the one or more patient breaths comprises analyzing at least one of: a patient airway pressure waveform and a patient airway flow waveform, associated with a period of time during which the detected type of PVA is determined to be present.

115. The system of claim 114, wherein the patient airway pressure waveform is obtained using measured patient airway pressure.

116. The system of claim 114, wherein the patient airway flow waveform is obtained using measured patient airway flow.

117. The method of claim 110, wherein determining the magnitude of PVA severity for each of one or more patient breaths comprises at least one of: comparing a patient airway pressure waveform to an airway pressure waveform for which the detected type of PVA is not present, and comparing a patient airway flow waveform to an airway flow waveform for which the detected type of PVA is not present.

118. The method of claim 117, wherein determining the magnitude of PVA severity for each of one or more patient breaths is based at least in part on an estimated work of breathing of the patient, wherein the estimated work of breathing of the patient is based at least in part on measured patient airway pressure.

119. The method of claim 110, wherein determining the magnitude of PVA severity for the detected type of PVA for each of the one or more patient breaths comprises: obtaining a first airway pressure waveform associated with a breath for which the detected type of PVA is not present, obtaining a patient airway pressure waveform associated with a patient breath of the one or more breaths for which the detected type of PVA is determined to the present. comparing the first airway pressure waveform to the patient airway pressure waveform to determine a deviation between the first airway pressure waveform and the patient airway pressure waveform, determining a magnitude of the deviation, and based at least in part on the magnitude of the deviation, determining the magnitude ofPVA severity'.

120. The method of claim 119, wherein determining the deviation comprises: determining, for the patient airway pressure waveform, a quantified graphical area associated with the detected type of PVA, and determining the magnitude of the deviation based on the quantified graphical area.

121. The method of claim 1 10, wherein determining the magnitude of PVA severity for the detected type of PVA for each of the one or more patient breaths comprises: obtaining a first airway flow waveform associated with a breath for which insufficient flow is not present, obtaining a patient airway flow waveform associated with a patient breath of the one or more breaths, for which the detected type of PVA is determined to the present, comparing the first airway flow waveform to the patient airway flow waveform to determine a deviation between the first airway flow waveform and the patient airway flow waveform, determining a magnitude of the deviation, and based at least in part on the magnitude of the deviation, determining the magnitude of PVA severity.

122. The method of claim 121, wherein determining the deviation comprises: determining, for the patient airway flow waveform, a quantified graphical area associated with the detected type of PVA, and determining the magnitude of the deviation based on the quantified graphical area.

123. The method of claim 122, comprising determining the magnitude of the deviation to be proportional to the quantified graphical area.

124. The method of claim 119, comprising determining a plurality of ranges of the magnitude of PVA severity, wherein each of the ranges is associated with a category relating to a level of severity of the detected type of PVA.

125. The method of claim 124, wherein the categories comprise a mild category, a moderate category and a severe category, wherein a range associated with the moderate category is higher than a range associated with the mild category, and wherein a rangeassociated with the severe category is higher than the range associated with the moderate category.

126. The method of claim 125, wherein the detected type of PVA is insufficient flow, and wherein the mild category is associated with the magnitude of the deviation in patient airway pressure in a range of between 0.4-0.8 cm H2O * second, the moderate category is associated with the magnitude of the deviation in patient airway pressure in a range of between 2.0-2.4 cm H2O * second, and the severe category is associated with the magnitude of the deviation in patient airway pressure at or above 7.9 cm H2O * second.

127. The method of claim 125, wherein the detected type of PVA is insufficient flow, and wherein the mild category is associated with the magnitude of the deviation in patient airw ay pressure of 0.6 cm H2O * second, the moderate category is associated with the magnitude of the deviation in patient airway pressure of 2.2 cm H2O * second, and the severe category is associated with the magnitude of the deviation in patient airway pressure at or above 7.9 cm H2O * second.

128. The method of claim 125, wherein the detected type of PVA is ineffective efforts during expiration, and wherein the mild category is associated with the magnitude of the deviation in patient airway pressure in a range of between 0.08-0.12 cm H20 * second, the moderate category is associated with the magnitude of the deviation in patient airway pressure in a range of betw een 0.3-0.5 cm H2O * second, and the severe category is associated with the magnitude of the deviation in patient airway pressure at or above 1.3 cm H2O * second.

