Apparatus and method for real-time evaluation of a likelihood of lung injury during mechanical ventilation

Abstract

The present disclosure provides a method for evaluating lung injury processes during mechanical ventilation. The method involves obtaining real-time measurements of airway pressure and flow signals from a mechanically ventilated subject. Based on these measurements, the method calculates energy delivery and dissipation within the lungs, identifying energy dissipation components associated with airflow, tissue deformation, and alveolar / airway recruitment. The method further correlates these energy dissipation components with biomarkers indicative of ventilator-induced lung injury (VILI). Feedback is then provided on the mechanical ventilation settings to optimize treatment and reduce the risk of VILI. This approach enables personalized ventilation strategies, potentially improving outcomes for patients with acute respiratory distress syndrome (ARDS).

Description

Docket No.: 89558.4.WOU1APPARATUS AND METHOD FOR REAL-TIME EVALUATION OF A LIKELIHOOD OF LUNG INJURY DURING MECHANICAL VENTILATIONGovernment Support

[0001] This invention was made with government support under Grant No. R01HL142702, awarded by the National Institutes of Health. The government has certain rights in the invention.Cross-Reference to Related Application(s)

[0002] This application claims the benefit under 35 U. S. C. § 119(e) to U. S. Provisional Application 63 / 761,611, filed February 21, 2025 and entitled “APPARATUS AND METHOD FOR REAL-TIME EVALUATION OF A LIKELIHOOD OF LUNG INJURY DURING MECHANICAL VENTILATION,” which is hereby incorporated herein by reference in its entirety.Field

[0003] The present disclosure relates to the field of biomedical engineering, specifically to tools and algorithms for real-time evaluation of a likelihood of lung injury during mechanical ventilation Background

[0004] Acute Respiratory Distress Syndrome (ARDS) is a severe condition affecting a significant number of patients in intensive care units, characterized by rapid onset of widespread inflammation in the lungs. Mechanical support is an important intervention for ARDS, yet this approach carries the risk of further lung injury, which can worsen patient outcomes. Despite decades of research, mortality rates for ARDS remain high, partly due to the challenges in tailoring support strategies to individual patient needs. Traditional approaches often apply a uniform strategy, which may not adequately address the distinct physiological responses of each patient, leading to suboptimal outcomes.

[0005] Current methods for managing mechanical support focus on general lung mechanics, such as volume and pressure settings, but lack the precision needed to assess specific injury mechanisms. This limitation hinders the ability to personalize treatment effectively, as it is difficult to discern the most injurious processes contributing to further lung injury.Brief Summary

[0006] The present disclosure relates to a method for evaluating lung injury processes during mechanical ventilation, aimed at reducing the risk of ventilator-induced lung injury (VILI) in patients with acute respiratory distress syndrome (ARDS). The method involves obtaining real-time measurements from a mechanically ventilated subject, including parameters such as airflow pressure, flow signal, airflow resistance, and breath duration. These measurements are used to calculate energy dissipation within the lungs, focusing on components such as airflow resistance, tissue deformation, and alveolar / airway recruitment.

[0007] With a correlation between these energy dissipation components and biomarkers indicative of lung injury severity, such as oxygenation levels and respiratory system compliance, the techniques ofDocket No.: 89558.4.WOU1this disclosure may provide feedback is provided on the ventilation process, including mechanical ventilator settings and / or the calculated energy dissipation, offering recommendations for adjustments to minimize repetitive recruitment and derecruitment events, as well as volutrauma. The system can output alerts to medical professionals or automatically adjust ventilator settings based on the calculated energy dissipation

[0008] This approach leverages advanced algorithms and real-time data analysis to provide a personalized ventilation strategy, potentially improving patient outcomes by reducing the incidence of VI LI. The method can be implemented as an add-on device to existing ventilators or integrated directly into ventilator systems, offering a solution for managing mechanical ventilation in ARDS patients. The method provides a comprehensive framework for evaluating the likelihood of developing lung injury during mechanical ventilation by integrating real-time measurements and energy dissipation calculations, leading to more informed clinical decisions.

[0009] In Example 1 of this disclosure, a method for evaluating a likelihood of developing lung injury during mechanical ventilation comprises (a) obtaining real-time measurements from a mechanically ventilated subject, the real-time measurements including at least an airflow pressure, a flow signal, an airflow resistance during a breath, and a duration of the breath; (b) calculating energy dissipation within lungs of the mechanically ventilated subject based on the real-time measurements; and (c) providing, by one or more processors, feedback on one or more of one or more settings of a mechanical ventilator treating the mechanically ventilated subject and the calculated energy dissipation.

[0010] Example 2 relates to the method according to Example 1, wherein the airflow pressure comprises one or more of tracheal pressure and esophageal pressure.

[0011] Example 3 relates to the method according to any one or more of Examples 1-2, wherein the calculation of energy dissipation involves calculating one or more energy components, the one or more energy components comprising one or more of dissipation due to airflow resistance, dissipation due to cyclic stretching of viscoelastic tissues, dissipation due to alveolar / airway recruitment, and any combination thereof.

[0012] Example 4 relates to the method according to Example 3, further comprising identifying development of ventilator-induced lung injury (VILI) based at least in part on the dissipation due to alveolar / airway recruitment.

[0013] Example 5 relates to the method according to any one or more of Examples 3-4, further comprising identifying development of volutrauma based at least in part on the dissipation due to cyclic stretching of viscoelastic tissues.

[0014] Example 6 relates to the method according to any one or more of Examples 1-5, further comprising controlling an oscillometry device to provide a forced oscillatory flow into the mechanically ventilated subject to assist in obtaining the real-time measurements.

[0015] Example 7 relates to the method according to any one or more of Examples 1-6, wherein obtaining the airflow resistance comprises one or more of employing a forced oscillation technique to measure respiratory system impedance; deriving the airflow resistance from ventilator-measured pressure and flow waveforms; fitting measured pressure and flow data to a respiratory system model; and employing an interrupter techniqueDocket No.: 89558.4.WOU1

[0016] Example 8 relates to the method according to any one or more of Examples 1—7, wherein providing the feedback comprises outputting, by the one or more processors, one or more recommendations for adjusting one or more of the one or more settings of the mechanical ventilator to minimize volutrauma events.

[0017] Example 9 relates to the method according to any one or more of Examples 1-8, wherein providing the feedback comprises outputting, by the one or more processors, an alert to a medical professional that the mechanically ventilated subject is at risk for ventilator-induced lung injury (VILI) due to the one or more settings of the mechanical ventilator.

[0018] Example 10 relates to the method according to Example 9, wherein the alert comprises one or more of an audible alert, a visual alert, and a tactile alert.

[0019] Example 11 relates to the method according to any one or more of Examples 1-10, wherein providing the feedback comprises automatically adjusting, by the one or more processors, one or more of the one or more settings of the mechanical ventilator based on the energy dissipation

[0020] Example 12 relates to the method according to any one or more of Examples 1-11, correlating the energy dissipation within the lungs to physical functions of the lungs

[0021] Example 13 relates to the method according to any one or more of Examples 1-12, wherein correlating the energy dissipation to the physical function of the lungs comprises correlating, by the one or more processors, the energy dissipation with one or more biomarkers as indicators of lung injury severity.

[0022] Example 14 relates to the method according to Example 13, wherein the one or more biomarkers comprise oxygenation levels and respiratory system compliance.

[0023] Example 15 relates to the method according to any one or more of Examples 1-14, wherein calculating the energy dissipation within the lungs comprises: (a) determining a value indicative of the energy dissipation; (b) comparing the value to an acceptable dissipation range; and (c) in response to the value falling outside of the acceptable dissipation range, determining that the mechanical ventilator is potentially damaging the lungs.

[0024] Example 16 relates to the method according to any one or more of Examples 1-15, wherein the method is performed by the one or more processors included in a computing device operably connected to the mechanical ventilator.

[0025] Example 17 relates to the method according to any one or more of Examples 1-15, wherein the method is performed by the one or more processors integrated into the mechanical ventilator.

[0026] Example 18 related to the method according to any one or more of Examples 1-17, wherein providing the feedback comprises outputting, by the one or more processors, one or more recommendations for adjusting one or more of the one or more settings of the mechanical ventilator to minimize repetitive recruitment and derecruitment events.

[0027] In Example 19, a device is configured to perform any of the methods of any combination of Examples 1-18

[0028] In Example 20, an apparatus comprises means for performing any of the method of any combination of Examples 1-18.Docket No.: 89558.4.WOU1

[0029] In Example 21, a non-transitory computer-readable storage medium has stored thereon instructions that, when executed, cause one or more processors of a computing device to perform the method of any combination of Examples 1-18.

[0030] In Example 22, a system comprising a mechanical ventilator and one or more processors integrated into the mechanical ventilator, the one or more processors configured to perform a method of any combination of Examples 1-18.

[0031] In Example 23, a method comprises performing any of the techniques of any combination of Examples 1-18.

[0032] In Example 24, any of the techniques described herein are included.

[0033] While multiple examples are disclosed, still other examples will become apparent to those skilled in the art from the following detailed description, which shows and describes illustrative examples. As will be realized, the various implementations are capable of modifications in various obvious aspects, all without departing from the spirit and scope thereof. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not restrictive.Brief Description of the Drawings

[0034] FIG 1 is a system diagram illustrating the integration of a mechanical ventilator with a computing device for real-time evaluation of a potential lung injury during mechanical ventilation, in accordance with one or more techniques of this disclosure

[0035] FIG 2 is a block diagram illustrating a computing device configured for real-time evaluation of lung injury processes during mechanical ventilation, in accordance with one or more techniques of this disclosure.

[0036] FIG 3 is a conceptual diagram illustrating the components of energy dissipation in the lung, including airflow, tissue viscoelasticity, and alveolar / airway recruitment, in accordance with one or more techniques of this disclosure.

[0037] FIG 4 is a graph illustrating the pressure-volume loop highlighting energy dissipation components during mechanical ventilation, in accordance with one or more techniques of this disclosure.

[0038] FIG 5 is a schematic diagram of lung pressures and airflow relationships relevant to energy dissipation analysis, in accordance with one or more techniques of this disclosure

[0039] FIG 6 is a flow diagram illustrating a method for real-time evaluation of energy dissipation in mechanically ventilated subjects, in accordance with one or more techniques of this disclosure.Detailed Description

[0040] FIG 1 illustrates a system 100 designed for real-time evaluation of potential lung injury during mechanical ventilation. The system integrates several components to monitor and analyze the mechanical behavior of the lungs, providing feedback to optimize ventilator settings and reduce the risk of ventilator-induced lung injury (VILI).

[0041] In the example of FIG. 1, system 100 includes mechanical ventilator 102 Mechanical ventilator 102 is responsible for delivering controlled breaths to subject 108. Mechanical ventilator 102 regulatesDocket No.: 89558.4.WOU1parameters such as tidal volume, respiratory rate, and pressure settings to ensure adequate ventilation. While shown as separate components, in some instances, mechanical ventilator 102 may be equipped with sensors, including airflow resistance sensor 104, to monitor airflow and pressure, providing realtime data for analysis.

[0042] System 100 also includes airflow resistance sensor 104. Airflow resistance sensor 104 measures the resistance encountered by air as it flows through the airways. Airflow resistance is one of the parameters that can be used in assessing the mechanical load on the respiratory system. Airflow resistance sensor 104 represents any device, system, or method capable of determining airflow resistance in subject 108 during mechanical ventilation Airflow resistance may be obtained through any clinically validated technique, including but not limited to: forced oscillation techniques (FOT), impulse oscillometry, interrupter techniques, ventilator-derived resistance calculations, esophageal manometry-based methods, or any other approach that yields real-time or near-real-time airflow resistance values suitable for energy dissipation calculations. Airflow resistance sensor 104 provides data that can be used to calculate energy dissipation due to airflow resistance, helping to identify potential issues such as obstructions or constrictions in the airways.