129. The method of claim 125, wherein the detected type of PVA is ineffective efforts during expiration, and wherein the mild category is associated w ith the magnitude of the deviation in patient airway pressure of 0. 1 cm H2O * second, the moderate category is associated with the magnitude of the deviation in patient airway pressure of 0.4 cm H2O * second, and the severe category is associated with the magnitude of the deviation in patient airway pressure at or above 1.3 cm H2O * second.

130. The method of claim 121, comprising determining a plurality of ranges of the magnitude of PVA severity, wherein each of the ranges is associated with a category relatingto a level of severity of the detected type of PVA.

131. The method of claim 130, wherein the categories comprise a mild category, a moderate category and a severe category, wherein a range associated with the moderate category7is higher than a range associated with the mild category7, and wherein a range associated with the severe category is higher than the range associated with the moderate category.

131. The method of claim 130, wherein the detected type of PVA is ineffective efforts during expiration, and wherein the mild category7is associated with the magnitude of the deviation in patient airway flow in a range of between 0.3-0.5 1 / min * sec, the moderate category is associated with the magnitude of the deviation in patient airway flow in a range of between 1.3- 1.5 1 / min * sec, and the severe category' is associated with the magnitude of the deviation in patient airway flow at or above 3.8 1 / min * sec.

132. The method of claim 131, wherein the detected type of PVA is ineffective efforts during expiration, and wherein the mild category is associated with the magnitude of the deviation in patient airway flow of 0.4 1 / min * sec, the moderate category7is associated with the magnitude of the deviation in patient airway flow of 1.4 1 / min * sec, and the severe category is associated with a magnitude of the deviation in patient airway flow at or above 3.8 1 / min * sec.

133. The method of claim 110, comprising determining the magnitude of PVA severity7for each of the one or more patient breaths as a number, wherein the number is proportional to the magnitude of PVA severity'.

134. The method of claim 133, comprising determining, based on the number, a PVA severity7score between 0 and 1, wherein the PVA severity score is proportional to the number.

135. The method of claim 112, wherein determining the measure of PVA severity for the period of time comprises determining a PVA severity index, and wherein determining the PVA severity7index comprises: for each of the multiple patient breaths that are synchronous, assigning a PVAseverity score of 0, for each of the multiple patient breaths that include the detected type of PVA, assigning a PVA severity score associated with PVA severity, and determining the PVA severity index based on the assigned PVA severity scores for each of the multiple patient breaths.

136. The method of claim 133, comprising determining, based on the number, a PVA severity score between 0 and 1, wherein: when the number is between 0 and a specified threshold, the PVA severity score is determined to be proportional to the number, when the number is at or above the specified threshold, the PVA severity score is determined to be 1 .

137. The method of claim 110, wherein the measure of severity is based at least in part on an estimated work of breathing of the patient estimated on a breath by breath basis, wherein the estimated work of breathing of the patient is based at least in part on measured patient airway pressure.

138. The method of claim 110, wherein the one or more patient breaths comprises a plurality of successive breaths.

139. The method of claim 138, wherein determining the measure of PVA severity comprises utilizing the magnitude of PVA severity associated with each of the plurality of successive breaths.

140. The method of claim 139, wherein determining the measure of PVA severity comprises: determining a moving average relating to the plurality of successive breaths over a specified period of time, and determining the measure of PVA severity based at least in part on the moving average.

141. The method of claim 119, wherein the detected type of PVA comprises insufficient flow, and wherein determining the deviation comprises, for a period of time associated withthe insufficient flow, determining decreased patient airway pressure in the patient airway pressure waveform relative to the first airway pressure waveform.

142. The method of claim 119, wherein the detected type of PVA comprises ineffective efforts during expiration, and wherein determining the deviation comprises, for a period of time associated with the ineffective efforts during expiration, determining decreased patient airway pressure in the patient airway pressure waveform relative to the first airway pressure waveform.

143. The method of claim 121, wherein the detected type of PVA comprises ineffective efforts during expiration, and wherein determining the deviation comprises, for a period of time associated with the ineffective efforts during expiration, determining decreased patient airway flow in the patient airway flow waveform relative to the first airway flow waveform.

144. The method of claim 110, wherein the one or patient breaths comprises a plurality of successive patient breaths, and wherein determining the measure of PVA severity comprises determining a numerical PVA severity score representative of PVA severity over a specified period of time.

145. The method of claim 144, wherein determining the PVA severity score comprises: identify ing each of the plurality of successive patient breaths as either a synchronous breath if the breath does not include any t pe of PVA or an asynchronous breath if the breath includes any type of detected PVA, for each of the synchronous breaths, assigning the magnitude of PVA severity to be 0, for each of the asynchronous breaths, determining the magnitude of PVA severity for the asynchronous breath, and determining the PVA severity7score based on the magnitude of PVA severity for each of the synchronous breaths and each of the asynchronous breaths.

146. The method of claim 145, wherein determining the PVA severity score comprises an average of the magnitude of PVA severity for all of the plurality7of breaths.

147. The method of claim 146, comprising determining the magnitude of PVA severity for each of the asynchronous breaths as a number between 0-1, w herein the number isproportional to the magnitude of PVA severity, and determining the PVA severity score as a numerical score between 0-1.

148. The method of claim 145, wherein determining the measure of PVA severity comprises: determining a moving average over a specified period of time for the PVA severity score, identifying a PVA cluster period as a period of time in which the moving average of the PVA severity score is continuously above a specified threshold for at least a specified amount of time, determining the measure of PVA severity for the specified period of time based at least in part on the PVA cluster period.

149. The method of claim 148, wherein the specified amount of time is between 45 seconds and 75 seconds.

150. The method of claim 148, wherein the specified amount of time is 1 minute.

151. The method of claim 148, wherein the measure of PVA severity is increased based on the PVA cluster period.

152. The method of claim 151, wherein the measure of PVA severity is determined to be a number between 0-1, and wherein the specified threshold is between 0.3-0.7.

153. The method of claim 151, wherein the measure of PVA severity is determined to be a number betw een 0-1, and wherein the specified threshold is 0.5.

154. The method of claim 151, wherein the measure of PVA severity is determined to be a number between 0-1, and wherein the specified threshold is between 0.5.

155. The method of claim 148, comprising: determining a quantified graphical area associated with a moving average of the PVA severity score over the PVA cluster period, determining a cluster power, associated with the PVA cluster period, based on thequantified graphical area, and determining the measure of PVA severity for the specified period of time based at least in part on the cluster power.

156. The method of claim 110, wherein the at least one of: one or more PVA mitigation notifications and at least one adjustment relating to the mechanical ventilation being provided to the patient comprises a quantified adjustment to a first ventilatory parameter of the one or more ventilatory parameters.

157. The method of claim 156, wherein the quantified adjustment is proportional to the measure of PVA severity’.

158. The method of claim 157, wherein each of a plurality of specified ranges of the measure of PVA severity' are associated with a specific quantified adjustment.

159. The method of claim 158, wherein the plurality of specified ranges of the measure of PVA severity' correspond to categories relating to levels of the measure of PVA severity comprising a mild category', a moderate category' and a severe category', wherein a range associated with the moderate category is higher than a range associated with the mild category, and wherein a range associated with the severe category’ is higher than the range associated with the moderate category.

160. The method of claim 110, comprising: monitoring, by the at least one controller, the measure of PVA severity over time following implementation of a first measure to mitigate the detected type of PVA, determining, by the controller, based on the monitored measure of PVA severity, that one or more conditions have been met to trigger providing a recommendation to implement a second measure to mitigate the detected type of PVA. and providing the recommendation to a care provider to implement the second measure.

161. The method of claim 160, wherein satisfaction of the one or more conditions indicates that the first measure did not meet one or more specified criteria of effectiveness in mitigating the detected type of PVA over a first period of time.

162. The method of claim 161, wherein the one or more conditions comprises at least one of: a condition that the measure of PVA severity’ has not decreased during a specified period of time following implementation of the first measure, and that the measure of PVA severity has not decreased at least to a specified threshold during the specified period of time following implementation of the first measure.

163. A system for managing patient ventilator asynchrony (PVA) during mechanical ventilation therapy of a patient, the system comprising: a mechanical ventilation system for providing mechanical ventilation to the patient; and at least one controller, communicatively coupled with the mechanical ventilation system, the at least one controller configured to: obtain a detected type of PVA, determine a measure of PVA severity' for the detected type of PVA, and provide, based at least in part on the detected type of PVA and the measure of PVA severity’, in relation to one or more ventilatory parameters relating to the mechanical ventilation being provided to the patient, at least one of: one or more PVA mitigation notifications and at least one adjustment relating to the mechanical ventilation being provided to the patient, wherein providing the at least one of: one or more PVA mitigation notifications and at least one adjustment relating to the mechanical ventilation being provided to the patient comprises determining an adjustment relating to the mechanical ventilation being provided to the patient, the adjustment comprising an amount of a change to at least one of the one or more ventilatory parameters.