[0043] Airway pressure-release ventilation 106 represents a ventilation mode that allows for the release of airway pressure during exhalation. Airway pressure-release ventilation 106 is designed to prevent overdistension of the lungs and reduce the risk of volutrauma. By modulating airway pressure, airway pressure-release ventilation 106 helps maintain optimal lung mechanics and minimize injury.

[0044] Subject 108 is the patient undergoing mechanical ventilation. System 100 is designed to monitor the subject 108's respiratory parameters continuously, providing data that can be used to assess lung function and identify potential risks of injury.

[0045] Computing device 110 includes one or more processors. Computing device 110 may analyze the data collected from airflow resistance sensor 104 and mechanical ventilator 102. Computing device 110 may run various algorithms to calculate energy delivery and dissipation within the lungs, focusing on components such as airflow resistance, tissue deformation, and alveolar / airway recruitment. Computing device 110 may provide feedback on ventilator settings, offering recommendations for adjustments to optimize treatment and reduce the risk of VI LI. Further examples of computing device 110 are described with respect to FIG. 2.

[0046] Overall, FIG. 1 depicts a comprehensive system for monitoring and managing mechanical ventilation, leveraging real-time data and advanced algorithms to enhance patient care and improve outcomes for individuals with acute respiratory distress syndrome (ARDS).

[0047] In accordance with the techniques described herein, computing device 110 may obtain realtime measurements from a mechanically ventilated subject 108, the real-time measurements including at least an airflow pressure, a flow signal, an airflow resistance during a breath, and a duration of the breath. Computing device 110 may calculate energy dissipation within lungs of the mechanically ventilated subject 108 based on the real-time measurements Computing device 110 may provide feedback on one or more of one or more settings of mechanical ventilator 102 treating the mechanically ventilated subject 108 and the calculated energy dissipation This method provides a comprehensive framework for evaluating the likelihood of developing lung injury during mechanical ventilation byDocket No.: 89558.4.WOU1integrating real-time measurements and energy dissipation calculations, leading to more informed clinical decisions.

[0048] ARDS presents a significant challenge in the field of intensive care, affecting a substantial number of patients admitted to intensive care units worldwide. The condition is characterized by severe inflammation and fluid accumulation in the lungs, leading to impaired gas exchange and respiratory failure. Mechanical ventilation is an important intervention for ARDS patients, providing necessary respiratory support. However, this intervention carries the risk of VILI, a complication that can exacerbate lung damage and increase mortality rates. The mechanisms of VILI are complex, involving factors such as overdistension of lung tissues (volutrauma) and repetitive opening and closing of alveoli (atelectrauma).

[0049] Current approaches to mechanical ventilation often employ a “one size fits all" strategy, which does not account for the individual pathophysiological differences among patients. This generalized approach can lead to suboptimal ventilation settings, increasing the risk of VILI. Traditional methods focus on controlling tidal volume and airway pressures, but these parameters do not fully capture the dynamic processes occurring within the lungs during mechanical ventilation.

[0050] The present disclosure addresses these challenges by introducing a method and system for real-time evaluation of lung injury processes during mechanical ventilation This approach provides a comprehensive analysis of energy delivery and dissipation within the lungs, focusing on the important components of airflow, tissue deformation, and alveolar / airway recruitment. By quantifying these energy dynamics, the method offers a robust technique to identify and mitigate the factors contributing to VILI. This approach enables personalized ventilation strategies tailored to the specific needs of each patient, potentially reducing the incidence of VILI and improving outcomes for individuals with ARDS

[0051] Experiments were conducted with approval from the SUNY Upstate Medical University IACUC following ARRIVE guidelines. The experimental setup is similar to the setup of FIG. 1, with pigs replacing humans as subject 108. ARDS was generated in female Yorkshire pigs (n = 40, 38.3 ± 1 8 kg) as follows. The animals were anesthetized with intravenous ketamine (90 mg / kg) and xylazine (10 mg / kg) and paralyzed with rocuronium. Next, continuous positive airway pressure (CPAP) was applied while 3% Tween-20 was instilled into the bilateral dependent basilar lung regions (0.75mL / kg into each side). Tween-20, a detergent, disrupts the natural surfactant layer, resulting in increased surface tension, alveolar collapse, and impaired gas exchange This disruption triggers an inflammatory response that damages the alveolar-capillary barrier, increasing its permeability and causing fluid leakage into the alveoli. Consequently, this damage hinders the lungs' ability to oxygenate blood and remove carbon dioxide, thereby mimicking the clinical features of ARDS.

[0052] Following injury, the animals were randomly divided into four groups OD+RD+, OD+RD-, OD-RD+ and OD-RD- according to whether they were protected from or subjected to overdistention (OD+ and OD-, respectively), and similarly for recruitment and derecruitment (RD+ and RD-, respectively). To accomplish this, the animals were ventilated with airway pressure release ventilation (APRV) because this allowed independent control of OD and RD as follows:

[0053] OD determined by inspiration pressure: (PHigh):

[0054] OD+ by setting PHigh= 40 cmH2O, leading lung overdistension,Docket No.: 89558.4.WOU1

[0055] OD- created by setting PHigh= 28 cmH2O, since this is below the plateau pressure limit suggested by clinical guidelines,

[0056] RD determined by expiration duration (TLow):

[0057] RD+ by prolonged exhalation through termination when end-expiratory flow had fallen to 25% of its early peak expiratory flow rate, resulting in TLow~1 s.

[0058] RD- created by short exhalation by terminating expiration when end-expiratory flow had fallen to only 75% of its early peak expiratory flow rate, resulting in TLow~0.5 s.

[0059] The progression of ARDS from its onset due to disease or trauma to either recovery or death is poorly understood Currently, there are no generally accepted treatments aside from supportive care using mechanical ventilation. However, this can lead to ventilator-induced lung injury (VILI), which contributes to a 30-40% mortality rate. The techniques described herein quantify forms of energy transport and dissipation during mechanical ventilation to directly evaluate their relationship to VILI. A porcine ARDS model was used, with ventilation parameters independently controlling lung overdistension and alveolar / airway recruitment / derecruitment (RD). Hourly measurements of airflow, tracheal and esophageal pressures, respiratory system impedance, and oxygen transport were taken for six hours following lung injury to track energy transfer and lung function. The final degree of injury was assessed histologically. Total and dissipated energies were quantified from lung pressure-volume relationships and subdivided into contributions from airflow, tissue viscoelasticity and RD. Only RD correlated with physiologic recovery. Despite accounting for a very small fraction (2-5%) of the total energy dissipation, RD is damaging because it occurs quickly over a very small area. It is estimated that the power intensity of RD energy dissipation to be 100W / m2, equivalent to 10% of the Sun’s luminance at the Earth’s surface. Minimizing repetitive RD events may thus be crucial for mitigating VILI.

[0060] FIG 2 is a block diagram illustrating a more detailed example of a computing device configured to perform the techniques described herein. Computing device 210 of FIG. 2 is described below as an example of computing device 110 of FIG. 1. FIG. 2 illustrates only one particular example of computing device 210, and many other examples of computing device 210 may be used in other instances and may include a subset of the components included in example computing device 210 or may include additional components not shown in FIG. 2.

[0061] Computing device 210 may be any computer with the processing power required to adequately execute the techniques described herein. For instance, computing device 210 may be an integrated computer system, such as a computer system integrated into a mechanical ventilator, a computing device with one or more processors capable of attaching to and being in communication with a mechanical ventilator, a separate computing system capable of communicating with one or more sensors integrated into the mechanical ventilator system, or any combination thereof in a distributed computing system. In other instances, computing device 210 may be any one or more of a mobile computing device (e g, a smartphone, a tablet computer, a laptop computer, etc ), a desktop computer, a smarthome component (e.g., a computerized appliance, a home security system, a control panel for home components, a lighting system, a smart power outlet, etc.), an integrated computer system, a vehicle, a wearable computing device (e.g., a smart watch, computerized glasses, a heart monitor, aDocket No.: 89558.4.WOU1glucose monitor, smart headphones, etc.), a virtual reality / augmented reality / extended reality (VR / AR / XR) system, a video game or streaming system, a network modem, router, or server system, or any other computerized device that may be configured to perform the techniques described herein.

[0062] As shown in the example of FIG. 2, computing device 210 includes user interface components (UIC) 212, one or more processors 240, one or more communication units 242, one or more input components 244, one or more output components 246, and one or more storage components 248. UIC 212 includes display component 202 and presence-sensitive input component 204. Storage components 248 of computing device 210 include communication module 220, analysis module 222, and data store 226.

[0063] One or more processors 240 may implement functionality and / or execute instructions associated with computing device 210 to determine a likelihood of a subject incurring a lung injury during mechanical ventilation. That is, processors 240 may implement functionality and / or execute instructions associated with computing device 210 to obtain real-time measurements from a mechanically ventilated subject and calculate energy dissipation in the patient in order to determine whether the mechanically ventilated subject is potentially experiencing lung injury.

[0064] Examples of processors 240 include any combination of application processors, display controllers, auxiliary processors, one or more sensor hubs, and any other hardware configured to function as a processor, a processing unit, or a processing device, including dedicated graphical processing units (GPUs). Modules 220 and 222 may be operable by processors 240 to perform various actions, operations, or functions of computing device 210. For example, processors 240 of computing device 210 may retrieve and execute instructions stored by storage components 248 that cause processors 240 to perform the operations described with respect to modules 220 and 222. The instructions, when executed by processors 240, may cause computing device 210 to determine a likelihood of a subject incurring a lung injury during mechanical ventilation.

[0065] Communication module 220 may execute locally (e.g., at processors 240) to provide functions associated with obtaining various real-time measurements from sensors or devices and outputting any determined feedback or alerts. In some examples, communication module 220 may act as an interface to a remote service accessible to computing device 210. For example, communication module 220 may be an interface or application programming interface (API) to a remote server that obtains various real-time measurements from sensors or devices and outputs any determined feedback or alerts.

[0066] In some examples, analysis module 222 may execute locally (e.g, at processors 240) to provide functions associated with calculating energy dissipation and predicting a likelihood of a subject incurring a lung injury during mechanical ventilation based on the energy dissipation. In some examples, analysis module 222 may act as an interface to a remote service accessible to computing device 210. For example, analysis module 222 may be an interface or application programming interface (API) to a remote server that calculates energy dissipation and predicts a likelihood of a subject incurring a lung injury during mechanical ventilation based on the energy dissipation

[0067] One or more storage components 248 within computing device 210 may store information for processing during operation of computing device 210 (e.g, computing device 210 may store data accessed by modules 220 and 222 during execution at computing device 210). In some examples,Docket No.: 89558.4.WOU1storage component 248 is a temporary memory, meaning that a primary purpose of storage component 248 is not long-term storage Storage components 248 on computing device 210 may be configured for short-term storage of information as volatile memory and therefore not retain stored contents if powered off. Examples of volatile memories include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories known in the art.

[0068] Storage components 248, in some examples, also include one or more computer-readable storage media. Storage components 248 in some examples include one or more non-transitory computer-readable storage mediums. Storage components 248 may be configured to store larger amounts of information than typically stored by volatile memory. Storage components 248 may further be configured for long-term storage of information as non-volatile memory space and retain information after power on / off cycles. Examples of non-volatile memories include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. Storage components 248 may store program instructions and / or information (e.g., data) associated with modules 220 and 222 and data store 226. Storage components 248 may include a memory configured to store data or other information associated with modules 220 and 222 and data store 226.

[0069] Communication channels 250 may interconnect each of the components 212, 240, 242, 244, 246, and 248 for inter-component communications (physically, communicatively, and / or operatively). In some examples, communication channels 250 may include a system bus, a network connection, an inter-process communication data structure, or any other method for communicating data.

[0070] One or more communication units 242 of computing device 210 may communicate with external devices via one or more wired and / or wireless networks by transmitting and / or receiving network signals on one or more networks. Examples of communication units 242 include a network interface card (e.g., such as an Ethernet card), an optical transceiver, a radio frequency transceiver, a GPS receiver, a radio-frequency identification (RFID) transceiver, a near-field communication (NFC) transceiver, or any other type of device that can send and / or receive information. Other examples of communication units 242 may include short wave radios, cellular data radios, wireless network radios, as well as universal serial bus (USB) controllers.

[0071] One or more input components 244 of computing device 210 may receive input Examples of input are tactile, audio, and video input. Input components 244 of computing device 210, in one example, include a presence-sensitive input device (e.g., a touch sensitive screen, a PSD), mouse, keyboard, voice responsive system, camera, microphone or any other type of device for detecting input from a human or machine. In some examples, input components 244 may include one or more sensor components (e.g., sensors 252). In some instances, sensors 252 may include any sensor or device capable of measuring any real-time measurements included in the calculation of energy dissipation described herein (e g, an airflow pressure, a flow signal, an airflow resistance during a breath, and a duration of the breath), such as an oscillometry device, a velometer, or sensors used in airflow interrupter techniques or rhinomanometry. In other examples, sensors 252 may include one or more biometric sensors (e.g., fingerprint sensors, retina scanners, vocal input sensors / microphones, facialDocket No.: 89558.4.WOU1recognition sensors, cameras), one or more location sensors (e.g., GPS components, Wi-Fi components, cellular components), one or more temperature sensors, one or more movement sensors (e.g., accelerometers, gyros), one or more pressure sensors (e.g., barometer), one or more ambient light sensors, and one or more other sensors (e.g., infrared proximity sensor, hygrometer sensor, and the like). Other sensors, to name a few other non-limiting examples, may include a radar sensor, a lidar sensor, a sonar sensor, a heart rate sensor, magnetometer, glucose sensor, olfactory sensor, compass sensor, or a step counter sensor.

[0072] One or more output components 246 of computing device 210 may generate output in a selected modality. Examples of modalities may include a tactile notification, audible notification, visual notification, machine generated voice notification, or other modalities. Output components 246 of computing device 210, in one example, include a presence-sensitive display, a sound card, a video graphics adapter card, a speaker, a cathode ray tube (CRT) monitor, a liquid crystal display (LCD), a light emitting diode (LED) display, an organic LED (OLED) display, a virtual / augmented / extended reality (VR / AR / XR) system, a three-dimensional display, or any other type of device for generating output to a human or machine in a selected modality.

[0073] UIC 212 of computing device 210 may include display component 202 and presence-sensitive input component 204. Display component 202 may be a screen, such as any of the displays or systems described with respect to output components 246, at which information (e.g., a visual indication) is displayed by UIC 212 while presence-sensitive input component 204 may detect an object at and / or near display component 202.

[0074] While illustrated as an internal component of computing device 210, UIC 212 may also represent an external component that shares a data path with computing device 210 for transmitting and / or receiving input and output For instance, in one example, UIC 212 represents a built-in component of computing device 210 located within and physically connected to the external packaging of computing device 210 (e.g., a screen on a mobile phone). In another example, UIC 212 represents an external component of computing device 210 located outside and physically separated from the packaging or housing of computing device 210 (e.g., a monitor, a projector, etc. that shares a wired and / or wireless data path with computing device 210).

[0075] UIC 212 of computing device 210 may detect two-dimensional and / or three-dimensional gestures as input from a user of computing device 210. For instance, a sensor of UIC 212 may detect a user's movement (e.g., moving a hand, an arm, a pen, a stylus, a tactile object, etc.) within a threshold distance of the sensor of UIC 212. UIC 212 may determine a two or three-dimensional vector representation of the movement and correlate the vector representation to a gesture input (e.g., a handwave, a pinch, a clap, a pen stroke, etc.) that has multiple dimensions. In other words, UIC 212 can detect a multi-dimension gesture without requiring the user to gesture at or near a screen or surface at which UIC 212 outputs information fordisplay. Instead, UIC 212 can detect a multi-dimensional gesture performed at or near a sensor which may or may not be located near the screen or surface at which UIC 212 outputs information for display.

[0076] In accordance with the techniques of this disclosure, communication module 220 may obtain real-time measurements from a mechanically ventilated subject, the real-time measurements includingDocket No.: 89558.4.WOU1at least an airflow pressure, a flow signal, an airflow resistance during a breath, and a duration of the breath. In some instances, the airflow pressure may be one or more of tracheal pressure and esophageal pressure. By including tracheal and esophageal pressure in the airflow pressure measurements, the method enhances the accuracy of the energy dissipation calculations, allowing for more precise assessment of lung injury risk

[0077] In some instances, communication module 220 may control any number of devices to obtain the real-time measurements. For airflow resistance, communication module 220 may control any device or sensor capable of measuring an airflow resistance in a subject in order to obtain the real-time measurement of airflow resistance. For example, communication module 220 may control an oscillometry device to provide a forced oscillatory flow into the mechanically ventilated subject to assist in obtaining the real-time measurements. Controlling an oscillometry device to provide forced oscillatory flow assists in obtaining accurate real-time measurements, improving the reliability of the energy dissipation calculations. In other instances, in obtaining the airflow resistance, communication module 220 may perform one or more of employing a forced oscillation technique to measure respiratory system impedance, deriving the airflow resistance from ventilator-measured pressure and flow waveform, fitting measured pressure and flow data to a respiratory system model, and employing an interrupter technique

[0078] Analysis module 222 may calculate energy dissipation within lungs of the mechanically ventilated subject based on the real-time measurements. In some instances, in calculating the energy dissipation, analysis module 222 may calculate one or more energy components, the one or more energy components including one or more of dissipation due to airflow resistance, dissipation due to cyclic stretching of viscoelastic tissues, dissipation due to alveolar / airway recruitment, and any combination thereof. The calculation of energy dissipation involving multiple energy components allows for a detailed analysis of the different factors contributing to lung injury, enabling targeted interventions to mitigate specific injury mechanisms.

[0079] In some instances, analysis module 222 may further identify development of ventilator-induced lung injury ( VI LI ) based at least in part on the dissipation due to alveolar / airway recruitment. Identifying ventilator-induced lung injury (VILI) based on alveolar / airway recruitment dissipation provides a direct link between energy dynamics and clinical outcomes, facilitating early detection and prevention of VILI.

[0080] Additionally or alternatively, analysis module 222 may identify development of volutrauma based at least in part on the dissipation due to cyclic stretching of viscoelastic tissues. Identifying volutrauma based on cyclic stretching of viscoelastic tissues allows for the differentiation of injury types, enabling more tailored ventilation strategies to minimize specific forms of lung damage.

[0081] Communication module 220 may provide feedback on one or more of one or more settings of a mechanical ventilator treating the mechanically ventilated subject and the calculated energy dissipation. In some instances, in providing the feedback, communication module 220 may output one or more recommendations for adjusting one or more of the one or more settings of the mechanical ventilator to minimize repetitive recruitment and derecruitment events. Providing feedback with recommendations for adjusting ventilator settings to minimize repetitive recruitment and derecruitment events helps reduce the risk of lung injury, enhancing patient safety. Additionally or alternatively, inDocket No.: 89558.4.WOU1providing the feedback, communication module 220 may output one or more recommendations for adjusting one or more of the one or more settings of the mechanical ventilator to minimize volutrauma events. Providing feedback to minimize volutrauma events ensures that ventilation strategies are optimized to prevent overdistension of lung tissues, reducing the likelihood of injury. Additionally or alternatively, in providing the feedback, communication module 220 may output an alert to a medical professional that the mechanically ventilated subject is at risk for ventilator-induced lung injury (VILI) due to the one or more settings of the mechanical ventilator. Outputting an alert to a medical professional about the risk of VILI due to ventilator settings enables timely intervention, potentially preventing further lung damage. In some instances, the alert may be one or more of an audible alert, a visual alert, and a tactile alert, output to devices such as a monitor, a light, a personal computing device of the physician, or any other device capable of producing such an alert. Including various alert types, such as audible, visual, and tactile alerts, ensures that medical professionals are promptly informed of potential risks, facilitating rapid response. Additionally or alternatively, in providing the feedback, communication module 220 may automatically adjust one or more of the one or more settings of the mechanical ventilator based on the energy dissipation. Automatically adjusting ventilator settings based on energy dissipation provides a proactive approach to managing ventilation, reducing the burden on medical staff and improving patient outcomes.

[0082] In some instances, analysis module 222 may further correlate the energy dissipation within the lungs to physical functions of the lungs. Correlating energy dissipation with physical lung functions offers insights into the physiological impact of ventilation, aiding in the assessment of lung health and recovery. In correlating the energy dissipation to the physical function of the lungs, analysis module 222 may correlate the energy dissipation with one or more biomarkers as indicators of lung injury severity. Correlating energy dissipation with biomarkers as indicators of lung injury severity provides a quantitative measure of injury progression, supporting more precise treatment adjustments. The one or more biomarkers may include oxygenation levels and respiratory system compliance. Using oxygenation levels and respiratory system compliance as biomarkers enhances the ability to monitor lung function and injury severity, guiding clinical decision-making.

[0083] In some instances, in calculating the energy dissipation within the lungs, analysis module 222 may determine a value indicative of the energy dissipation. Analysis module 222 may further compare the value to an acceptable dissipation range. In response to the value falling outside of the acceptable dissipation range, analysis module 222 may determine that the mechanical ventilator is potentially damaging the lungs. Calculating energy dissipation by determining a value indicative of dissipation, comparing it to an acceptable range, and identifying potential lung damage allows for early detection of harmful ventilation settings.

[0084] In some instances, computing device 210 is operably connected to the mechanical ventilator. Performing the method with processors in a computing device connected to the ventilator ensures seamless integration with existing medical equipment, facilitating widespread adoption In other instances, computing device 210 is integrated into the mechanical ventilator. Integrating the method into the mechanical ventilator streamlines the process, providing real-time feedback and adjustments without the need for additional external devices.Docket No.: 89558.4.WOU1

[0085] ARDS is a commonly fatal condition that is managed with mechanical ventilation, which can unfortunately lead to ventilator-induced lung injury (VILI). The techniques described herein explore how forms of energy delivery / dissipation from mechanical ventilation are linked to biomarkers of VILI. The techniques described herein identify energy delivery / dissipation due to airflow, tissue deformation, and alveolar / airway recruitment. Both tissue deformation and recruitment have been hypothesized to contribute to VILI. These techniques provide a quantitative method to evaluate those hypotheses and identifies recruitment as being far more injurious despite its far smaller magnitude. These techniques provides an avenue for patient-centered treatment that might reduce ARDS mortality.

[0086] ARDS presents a global health challenge, as indicated by an international study that estimated that 10% of ICU admissions experienced ARDS. While supportive care using mechanical ventilation provides crucial treatment, it carries the risk of causing ventilator-induced lung injury (VILI). Decades of research have been devoted to the reduction of VILI, leading to low-tidal volume ventilation as the standard of care for ARDS Despite more than two decades of low-tidal volume ventilation, this approach still results in ARDS mortality rates that are unacceptably high at 30-40%.

[0087] Biomechanical insult contributes to VILI, with the focus on two mechanisms: 1) volutrauma (overdistension) and / or 2) atelectrauma (alveolar recruitment / derecruitment). VILI can be linked to the energy delivered to, or dissipated within, the lungs from positive-pressure mechanical ventilation. The specific mechanisms underlying VILI are not immediately apparent from current surrogates of lung mechanics (e.g., tidal volume and plateau pressure). The techniques described herein invoke the energy transfer hypothesis to develop robust detection methods and identify markers associated with the cause of VILI.

[0088] During the ventilation cycle, energy is transferred to the lung in two forms: recoverable and dissipated. The recoverable form is returned to the environment in its entirety upon expiration, likely leaving no lasting impact on tissue structure. Recoverable energy might temporarily change the state of the tissue in a way that could facilitate injury propagation, such as by expanding elastic pores to allow transudation of plasma fluid into the air spaces, but this would not constitute tissue damage per se. Nevertheless, recoverable energy could temporarily change the state of the system in a manner that could propagate injury.