164. The system of claim 163, yvherein the at least one controller is configured to obtain patient data identifying one or more patient characteristics comprising at least one of: patient sex, patient age, patient height, a patient body weight related characteristic, and a patient respiratory mechanics parameter, and to provide the at least one of: one or more PVA mitigation notifications and at least one adjustment relating to the mechanical ventilation being provided to the patient based at least in part on the patient data.

165. The system of claim 163, yvherein the at least one controller is configured todetermine the measure of PVA severity comprising determining a magnitude of PVA severity for each of one or more patient breaths.

166. The system of claim 165, wherein the one or more patient breaths comprises a plurality7of patient breaths.

167. The system of claim 166, wherein the at least one controller is configured to determine the amount of the change based at least in part on the magnitude of PVA severity for each of the plurality7of patient breaths.

168. The system of claim 167, wherein the at least one controller is configured to determine the magnitude of PVA severity for each of the one or more patient breaths as a number, wherein the number is proportional to the magnitude of PVA severity.

169. The system of claim 168, wherein the at least one controller is configured to determine, based on the number, a PVA severity7score between 0 and 1, wherein the PVA severity score is proportional to the number, and to determine the amount of the change based at least in part on the PVA severity7score.

170. The system of claim 168, wherein the at least one controller is configured to determine, based on the number, a PVA severity7score between 0 and 1 , and to determine the amount of the change based at least in part on the PVA severity7score, wherein: when the number is between 0 and a specified threshold, the PVA severity7score is determined to be proportional to the number, when the number is at or above the specified threshold, the PVA severity score is determined to be 1.

171. The method of claim 165, wherein determining the magnitude of PVA severity for each of the plurality of patient breaths comprises: obtaining a first airway pressure waveform associated with a breath for which the detected type of PVA is not present, obtaining a patient airway pressure waveform associated with a patient breath of the one or more breaths for which the detected type of PVA is determined to the present. comparing the first airway pressure waveform to the patient airway pressurewaveform to determine a deviation between the first airway pressure waveform and the patient airway pressure waveform. determining a magnitude of the deviation, and based at least in part on the magnitude of the deviation, determining the magnitude of PVA severity.

172. A method for managing patient ventilator asynchrony (PVA) during mechanical ventilation therapy of a patient, the method comprising: determining presence of a type of PVA; determining a measure of PVA severity for the type of PVA determined to be present; and based at least in part on the determined measure of PVA severity, adjusting at least one ventilatory’ parameter relating to the mechanical ventilation being provided to the patient to mitigate the type of PVA determined to be present.

173. The method of claim 172, wherein the adjusting is based at least in part on patient data identifying one or more patient characteristics comprising at least one of: patient sex, patient age, patient height, a patient body weight related characteristic, and a patient respiratory mechanics parameter.

174. The method of claim 172, wherein determining the measure of PVA severity comprises determining a magnitude of PVA severity for each of one or more patient breaths.

175. The method of claim 174, wherein the one or more patient breaths comprises a plurality of patient breaths.

176. The method of claim 175, wherein the adjusting is based at least in part on the magnitude of PVA severity for each of the plurality of patient breaths.

177. The method of claim 176, comprising determining the magnitude of PVA severity for each of the plurality of patient breaths as a number, wherein the number is proportional to the magnitude of PVA severity.

178. The method of claim 177, comprising determining, based on the number, a PVAseverity score between 0 and 1, wherein the PVA severity score is proportional to the number, and wherein the adjusting is based at least in part on the PVA severity score.

179. The method of claim 177, comprising determining, based on the number, a PVA severity score between 0 and 1, wherein the adjusting is based at least in part on the PVA severity score, and wherein: when the number is between 0 and a specified threshold, the PVA severity score is determined to be proportional to the number, when the number is at or above the specified threshold, the PVA severity score is determined to be 1.

180. The method of claim 175, wherein determining the magnitude of PVA severity for each of the plurality of patient breaths comprises: obtaining a first airway pressure waveform associated with a breath for which the detected type of PVA is not present, obtaining a patient airway pressure waveform associated with a patient breath of the one or more breaths for which the detected type of PVA is determined to the present, comparing the first airway pressure waveform to the patient airway pressure waveform to determine a deviation between the first airway pressure waveform and the patient airway pressure waveform. determining a magnitude of the deviation, and based at least in part on the magnitude of the deviation, determining the magnitude of PVA severity.

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