[0089] By contrast, tissue damage is a fundamentally irreversible and constitutes a dissipative process. Energy dissipation in the lung can arise from several different processes as show in FIG. 3.

[0090] Airflow resistance: Energy is dissipated as air flows between the mouth and alveoli. Bronchoconstriction due to smooth muscle contraction and airway wall thickening increases the pressure drop required for breathing by creating greater resistance to airflow, making it more difficult to move air in and out of the lungs. This is a hallmark of asthma.

[0091] Tissue-level viscoelastic responses: Energy is dissipated as alveoli stretch to accommodate volume changes during tidal breathing. Normal surfactant transport within the lining fluid that coats the interior surfaces of airways and alveoli creates dynamic surface tension that stabilizes the lung Pathologically, lung compliance is increased in emphysema and increases the work required to exhale. Conversely, surfactant deficiency and fibrosis decrease compliance and make lungs stiffer and increasing the effort needed to inhale.Docket No.: 89558.4.WOU1

[0092] Airway / Alveolar Recruitment: This occurs when airflow and parenchymal tethering work together to unblock lining fluid obstructions within airways and alveoli. This process creates crackling sounds ('rales') that can be heard during inspiration through a stethoscope.

[0093] Not all energy dissipation in the lung is injurious. Indeed, energy dissipation is an inherent feature of respiration. Airflow resistance primarily dissipates energy within the gas phase, minimizing direct tissue injury Tissue viscoelasticity contributes to energy dissipation during normal breathing as an inescapable consequence of tissue structure Dynamic surface tension contributes to energy dissipation at the air-liquid interface However, tissue overdistension can be damaging, potentially evident by altered viscoelastic responses. Furthermore, dissipation due to the recruitment of collapsed air spaces, while not significant in healthy lungs during mechanical ventilation, becomes more prevalent and significant in injured lungs because the microstresses can be highly damaging to the epithelial tissue. While surfactant plays a role in both tissue-level and recruitment dissipation, their effects differ, as detailed in the Supplement.

[0094] Separating the normal components of energy dissipation from those that are abnormal and potentially injurious is thus an important clinical problem. To address this problem, the techniques described herein evaluate energy delivery and dissipation in a mechanically ventilated porcine model of ARDS in which lung tissue overdistention (OD) and cyclic intra-tidal recruitment and derecruitment (RD) were controlled independently of each other. Quantitative comparisons between physiological response and energetics allow us to determine which sources of energy dissipation had the most significance for VILI.

[0095] FIG 3 is a conceptual diagram illustrating the components of energy dissipation in the lung, including airflow, tissue viscoelasticity, and alveolar / airway recruitment, in accordance with one or more techniques of this disclosure. FIG. 3 illustrates the conceptual components of energy dissipation in the lung during mechanical ventilation. Element 310 depicts airflow schematically, representing gas motion through conducting airways that contributes to viscous losses quantified as DairflowElement 312 represents oscillating parenchyma, indicating cyclic deformation of alveolar tissue Element 314 shows the oscillating air-liquid interface, where surfactant dynamics produce hysteresis; the interface comprises a primary surfactant layer (element 316), a secondary surfactant layer (element 318), and bulk liquid (element 320). The combined energy dissipation due to tissue viscoelasticity and surfactant transport is Dtissue, arising from cyclic stretch and interfacial phenomena. Element 324 illustrates an air bubble advancing within a collapsed airway segment (element 326), with the direction of bubble progression indicated by element 322. The localized epithelial response to the advancing interface is schematized by cell stress profiles (element 328), and the associated energy dissipation due to reopening is identified as Drecruit. Panel A emphasizes airflow dynamics; Panel B emphasizes tissue viscoelasticity and surfactant transport; Panel C emphasizes alveolar and airway recruitment. The drawing is conceptual and not to scale.

[0096] In addition to overall total input work (energy delivery) and energy dissipation, the techniques described herein focus on the three sub-components of energy dissipation in the lung illustrated in FIG.3Docket No.: 89558.4.WOU1

[0097] The first of these components (part A of FIG. 3) is due to interactions between the molecules of a viscous gas as it flows under either laminar or turbulent regimes along the conducting airways of the lung. This component (Dairflow) is calculated directly from airflow resistance calculations.

[0098] A second component of energy dissipation is due to cyclic stretching of the viscoelastic tissues of the lung combined with cyclic surfactant adsorption and desorption at the air-liquid interface (part B of FIG. 3), and is termed Dtissue. It is not possible to differentiate between the surfactant molecular component and tissue components in an air-filled lung since both components contribute to this tissue level of energy dissipation, which can be observed through the area of the quasi-steady pressurevolume (PV) loop hysteresis curves. These processes take place in a healthy lung and thus are not usually injurious. However, excessive tissue stretch can lead to over-distension injury in the same manner that any material will sustain damage in the face of sufficient strain. The resulting form of VILI is known as volutrauma, and is due to regional overdistention of lung units or airways. This necessarily involves the irreversible breakage of lung tissue structure at some level. Structural breakage is fundamentally dissipative, so volutrauma must involve some degree of energy dissipation, which will be assessed.

[0099] The third component of energy dissipation is due to recruitment of atelectatic or edematous alveoli and airways (part C of FIG. 3), and is referred to as Drecruit. It is known that this can create biomechanically significant localized stresses on the epithelial surface, and that cyclic tidal recruitment in a mechanically ventilated lung can lead to a form of VILI known as atelectrauma. Furthermore, intra-tidal recruitment is presumed to be minimal in a normal lung, and manifests to a significant degree only under pathologic conditions. These considerations make cyclic recruitment a potential suspect in the generation of injurious energy dissipation in the lungs, and will be calculated directly.

[0100] Surfactant plays a role in both Dtissue and Drecruit, but the underlying mechanisms differ significantly and can be distinguished from one another. Dtissue represents the dissipation associated with tissue stretching (part B of FIG. 3). The tissue is coated with a lining fluid containing surfactant, which exhibits dynamic surface tension due to transport processes between the bulk phase and the airliquid interface. Importantly, this hysteresis is not pathological but crucial for lung stability However, the surfactant's contribution to pressure-volume (PV) hysteresis is included in Dtissue as dynamic surface tension arises during the stretching of airways and parenchyma during ventilation. Consequently, the global behavior of surfactant is encapsulated within this component, and a systemic surfactant deficiency would be reflected in Dtissue.

[0101] In contrast, local surfactant deficiencies and liquid lining dynamics associated with ventilation can lead to fluid-structure interactions that destabilize specific lung regions. Mechanistically, this results in airway or alveolar derecruitment. The subsequent recruitment of these regions (part C of FIG. 3) dissipates energy, which is accounted for in Drecruit. and occurs exclusively during a portion of the inflation phase. This distinctive feature differentiates it from the energy dissipation associated with general tissue stretching

[0102] The techniques described herein seek to elucidate the contributions of potentially pathological phenomena — volutrauma versus atelectrauma — to ventilator-induced lung injury (VILI) WhileDocket No.: 89558.4.WOU1surfactant deficiency may influence both processes, the methods enable discrimination between the effects of these mechanisms

[0103] The experiments involved the application of engineered ventilation waveforms using airway pressure release ventilation (APRV), with physiological responses and energy delivery dissipation monitored as follows:

[0104] Ventilation Protocols: Following injury, the animals were randomly divided into four groups OD+RD+, OD+RD-, OD-RD+ and OD-RD- according to whether they were protected from or subjected to overdistention (OD+ and OD-, respectively), and for recruitment and derecruitment (RD+ and RD-, respectively).

[0105] Physiological responses were tracked by oxygenation, evaluated using the PaO2 / FiO2ratio, serves as a key indicator of physiologic respiratory health This ratio quantifies oxygen transfer in the lungs and is calculated by dividing the partial pressure of oxygen in arterial blood (PaO2, measured in mmHg) by the fraction of inspired oxygen (FiO2). A lower PaO2 / FiO2ratio indicates impaired oxygenation, commonly observed in conditions such as ARDS, pneumonia, or hypoxemic respiratory failure, and is used to guide ventilator settings A PaO2 / FiO2ratio below 300 mmHg is a diagnostic criterion for ARDS

[0106] Physicalogical responses were also tracked by respiratory mechanics, assessed using a custom oscillometry setup quantifies the biomechanical state of the respiratory tissues through the respiratory system compliance (CRS- inverse of elastance). CRS is defined as the change in lung volume per unit change in transpulmonary pressure and serves as an essential measure of lung elasticity and mechanical function. Clinically, reduced CRS is a hallmark of restrictive lung diseases, including ARDS and pulmonary fibrosis, and indicates stiff or non-compliant lungs that is used to guide ventilator management. Thus, C S is both a diagnostic and prognostic tool used to optimize respiratory support in patients with compromised lung function CRS was normalized to each animal’s value immediately following injury (HCRS).

[0107] Physiological responses were also tracked by histology, using post-mortem tissue sections from the right diaphragmatic lobe (Tween-injured tissue)

[0108] Energy delivered and dissipated within the lungs is determined by the dynamic relationship between transpulmonary pressure,and lung volume, V(t), creating a PV hysteresis loop, whose area is the total dissipated energy. The energy delivered (input work) is the integral of Ptrach(t) * V(t) during the inflation phase.

[0109] FIG 4 is a graph illustrating the pressure-volume loop highlighting energy dissipation components during mechanical ventilation, in accordance with one or more techniques of this disclosure. Schematic of a pressure-volume loop, with Ptpin blue outline, and the tissue pressurein green outline The sub-areas of these loops quantify the energy dissipation from airflow (white), tissue-level (blue hashed) and recruitment (red hashed).

[0110] FIG 4 presents a pressure-volume relationship that highlights energy dissipation components during mechanical ventilation. The horizontal axis label 410 indicates pressure in cm H2O, and the vertical axis label 412 indicates volume (relative or normalized, as defined in the specification). The transpulmonary pressure loop, Ptrach— Pesoph, is indicated by element 414, while the tissue distendingDocket No.: 89558.4.WOU1pressure loop, / tissue’ 'sindicated by element 416. Energy dissipation regions are marked as follows: airflow dissipation (element 418), tissue stretch and surfactant hysteresis (element 420), and recruitment-associated dissipation during inflation.

[0111] The custom oscillometry approach allows the determination of dynamic alveolar-level pressures during breathing (Ptissue). The subcomponents of dissipation Dairflow, Dtissueand Drecruitare shown schematically in hysteresis loops in FIG 4, with of the calculations described in the Methods and the Supplement

[0112] Physiological measurements and their relationship to ventilation scenarios. Pulmonary mechanics assessed by normalized compliance (OCRS) VS A) time (T0-T6), and B) treatment (T1-T6). Oxygenation was assessed by PaO2 / FiO2 ratio vs C) time (T0-T6), and D) treatment (T1-T6). Histology represented for PaO2 / FiO2 representative of E) OD-RD-, and F) OD+RD+. Time-course data (A and C) are represented as mean ± sem for each time point. T reatment-based representation (B and D) symbols represent T1-T6 time-point means with error bars as treatment mean ± sem. Each data point represents n = 10 experimental subjects for OD-RD- and OD+RD- and n = 9 experimental subjects for OD+RD+ and OD-RD+. ns = p > 0.05, * = p < 0.05, ** = p < 0.01. Those not defined are all p < 0.0001. Fig 3A, C reproduced with permission

[0113] In one example, say injury was induced at time TO. The techniques described herein monitor pulmonary mechanics, specifically normalized respiratory system compliance (HCRS), and oxygenation (PaO2 / FiO2) over the next 6 hours. This allowed us to assess the progression of the injury.

[0114] A post-injury increase in HCRS may occur at T1 for all animal groups, but this increase continues only for the two RD- groups. Treatment based analysis demonstrates statistical significance between RD+ and RD- groups, with HCRS significantly greater for the two RD- scenarios, and no significant influence by either the presence or absence of OD Data points represent time-point means, with error bars representing treatment mean ± sem.

[0115] Oxygenation (PaO2 / FiO2) is a clinically relevant quantitative biomarker of lung injury, with PaO2 / FiO2 < 300 mmHg indicating ARDS. Immediately after injury at TO, all animals demonstrated PaO2 / FiO2 ratios that were clinically significant for ARDS. Animals with neither OD or RD (OD-RD-) demonstrated a rapid improvement in oxygenation whereas animals with both OD and RD (OD+RD+) had sustained lung injury.

[0116] Images from the diaphragmatic lobes exposed to the Tween injury were selected from animals with a final PaO2 / FiO2 ratio reflecting the group means. These representative images demonstrate OD-RD- with relatively open alveoli, minimal edema / fibrin accrual, and no leukocyte infiltration, but with some capillary congestion. By comparison, the OD+RD+ group reveals accrual of edema with white and red blood cells within the alveolar air space. Furthermore, the OD+RD+ histology demonstrates relatively greater atelectasis with increased wall thickening.

[0117] In the energy analysis presented below, it should be recognized the ranking of the physiological recovery response to injuries ranged from most to least severe as OD+RD+, OD-RD+, OD+RD-, OD-RD-. Of particular significance is the observation that animals experiencing RD alone (OD-RD+) exhibited both significantly reduced compliance (p<0.001) and significantly greater impairment inDocket No.: 89558.4.WOU1oxygenation (p=0.0123) compared to those with OD alone (OD+RD-). This finding plays a role in identifying mechanisms of energy dissipation that could potentially contribute to VI LI.

[0118] Total Input work and total dissipation may be examined. OD, and especially the combination of RD and OD, resulted in the greatest total energy input during inspiration, but this does not consider the energy recovered during expiration. The total energy dissipated during the complete ventilation cycle is given by the PV loop hysteresis area, Dtotai. The time-course trends of input and dissipated energy are identical, although Dtotalis smaller because it does not include recoverable elastic energy stored in the lung at the end of inspiration.

[0119] The dissipated energy components sum to the values of Dtotai.

[0120] Dairflowfor the four treatment groups demonstrates trends that are nearly identical to those of Dtotal, with 76 - 88% of the total energy dissipation coming from airflow, likely due in large part to turbulent flow in the proximal airways, with a smaller contribution from laminar flow more distally.

[0121] Dtissuewas 14-24% of Dtotal, and was substantially larger in the OD+ groups compared to the OD- groups. This dissipation component occurs in all ventilated regions, though there may be heterogeneous contributions due to differences in regional expansion.

[0122] Drecruitwas only 2-5% of Dtotal. This dissipation occurs only in those regions where airway / alveolar walls are separated by a finger of air that penetrates a liquid obstruction that holds walls in apposition or by liquid plugs that propagate through the vessels.

[0123] The techniques described herein use the above data to identify a correlation between the manifestation of ARDS / VILI and the potential sources of applied energy and energy dissipation. The techniques described herein use oxygenation as the reference biomarker of lung injury, noting that it is consistent with mechanical compliance measurements and previous histological observation as shown in FIG. 3.

[0124] The physiologic rank order does not correlate with the rank order of either inspiration or total dissipated energy. In particular, the energies are less for OD-RD+ than for OD+RD-, even though OD-RD+ is more physiologically impactful than OD+RD- This correlation analysis demonstrates that neither total applied energy, nor total energy dissipation can explain the physiological impact of VILI

[0125] There exists a correlation analysis of physiological outcome to overall energy delivery and dissipation. A corresponding comparison may be made between the physiological response based on oxygenation and each of the sub-components of energy dissipation. By comparing the physiological rank order to the rank order of airflow and tissue-level dissipation, no correlation exists. In contrast, the rank order of recruitment dissipation is consistent with the physiological response rank order. From this analysis, the only component of energy dissipation that has the same rank order as the physiological response is that due to RD, implying that recruitment derecruitment dissipation is the only energy component that can be directly linked to VILI.

[0126] There exists a correlation analysis of physiological outcome to energy dissipation components. A comparison of the A) PaO2 / FiO2physiological outcome to the B) Airflow Dissipation, C) Tissue Energy Dissipation, and D) Recruitment Dissipation, respectively, show that the physiological rank-order response demonstrates that no correlation exists between the rank order of Airflow and Tissue Dissipation, indicating that these cannot be responsible for the physiological response In contrast, theDocket No.: 89558.4.WOU1physiological rank-order correlates with the rank order of Recruitment Dissipation, indicating that recruitment dissipation can be responsible for VI LI.

[0127] The correlation analysis above provides evidence that the oxygenation response is aligned only with recruitment energy dissipation. This relationship also holds for the mechanical recovery, HCRS. The statistical and temporal relationships between oxygenation and mechanics operate as a function of

[0128] There exists relationships between recruitment energy dissipation and physiological responses, demonstrating recovery only for low-levels of recruitment dissipation. Reduced recruitment energy dissipation is correlated with improved oxygenation (PaO2 / FiO2) and mechanical recovery (nCRS). First, the initial PaO2is impaired for all treatment cases, validating that the initial lung injury is independent of the treatment modality, with all groups experiencing a significant decrease in oxygenation. In comparison, a dose-dependent relationship exists betweenand oxygenation recovery. Importantly, the behavior observed at TO (initial state) to T6 improves oxygenation recovery at lower levels. A linear regression of the T6 response gives the relationship:PaO2 / FiO2mmHg= 480 mmHg − 237 Drecruit, R2= 0.32. (1)

[0129] Statistical analysis illustrated by the 95% confidence range shows that the regression slope is many standard errors from the zero-slope null hypothesis (p = 0.0001) This provides significant evidence in favor of the hypothesis that low levels ofare associated with injury recovery.

[0130] The temporal dose-dependent recovery transition. OD-RD- and OD+RD- recovery is swift at low 0.4 cmH2O*L), with significant slopes (p = 0.0001 and p = 0.009, respectively), demonstrating robust recovery. OD-RD+ provides intermediate values of(0.4 cmH2O*L < < 1 0 cmH2O*L), with recovery following a more tempered pace, but a significantly non-zero slope (p=0.009), illustrating the continuation of the dose-response behavior. Conversely, oxygenation recovery does not exist with1 cmH2O*L (OD+RD+), and thus the null hypothesis (p = 09) cannot be dismissed at large values of

[0131] Similar dose-dependent recovery patterns are observed for mechanics (HCRS). Reducedis associated with improved nCRs recovery. A linear regression of this relationship is shown as:0.7nCRS = 2.0 − Drecruit, R2= 0.47. (2)cmH2O * L

[0132] Statistical analysis illustrated by the 95% confidence range is consistent with the hypothesis that low levels of-^are associated with injury recovery (p < 0.0001).

[0133] The temporal dose-dependent recovery of OCRS in a manner equivalent to that illustrated by oxygenation. T reatments with lower< 0.4 cmH2O*L), display significant recovery with nonzero slopes (p = 0.007 and p < 0.0001, respectively). Intermediate values of(0.4 cmH2O*L < 1.0 cmH2O*L) provided by OD-RD+ indicate a positive, but potentially not significant, recovery trend (p = 0.09). Like oxygenation, hightreatment (OD+RD+,1 cmH2O*L (OD+RD+), does not exhibit significant compliance recovery, and the null hypothesis (p = 0.09) cannot be dismissed at large values of This indicates that injury continues at high levels ofDocket No.: 89558.4.WOU1

[0134] In summary, the physiological responses quantified by oxygenation and pulmonary mechanics illustrate significant dose-response relationships that correlate significantly with energy dissipation from airway / alveolar recruitment only. The techniques described herein hypothesize that this form of energy dissipation should be minimized to prevent VILI.

[0135] The injurious component of dissipated energy, that due to alveolar / airway reopening, is small. A possible explanation for the damaging nature of reopening is that it is focused on specific and very small regions of tissue. Dissipation by reopening occurs only in regions where airway / alveolar walls are held together in apposition due to surface tension, or by liquid plugs that block airflow and create a compliant collapse configuration. Energy dissipation occurs when a finger of air forces the walls apart, or the plug is removed through meniscus rupture. These are highly localized events. Furthermore, these events take place over very short time intervals because alveoli and airways transition between open and closed states in small fractions of a second. These factors potentially give rise to a high dissipation power intensity that may initially cause small areas of local damage from which injury then propagates. In contrast, dissipation due to gas flow in airways and stretch of viscoelastic tissues occur everywhere throughout the lung and so likely have smaller dissipation power intensities despite having larger overall magnitudes.

[0136] To quantify the power intensity due to recruitment, the techniques described herein consider the amount of energy dissipation from the recruitment of a single alveolus. T o evaluate dissipation per alveolar recruitment event, the techniques described herein consider the work done in inflating a single collapsed alveolus to a fully inflated sphere of radius R through application of a pressure P, given by Erecruit= P ΔV. (3)

[0137] Since Erecruitis required to overcome the surface tension forces holding the alveolus in a collapsed state, it represents an estimate of the energy dissipated to break those forces. The reopening pressure P is related to surface tension, y, and is inversely related to R following the Law of Laplace,P = — (4) R

[0138] where K ~ 10 or larger depending on the recruitment speed. Assuming a spherical alveolus, the approximate energy dissipation from a recruited alveolus isErecruit= (4 / 3)πKγR2. (5)

[0139] For a moderately elevated surface tension (y = 30 dyn / cm), and an alveolar radius in the range 5x10-3cm ≤ R ≤ 1x10-2cm, the estimate for energy dissipation from a single recruitment event is estimated as3.14 nJ / alv ≤ Erecruit≤ 12.6 nJ / alv'14^ -E™™it <12-6^?( )

[0140] where the lower bound represents the energy needed to recruit an alveolus near RV while the upper bound is the energy to recruit an alveolus near TLC.Docket No.: 89558.4.WOU1

[0141] The dissipated power is defined as Powerrecruit=E / T, where Trecruitis the recruitment time. Since the auditory spectrum for fine crackles in the lung occurs over the kilohertz range, recruitment events occur over a timescale of Trecruit~10-3s. The power dissipated by recruitment of a single alveolus is thus in the range3.14 μW / alv ≤ Powerrecruit≤ 12.6 μW / alv. (7)alv alv

[0142] This gives the recruitment dissipation power intensity for a single alveolus (that is, the rate of energy dissipation per unit area applied to the epithelial surface) asW W— = 100— (8)cm2mz

[0143] This result is independent of the timing during the breathing cycle, as recruitment at RV has a smaller dissipation energy than at TLC, but this is distributed over a smaller area as well.

[0144] For context, the techniques described herein analyze the power intensity from tissue stretch, using measurements from OD+RD+ asDstretch~ 5 cmH20 * L = 0.5 J (9)

[0145] where it is assumed that the majority of Dstretchoccurs at the alveolar level, with an alveolar surface area for a 38.3 kg pig (the average of this study) as Aalv= 93 m2. Furthermore, this dissipation occurs at end inspiration over a time range from 0.1s < tstretch< 1 s. This gives the tissue stretch dissipation power intensity asW W5.4 10“3< 5.4 10“W / m2≤ Istretch≤ W / m2(10)

[0146] In a ‘baby lung’ scenario the power intensity could be greater owing to the smaller surface area of communicating alveoli. Nevertheless, the above calculation indicates that pure stretching of tissues, while providing a larger net dissipation, provides regional power intensity that is many orders of magnitude smaller than that from highly focused recruitment events (Eq. 8).

[0147] For further context, the irradiance of sunlight onto the Earth’s surface at high noon is Isun~ 1000 W / m2, so the power intensity associated with energy dissipation due to alveolar recruitment is equivalent to that of solar energy at dawn or dusk, or on a cloudy day. Such power intensity is perhaps not normally associated with damage to the skin, but when applied to the delicate pulmonary epithelium over hours of mechanical ventilation it might well cause significant damage.

[0148] Recruitment Magnitude:

[0149] The total RD energy dissipated during a breath is the product of Erecruit(Eq. 8) and the total number of alveoli, NAlvrecruit, that become recruited. This gives(11)

[0150] Table 1 provides NAlvrecruitfor each of the four treatment modalities. It is notable that the number of recruited alveoli for the most harmful modalities (OD+RD+ and OD-RD+) is 5-10 times larger than those for the protective modalities (OD+RD- and OD-RD-). The rapid recoveries from injury seen with the OD+RD- and OD-RD- treatments suggests that there is a critical level of recurring injury thatDocket No.: 89558.4.WOU1can be endured without causing a catastrophic decline in organ-level function. The critical breakpoint is approximately 5-10 million recruitment events per cycle for the surfactant deactivation injury model examined in this study. This breakpoint may vary for different causes of ARDS. Nonetheless, the above analysis suggests that the total number of cyclically recruited alveoli per breath may be the most crucial predictor of VILI.NAlvrecruitVNAlvVrecruit%recruit(million alveoli) (ml) (ml) (%) OD+RD+ 10 - 40 20 - 40 400 5 - 10OD-RD+ 4 - 16 8 - 16 200 4 - 8OD+RD- 2.4- 12 5 - 10 200 2.5 - 5OD-RD- 0.8 - 4 1.7 -3.4 175 1 - 2Table 1: Estimated number of alveoli recruited (NAlvrecruit), and approximate volume associated with the recruited alveoli (VNAIV). The volume of the lung related to the recruitment phase (Vrecruit) allows for the estimate of the percentage of alveoli (%recruit) that are engaged in recruitment / derecruitment events.

[0151] The techniques described herein use APRV to independently control OD and RD in the lung in a porcine ARDS model and found that approximately 20% of the delivered energy was elastic energy that was recovered during exhalation, while the rest was dissipated. Of the dissipated energy, 76-88% was dissipated through airflow, 14-24% through tissue-level dissipation, and only 2-5% was dissipated through RD. Pulmonary mechanics quantified by normalized compliance (nCRS) and functional oxygenation (PaO2 / FiO2) provided biomarkers of lung injury, with the rank order of physiological outcomes from worst-to-best being: 1) OD+RD+, 2) OD-RD+, 3) OD+RD-, and 4) OD-RD- Only the measured component of energy dissipated by alveolar / airway reopening (Drecruit) correlated with this physiological response, suggesting that dissipation from alveolar / airway reopening is directly related to the generation or persistence of VILI while the other components of dissipated energy are not.

[0152] The correlation between RD and VILI is a significant result, especially given that fact that only a small fraction of energy dissipation (2-5%) is caused by these events. T o elucidate the biomechanics of this process, the power intensity of RD events is estimated using theoretical and computational analyses. Intensity from RD is approximately 100 W / m2, which is many orders of magnitude greater than that due to tissue stretch. This large power intensity results from RD being highly localized, and of very short duration, acting like an explosive event at the epithelial surface. In vitro RD studies have demonstrated direct cell damage and barrier function disruption, which can lead to cascading lung instability associated with VILI. The calculations also demonstrate the likelihood that the lung can recover if RD is reduced below a critical value. This indicates adaptive personalized ventilation that can reduce the severity of VILI and lead to recovery.

[0153] The techniques described herein use oxygenation (PaO2 / FiO2) and mechanics (HCRS) to assess injury recovery / progression as a function of treatment modality. PaO2 / FiO2is not only affected by lung injury but also potentially by cardiovascular factors. The techniques described herein showed, however, that heart rate, cardiac output, and global end-diastolic volume were similar among the treatmentDocket No.: 89558.4.WOU1groups. The techniques described herein also showed histologically that the OD+RD+ group had a significant increase in fibrin accumulation and leukocyte infiltration with a relative increase in red blood cells, whereas the OD-RD- group had a significant decrease in air space leukocytes. These complementary approaches thus support the validity of using PaO2 / FiO2 to evaluate the impact of energy delivery and dissipation on lung injury.

[0154] It should be noted that while these data suggest irreparable lung damage results from energy dissipation due to reopening of closed lung units, they cannot eliminate the impact of OD on lung injury. Studies indicate that atelectrauma is more damaging than volutrauma, and that barrier disruption during simultaneous volutrauma and atelectrauma is driven by atelectrauma. The data shows that OD combined with RD (OD+RD+) is more damaging that RD alone (OD-RD+). OD may potentiate more injurious RD by increasing the tissue tension near regions of closure, which substantially increases microscale biomechanical stresses. Furthermore, when considering the traditional approaches targeting the RD component of VI LI, such as recruitment maneuvers and the open lung approach, none have demonstrated a clear benefit and may pose risks. It is plausible that opening the lung without maintaining a sustained and stable inflation could increase localized RD energy dissipation. An alternative approach could entail a more deliberate stabilization of the lung to abrogate this highly focalized injurious component.

[0155] There are several limitations to this study that should be considered. First, the analysis relies on a calculation of airflow resistance to determine the component of energy related to dissipation from airflow, and thus to determine the quasi-steady alveolar-level PV loop. It is assumed airflow resistance is equal to Rrsand that this value remained constant throughout the ventilatory cycle. In reality, airflow resistance changes throughout the ventilation cycle. It is also assumed that the recruitment energy can be determined from the inflation limb of the alveolar PV relationship prior to its true inflection point. This region is associated with audible crackles that are indicative of recruitment, and the sharp increase in compliance in that region is also strongly suggestive of significant recruitment events. Nevertheless, the PV area determined from the linear extrapolation of the PV loop to the beginning of inspiration is only a first-order approximation of the recruitment energy dissipation, and likely is an overestimate. Likewise, the remainder of the PV loop, after subtraction of airflow and recruitment contributions, is attributed entirely to tissue-level dissipation (including energy loss from dynamic surface tension), which again is an approximation. Despite these limitations, however, the absence of a statistical difference in tissue-level dissipation between the two OD- cases, and the similarity of the two OD+ cases, suggests that the analysis of the PV relationships was a reasonable first approximation

[0156] Finally, the clinical relevance of the study may be limited by an ARDS model that involves a one-time localized delivery of a 3% Tween-20 detergent solution into only the dependent basilar lung regions, which deactivates surfactant in that region. While surfactant deactivation is a key characteristic of ARDS, this is not typical of the initiation of ARDS clinically, which may involve systemic causes such as sepsis. Nevertheless, ARDS from other causes eventually results in barrier-function dysregulation that results in surfactant deactivation, so the model recapitulates an endpoint common to ARDS in general.Docket No.: 89558.4.WOU1

[0157] While both overdistension (OD) and recruitment / de-recruitment (RD) have been broadly implicated in VILI, their precise initiation and relative contributions have remained unclear. This study introduces an impedance measurement tool and algorithm that enables the evaluation of energy delivery and dissipation. By correlating these measurements with the physiological responses of a mechanically ventilated pig model of ARDS, the techniques described herein quantify energy delivery and dissipation due to airflow, tissue deformation, and alveolar / airway recruitment. This approach elucidates the physiological significance of OD and RD, providing a clearer understanding of their roles in VILI.

[0158] The only component of energy dissipation that correlated to reduced physiological recovery was recruitment, even though only 2-5% of the total energy dissipation was attributed to that component. The techniques described herein estimate that the energy dissipation from a single recruitment event is very low (~1 OnJ). Nevertheless, the dissipation from such an event occurs over a very short timescale and is applied to a tiny surface area. This results in a dissipation power intensity estimated to be 100 W / m2The techniques described herein also found that recovery from ARDS was rapid if the total recruitment dissipation energy was low, but recovery did not occur when 5-10% of alveoli underwent repetitive recruitment / derecruitment. The findings suggest that energy dissipation due to recruitment during inspiration is the primary cause of VILI, implying that it is crucial to minimize recruitment and derecruitment events during mechanical ventilation of the injured lung. A personalized mechanical ventilation approach using the detection methods described herein may help reduce VILI and, consequently, lower the mortality rate of ARDS

[0159] In one study performed using the techniques described herein, female Yorkshire pigs (38.3 ± 1.8 kg) approximately 6 months of age were anesthetized with intravenous ketamine (90 mg / kg) and xylazine (10 mg / kg). A 7.5-mm endotracheal tube was inserted via tracheostomy and connected to a mechanical ventilator at baseline settings of a tidal volume (Vt) of 10 mL / kg, respiratory rate (RR) of 12 breaths / min, positive end-expiratory pressure (PEEP) of 5 cmH2O, and a fraction of inspired oxygen (FiO2) of 100%. Central venous catheters were placed in both external jugular veins to facilitate administration of fluids and medications. Additionally, a femoral artery catheter was placed for arterial blood pressure monitoring and blood gas (ABG) measurements.

[0160] ARDS Model: Animals were placed on continuous positive airway pressure (CPAP) equivalent to their baseline plateau pressure of 20 cmH2O. To simulate the heterogeneous pathophysiology of ARDS, a 3% Tween-20 detergent solution was precisely instilled into the bilateral dependent basilar lung regions (0.75mL / kg into each side) using a bronchoscope

[0161] The mechanical properties of the respiratory system may be monitored using any suitable technique for determining respiratory system resistance (RRS). In various implementations, respiratory system resistance may be obtained through one or more of the following approaches:

[0162] (a) Forced oscillation techniques, wherein oscillatory flow is superimposed on tidal breathing and impedance is calculated from the relationship between pressure and flow at one or more frequencies;

[0163] (b) Impulse oscillometry, wherein brief pressure pulses are applied and the resulting flow response is analyzed;Docket No.: 89558.4. WOU1

[0164] (c) Interrupter techniques, wherein brief interruptions to airflow allow estimation of alveolar pressure and resistance;

[0165] (d) Ventilator-derived measurements, wherein resistance is calculated from pressure and flow waveforms during mechanical ventilation using end-inspiratory or end-expiratory pause maneuvers;

[0166] (e) Model-based estimation, wherein respiratory mechanics parameters are estimated by fitting measured pressure and flow data to single-compartment or multi-compartment models of the respiratory system; or

[0167] (f) Any combination of the foregoing techniques.

[0168] In one example implementation, the respiratory system properties were monitored using a forced oscillation technique with a custom oscillometry system. However, the techniques of this disclosure are not limited to any particular method for obtaining respiratory system resistance, and any clinically validated approach yielding real-time or near-real-time resistance values may be employed

[0169] Every hour, the ventilator circuit underwent 10-s epochs of forced oscillatory flow, with measurements taken using a pneumotachograph forand a piezoresistive pressure sensor system for tracheal pressure, Ptrach(t) An esophageal balloon catheter was position in the mid-esophagus for the recording of esophageal pressure, Pesoph(t). The three signals were first low-pass filtered at 30 Hz and then sampled at a rate of 100 Hz with a 16-bit A / D converter. Software was used to control the driving of the speaker and acquire data. The oscillatory flow and pressure signals were then separated from the ventilator waveforms by high-pass filtering.

[0170] Respiratory system resistance (RRS) may be determined using any suitable analytical approach. In various implementations, RRS may be derived from:

[0171] (a) Frequency-domain analysis, wherein impedance is calculated from the ratio of pressure to flow spectra and fit to a respiratory system model;

[0172] (b) Time-domain analysis, wherein resistance is estimated from the relationship between instantaneous pressure and flow during breathing;

[0173] (c) Least-squares fitting of measured data to single-compartment or multi-compartment respiratory models;

[0174] (d) Machine learning or adaptive algorithms trained on respiratory mechanics data; or

[0175] (e) Any other validated computational method for estimating respiratory resistance from pressure and flow measurements.

[0176] In one example, respiratory system impedance was calculated in the frequency domain by finding the ratio of the averaged cross and auto power spectra of pressure and flow, with a singlecompartment resistance-elastance-inertance model fit to the impedance spectra. However, the techniques of this disclosure are applicable regardless of the specific method used to obtain RRS, provided the method yields values suitable for the energy dissipation calculations described herein. The single-compartment resistance-elastance-inertance model of the respiratory system having impedance, Z( ), given byZ(f) = RRS+ i(2πfIRS- ERS / 2πf) (S1)\ Znj JDocket No.: 89558.4.WOU1

[0177] was fit to the impedance spectra from 5 to19 Hz, where is respiratory system resistance, is respiratory system inertance,is respiratory system elastance, and i = +√-1. This analysis captures the general shape of Z( ) from 5 to 19 Hz.

[0178] The energy level compatibility calculations are based on alveolar recruitment (Table 1) rather than liquid plugs in airways. Using energy analysis, it is estimated that 5-10% of alveoli would need to be affected to account for the observed damage in OD+RD+ cases, corresponding to 10-40 million alveoli. Airway plug rupture was not evaluated as the primary cause of damage. If respiratory bronchioles were plugged, approximately 5-10% of terminal bronchioles would be involved. However, energy dissipation would primarily occur through alveolar reopening, with closure induced by adsorption atelectasis.

[0179] Additionally, a secondary effect of airway plug blockage onmay influence energy dissipation dynamics:

[0180] The secondary effect could contribute to additional airflow dissipation within the PV lobe region, currently attributed to recruitment. If so, a portion of the 2-5% energy dissipation inwould be airflow-related, further reducing the energy associated with recruitment. However, because most airflow dissipation occurs in larger airways, this effect is likely negligible and would not alter the overall conclusion that recruitment is linked to physiological impact This adjustment would proportionally reduce the estimated number of recruitment events in OD+RD+ cases, lowering the affected alveolar fraction from the current estimate of 5-10%

[0181] Alternatively, this effect might manifest in the loop region attributed to airflow dissipation, the largest region of the PV loop. This scenario would not alter the conclusion that RD is associated with lung injury, for the following reasons:

[0182] There is no observed correlation between airflow dissipation and physiological measurements. For instance,is lower in OD-RD+ compared to OD+RD-, even though tidal volumes are nearly identical

[0183] Airflow dissipation primarily occurs in the proximal airways, as demonstrated in previous studies and distal airway closure is unlikely to significantly impact these regions.

[0184] As such, this secondary flow effect does not alter the findings.

[0185] The techniques described herein calculatedand but notsince there is no clinical evidence for airflow causing VI LI. For completeness, it was determined that the average power intensity due to airflow as

[0186] 2 10“2< 2x 10-^,

[0187] which is far smaller than Irecruitrecruit~ 100^ observed from RD. Even if the dissipation is distributed primarily over upper airways, that region is not the observed site of damage in ARDS / VILI, and would still be several orders of magnitude smaller thanAs such, airflow dissipation is highly unlikely to cause VI LI.

[0188] Examples of Pressure vs Volume (PV) curves reveal the distinguishing characteristics of the PV loops for four treatment groups: RD-OD- (A), OD+RD- (B), OD-RD+ (C), and OD+RD+ (D). These loops were evaluated to determine the levels of energy delivery and dissipation, and their associationDocket No.: 89558.4.WOU1with physiological function. Note that there is a scale change for Relative Volume for OD+RD+. The Ptploop is represented in blue and the loop in green. The recruitment region is depicted by the red cross-hatched area.

[0189] In a tracheal pressure and flow waveforms from the four groups of animals, a power analysis was conducted using statistics from of a similar animal model. The techniques described herein thus planned for n = 10 per group, using alpha = 0.05 and power = 90% Dropout due to technical issues resulted in n = 10 for OD-RD-, n = 7 for OD-RD+, n = 9 for OD+RD- and n = 9 for OD+RD+.

[0190] Continuous monitoring of oxygen saturation, mean arterial pressure (MAP), heart rate, and temperature was performed. Arterial blood gases and electrolyte concentrations were measured every hour (pH, PaO2, PCO2, oxygen saturation, sodium, potassium, chloride, calcium, and PaO2 / FiO2ratio) Ventilatory parameters (peak inspiratory pressure, mean airway pressure, plateau pressure, tidal volume, resistance, Pnigh, PLOW, Tnigh, and TLOW) were also acquired hourly. A system provided estimates of cardiac output, extravascular lung water, and global end-diastolic volume. Driving pressure (AP) was calculated as the difference between the plateau pressure and positive end-expiratory pressure. The techniques described herein focus on oxygenation reflected in PaO2 / FiO2as a key measure of physiologic health, with PaO2 / FiO2< 300 mmHg indicating ARDS.

[0191] Post-mortem, tissue sections from the right diaphragmatic lobe (Tween-injured tissue) were excised and submerged in formalin for histopathologic analysis Quantitative evaluations from these measurements are provided in.

[0192] These experiments demonstrate that OD-RD- significantly improves oxygenation and mechanics while reducing pulmonary edema, microvascular permeability, histopathology, and white blood cell infiltration, but without compromising hemodynamics.

[0193] The mechanical properties of the respiratory system were assessed using a custom oscillometry setup Ten second epochs of forced oscillatory flow were applied hourly for 6 hours following Tween injury Airflow (V, tracheal pressure (Ptrach), and esophageal pressure (Pesoph) as an estimate of pleural pressure were measured as functions of time (t) during each epoch. Respiratory impedance (Z) as a function of frequency ( / ) was calculated from the Fourier domain ratio of pressure to flow. Z( ) was fit with a single-compartment resistance-elastance-inertance model that provided values for respiratory system resistance (RRS), elastance and inertance. The biomechanical state of the respiratory tissues was assessed by respiratory system compliance (CRS - inverse of elastance). This physiological outcome measure is reflective of respiratory system mechanics. CRS was normalized to each animal’s value immediately following injury (HCRS).

[0194] Energy calculations necessitate quantifying the time-dependent pressures within the lung. Specifically, the alveolar pressure is approximated as Paiv= Ptrach- Pair iow. where Pairfiow(t) = RRSV(t). The distending pressure at the tissue level is Ptissue(t) = Palv(t) - Pesoph(t). Details of these calculations are provided in the Supplement

[0195] FIG 5 is a schematic diagram of lung pressures and airflow relationships relevant to energy dissipation analysis. The trachea is labeled as element 510, and the thoracic cavity outline is indicated by element 512. An alveolar cluster inset is provided at element 514. Airflow vector arrows depict the direction of gas movement, and the airflow-related pressure drop Pairflow. T racheal pressure is labeledDocket No.: 89558.4.WOU1PtrachThe pressure surrounding the lung, approximated by esophageal pressure Pesophand pleural pressure. Alveolar pressure is Palv= Ptrach— Pairflow Tissue distending pressure is Ptissue= Palv— f’esoph- Transpulmonary pressure is Ptp= Ptrach- Pesoph.

[0196] The techniques described herein calculated dissipation from pressures described above. In calculating energy dissipation due to airflow, a measure of airflow resistance is employed to estimate the pressure drop across the conducting airways. In various implementations, the airflow resistance value may be: (a) approximated as equal to the total respiratory system resistance (RRS); (b) derived from partitioning total respiratory resistance into airway and tissue components; (c) measured directly using techniques specific to airflow resistance; or (d) estimated using any other clinically or experimentally validated approach. The pressure drop due to airflow may then be calculated as Pairflow(t) = Raw * V(t), where Raw represents the airflow resistance value obtained through any of the foregoing approaches. In one example implementation, RRS was used as an approximation of airflow resistance. As such, the pressure drop due to airflow is approximated asPairflowW = (S2)

[0197] and airflow dissipation is calculated asDairflow= ∫tt+TPairflowV̇ dt = ∫tt+TRRSV̇2(t)dt. (S3)

[0198] Alveolar pressure is estimated asPalv(t) = Ptrach(t) − Pairflow(t), (S4)

[0199] allowing the pressure drop across the lung tissues, Ptissue(t), to be calculated as

[0200] Ptissue(P) ~ Palv ) ~ Pesoph’ (S5)

[0201] and so the PtissueVS V loop is similar to a quasi-steady P-V loop.

[0202] Following Eqn (13),Dtissue+ Drecruit= ∫tt+TPtissue(t)V̇(t)dt. (S6)Jto

[0203] The total Input Work provided to the lung by the ventilator during inspiration is given by:Input Work = ∫VVPtrachdV = ∫tt+TPtrachdV / dt dt = ∫tt+TPtrachV̇dt, (12)Vminand Vmaxare the lung volumes at the beginning and end of inspiration, respectively The tidal volume is given by VT= Vmax- Vmin. Likewise, t0is the time of onset of inhalation, and Tinhaiais the inhalation duration.

[0204] The energy dissipated within the lungs over a single complete breath is equal to the area enclosed by the pressure-volume loop. As such, the total dissipated energy per breath, Dtotabis r rVCto+r) rto+7’ Dtotal= ∮ PtpdV = ∫V(t)V(t+T)PtpdV = ∫tt+TPtp(t)V̇dt = Dairflow+ Dtissue+ Drecruit(13)Docket No.: 89558.4.WOU1

[0205] where T is the duration of the breath (i.e., from the beginning of inspiration to the end of the subsequent expiration), and Dairfiow, Dtissueand Drecruitare the amounts of energy dissipated by airflow, tissue movement and RD, respectively.

[0206] The techniques described herein calculated Dtotalfrom the area of the PV loop using the transpulmonary pressure, Ptp(t) = Ptrach(t) - Pesoph(t). The three components of Dtotalcorrespond to distinct regions of the PV loop as illustrated in FIG. 4. These regions were identified as follows:

[0207] DairflowIt is assumed that Rrsapproximates airflow resistance, so the pressure drop across the pulmonary airways is Pairflow(t) = RrsV̇(t). The time-integral of Pairfiow t} * V(t) then determines Dairflow

[0208] DrecruitAt the acinar level, the dissipation region from airway / alveolar recruitment can be understood from the inflation limb of the PV loop. This can be represented as the dynamic transition from a de-recruited state ( / ~0) with very low compliance (a “baby acinus”), to the recruited state ( / = 1). Following the computational analysis of, the black curve represents the dynamic switch, and the shaded area reflects the recruitment energy dissipation forthat sub-acinar region.

[0209] Accordingly, the inflation limb of the experimental Ptissue(t) vs V(t) loops, where Ptissue= ((Ptrach(t) - RrsV̇(t)) - Pesoph(t) approximates the pressure drop across the lung parenchymal tissues. To calculate Drecruit, the techniques described herein identified the lower pressure inflection point, Pflex, as the start of alveolar recruitment. Drecruitwas taken as the area of the region with upward concavity. A detailed description of this area calculation is provided in the Supplement.

[0210] Dtissue: Finally, Dtissue= Dtotal− Dairflow− Drecruit.

[0211] With recruitment, f increases, and the PV relationship undergoes a dynamic transition towards a healthy acinus (black solid curve). The red hashed region reflects the energy dissipation from the transition in states Reproduced with permission.

[0212] To calculate Drecruit, the techniques described herein evaluated the inflation leg of the Ptissue vs V loop following the nomenclature in. This defines the lower Pfiexas the lower ‘knee’ of the inflation leg, and the ‘true inflection point’ from the mathematical term for inflection that refers to the point of a function where the concavity changes direction. Pflexis sometimes referred to as the lower inflection point (LIP), though it does not meet the mathematical definition of inflection.

[0213] At the acinar level, this dissipation region is understood from the dynamic transition from a derecruited state ( ~0 in red) with very low compliance (a “baby acinis”), to 2) the recruited state ( / = 1 in green). The black curve represents the dynamic switch, and the shaded area reflects the energy dissipation for that sub-acinar region. The weighted sum of these acinar contributions represent the energy dissipation that is calculated from the full P-V loop.

[0214] The techniques described herein identify the region of upward concavity in the inflation phase of the P-V loop as the location of alveolar recruitment, and estimate the dissipation energy from the area of that region. The following algorithm was followed:

[0215] The inflation limb of the PV loop was defined as the region where dV / dPtissue> 0.

[0216] This inflation limb was low-pass filtered with an empirically determined cut-off frequency of 2Hz and a Steepness = 0.5 that sets the transition width of 24Hz.Docket No.: 89558.4.WOU1

[0217] The filtered inflation limb was numerically differentiated, and inflection points identified as those for which d²V / dPtissue² = 0. The techniques described herein select the ‘true inflection point’ as the numerical inflection point that exists for Ptissue> Pftexwith dV / dPtissue> 0. This latter condition is important so that theselected portion includes all recruitment regions, since there are rare occasions where multiple inflection points can occur from quick pressure decreases due to a temporary rapid increase in compliance.

[0218] A secant line was connected between the inflection point to the beginning of the inflation limb.

[0219] The area between this secant line and the unfiltered Ptpvs V inflation limb gives Drecruit.

[0220] Finally, the value of Drecruitestimated above was substituted into in Eq. (S6) to provide a value for D tissue

[0221] FIG 6 is a flow diagram illustrating a method for real-time evaluation of energy dissipation in mechanically ventilated subjects, in accordance with one or more techniques of this disclosure The techniques of FIG 6 may be performed by one or more processors of a computing device, such as system 100 of FIG. 1 and / or computing device 210 illustrated in FIG. 2. For purposes of illustration only, the techniques of FIG. 6 are described within the context of computing device 210 of FIG. 2, although computing devices having configurations different than that of computing device 210 may perform the techniques of FIG 6

[0222] In accordance with the techniques of this disclosure, communication module 220 obtains realtime measurements from a mechanically ventilated subject (602). The real-time measurements may include at least an airflow pressure, a flow signal, an airflow resistance during a breath, and a duration of the breath. Analysis module 222 calculates energy dissipation within lungs of the mechanically ventilated subject based on the real-time measurements (604). Communication module 220 provides feedback on one or more of one or more settings of a mechanical ventilator treating the mechanically ventilated subject and the calculated energy dissipation (606)

[0223] Although the various examples have been described with reference to preferred implementations, persons skilled in the art will recognize that changes may be made in form and detail without departing from the spirit and scope thereof.

[0224] It is to be recognized that depending on the example, certain acts or events of any of the techniques described herein can be performed in a different sequence, may be added, merged, or left out altogether (e.g., not all described acts or events are necessary for the practice of the techniques). Moreover, in certain examples, acts or events may be performed concurrently, e g, through multithreaded processing, interrupt processing, or multiple processors, rather than sequentially

[0225] In one or more examples, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include computer-readable storage media, which corresponds to a tangible medium such as data storage media, or communication media including any medium that facilitates transfer of a computer program from one place to another, e.g, according to a communication protocol. In this manner, computer-readable media generally may correspond to (1) tangible computer-readable storage media which is non-transitory or (2) aDocket No.: 89558.4.WOU1communication medium such as a signal or carrier wave. Data storage media may be any available media that can be accessed by one or more computers or one or more processors to retrieve instructions, code and / or data structures for implementation of the techniques described in this disclosure. A computer program product may include a computer-readable medium.

[0226] It is contemplated that the various aspects, features, processes, and operations from the various embodiments may be used in any of the other embodiments unless expressly stated to the contrary. Certain operations illustrated may be implemented by a computer executing a computer program product on a non-transient, computer-readable storage medium, where the computer program product includes instructions causing the computer to execute one or more of the operations, or to issue commands to other devices to execute one or more operations.

[0227] By way of example, and not limitation, such computer-readable storage media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage, or other magnetic storage devices, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium For example, if instructions are transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. It should be understood, however, that computer-readable storage media and data storage media do not include connections, carrier waves, signals, or other transitory media, but are instead directed to non-transitory, tangible storage media. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0228] Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor,” as used herein may refer to any of the foregoing structures or any other structure suitable for implementation of the techniques described herein. In addition, in some aspects, the functionality described herein may be provided within dedicated hardware and / or software modules configured for encoding and decoding, or incorporated in a combined codec. Also, the techniques could be fully implemented in one or more circuits or logic elements.

[0229] The techniques of this disclosure may be implemented in a wide variety of devices or apparatuses, including a wireless handset, an integrated circuit (IC) or a set of IDs (e.g., a chip set). Various components, modules, or units are described in this disclosure to emphasize functional aspects of devices configured to perform the disclosed techniques, but do not necessarily require realization by different hardware units. Rather, as described above, various units may be combined in a codec hardware unit or provided by a collection of interoperative hardware units, including one or more processors as described above, in conjunction with suitable software and / or firmware.Docket No.: 89558.4.WOU1

[0230] Various embodiments of the invention may be implemented at least in part in any conventional computer programming language. For example, some embodiments may be implemented in a procedural programming language (e.g., “C”), or in an object oriented programming language (e.g., “C++”). Other embodiments of the invention may be implemented as a pre-configured, stand-alone hardware element and / or as preprogrammed hardware elements (e.g., application specific integrated circuits, FPGAs, and digital signal processors), or other related components.

[0231] Those skilled in the art should appreciate that such computer instructions can be written in a number of programming languages for use with many computer architectures or operating systems. Furthermore, such instructions may be stored in any memory device, such as semiconductor, magnetic, optical or other memory devices, and may be transmitted using any communications technology, such as optical, infrared, microwave, or other transmission technologies.

[0232] Among other ways, such a computer program product may be distributed as a removable medium with accompanying printed or electronic documentation (e.g., shrink wrapped software), preloaded with a computer system (e.g., on system ROM or fixed disk), or distributed from a server or electronic bulletin board over the network (e.g., the Internet or World Wide Web). In fact, some embodiments may be implemented in a software-as-a-service model (“SAAS”) or cloud computing model. Of course, some embodiments of the invention may be implemented as a combination of both software (e.g., a computer program product) and hardware. Still other embodiments of the invention are implemented as entirely hardware, or entirely software

[0233] While the various systems described above are separate implementations, any of the individual components, mechanisms, or devices, and related features and functionality, within the various system embodiments described in detail above can be incorporated into any of the other system embodiments herein.

[0234] The terms “about” and “substantially,” as used herein, refers to variation that can occur (including in numerical quantity or structure), for example, through typical measuring techniques and equipment, with respect to any quantifiable variable, including, but not limited to, mass, volume, time, distance, wave length, frequency, voltage, current, and electromagnetic field. Further, there is certain inadvertent error and variation in the real world that is likely through differences in the manufacture, source, or precision of the components used to make the various components or carry out the methods and the like. The terms “about” and “substantially” also encompass these variations The term “about” and “substantially” can include any variation of 5% or 10%, or any amount - including any integer -between 0% and 10%. Further, whether or not modified by the term “about” or “substantially,” the claims include equivalents to the quantities or amounts.

[0235] Numeric ranges recited within the specification are inclusive of the numbers defining the range and include each integer within the defined range. Throughout this disclosure, various aspects of this disclosure are presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the disclosure. Accordingly, the description of a range should be considered to have specifically disclosed all the possible sub-ranges, fractions, and individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specificallyDocket No.: 89558.4.WOU1disclosed sub-ranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6, and decimals and fractions, for example, 1.2, 3.8, 1½, and 4¾ This applies regardless of the breadth of the range. Although the various embodiments have been described with reference to preferred implementations, persons skilled in the art will recognize that changes may be made in form and detail without departing from the spirit and scope thereof.

[0236] Various examples of the disclosure have been described. Any combination of the described systems, operations, or functions is contemplated These and other examples are within the scope of the following claims.

Claims

Docket No.: 89558.4.WOU1ClaimsWhat is claimed is:

1. A method for evaluating a likelihood of developing lung injury during mechanical ventilation, comprising:(a) obtaining, by one or more processors, real-time measurements from a mechanically ventilated subject, the real-time measurements including at least an airflow pressure, a flow signal, an airflow resistance during a breath, and a duration of the breath;(b) calculating, by the one or more processors, energy dissipation within lungs of the mechanically ventilated subject based on the real-time measurements; and(c) providing, by the one or more processors, feedback on one or more of one or more settings of a mechanical ventilator treating the mechanically ventilated subject and the calculated energy dissipation.

2. The method of claim 1, wherein the airflow pressure comprises one or more of tracheal pressure and esophageal pressure.

3. The method of claim 1, wherein calculating the energy dissipation comprises calculating one or more energy components, the one or more energy components comprising one or more of dissipation due to airflow resistance, dissipation due to cyclic stretching of viscoelastic tissues, dissipation due to alveolar / airway recruitment, and any combination thereof4. The method of claim 3, further comprising identifying development of ventilator-induced lung injury (VILI) based at least in part on the dissipation due to alveolar / airway recruitment.

5. The method of claim 3, further comprising identifying development of volutrauma based at least in part on the dissipation due to cyclic stretching of viscoelastic tissues.

6. The method of claim 1, further comprising controlling an oscillometry device to provide a forced oscillatory flow into the mechanically ventilated subject to assist in obtaining the realtime measurements.Docket No.: 89558.4.WOU17. The method of claim 1, wherein obtaining the airflow resistance comprises one or more of:(i) employing a forced oscillation technique to measure respiratory system impedance;(ii) deriving the airflow resistance from ventilator-measured pressure and flow waveforms;(iii) fitting measured pressure and flow data to a respiratory system model; and (iv) employing an interrupter technique.

8. The method of claim 1, wherein providing the feedback comprises outputting, by the one or more processors, one or more recommendations for adjusting one or more of the one or more settings of the mechanical ventilator to minimize volutrauma events.

9. The method of claim 1, wherein providing the feedback comprises outputting, by the one or more processors, an alert to a medical professional that the mechanically ventilated subject is at risk for ventilator-induced lung injury (VILI) due to the one or more settings of the mechanical ventilator.

10. The method of claim 9, wherein the alert comprises one or more of an audible alert, a visual alert, and a tactile alert.

11. The method of claim 1, wherein providing the feedback comprises automatically adjusting, by the one or more processors, one or more of the one or more settings of the mechanical ventilator based on the energy dissipation.

12. The method of claim 1, correlating the energy dissipation within the lungs to physical functions of the lungs13. The method of claim 1, wherein correlating the energy dissipation to the physical function of the lungs comprises correlating, by the one or more processors, the energy dissipation with one or more biomarkers as indicators of lung injury severity.

14. The method of claim 13, wherein the one or more biomarkers comprise oxygenation levels and respiratory system complianceDocket No.: 89558.4.WOU115. The method of claim 1, wherein calculating the energy dissipation within the lungs comprises:(a) determining a value indicative of the energy dissipation;(b) comparing the value to an acceptable dissipation range; and (c) in response to the value falling outside of the acceptable dissipation range, determining that the mechanical ventilator is potentially damaging the lungs.

16. The method of claim 1, wherein the method is performed by the one or more processors included in a computing device operably connected to the mechanical ventilator.

17. The method of claim 1, wherein the method is performed by the one or more processors integrated into the mechanical ventilator.

18. The method of claim 1, wherein providing the feedback comprises outputting, by the one or more processors, one or more recommendations for adjusting one or more of the one or more settings of the mechanical ventilator to minimize repetitive recruitment and derecruitment events.

19. A computing device comprising one or more processors configured to:(a) obtain real-time measurements from a mechanically ventilated subject, the real-time measurements including at least an airflow pressure, a flow signal, an airflow resistance during a breath, and a duration of the breath;(b) calculate energy dissipation within lungs of the mechanically ventilated subject based on the real-time measurements; and(c) provide feedback on one or more of one or more settings of a mechanical ventilator treating the mechanically ventilated subject and the calculated energy dissipation.Docket No.: 89558.4.WOU120. A system comprising:(a) a mechanical ventilator treating a mechanically ventilated subject; and (b) one or more processors integrated into the mechanical ventilator, the one or more processors configured to:(i) obtain real-time measurements from the mechanically ventilated subject, the real-time measurements including at least an airflow pressure, a flow signal, an airflow resistance during a breath, and a duration of the breath;(ii) calculate energy dissipation within lungs of the mechanically ventilated subject based on the real-time measurements; and (iii) provide feedback on one or more of one or more settings of the mechanical ventilator treating the mechanically ventilated subject and the calculated energy dissipation.

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