Therapeutic scoring for the state of blood dynamics

Abstract

A system for monitoring a patient's arterial pressure determines a score predictive of the patient's responsiveness to therapy. Sensed hemodynamic data representing the patient's arterial pressure waveform is received by a hemodynamic monitoring device. Magnitude data and trend data are derived from the hemodynamic data. A score predictive of the patient's responsiveness to therapy is determined based on the magnitude data and trend data of the hemodynamic parameters. A representation of the score is output.

Description

Technical Field

[0001] The present disclosure generally relates to arterial blood pressure monitoring, and more specifically to the determination of one or more scores, each score predicting a patient's responsiveness to a particular therapy.

Background Art

[0002] The waveform signal of arterial blood pressure is often used for the determination of hemodynamic parameters such as cardiac output (CO), systemic vascular resistance (SVR), stroke volume (SV), stroke volume variation (SVV), pulse pressure variation (PPV), stroke volume index (SVI), cardiac index (CI), systemic vascular resistance index (SVRI), the maximum rate of rise of arterial pressure (often referred to as dP / dt max ), or other hemodynamic parameters that can be used to monitor and / or predict important physiological events. For example, an increase in SVV can indicate a decrease in cardiac preload. When cardiac preload decreases, SV and CO may decrease, and arterial blood pressure may decrease. Similarly, a decrease in cardiac afterload can be indicated by a decrease in SVR, while a decrease in dP / dt max can indicate a decrease in myocardial contractility. Such conditions, if left untreated, can pose a risk of prognosis such as organ perfusion disorders, irreversible ischemic disorders, neurological deficits, cardiomyopathy, renal dysfunction, or other serious medical conditions in surgical patients or critically ill patients.

[0003] Therefore, few hospitals do not have some device for monitoring one or more of these hemodynamic parameters. For example, in the operating room during major surgery, an anesthesiologist or other clinician often has the task of maintaining the patient's mean arterial pressure to ensure proper perfusion of organs and peripheral tissues. Therefore, the participating clinicians should closely monitor such hemodynamic parameters to determine whether administration of a therapeutic agent is indicated.

Summary of the Invention

Means for Solving the Problems

[0004] In one example, a method for monitoring a patient's arterial pressure and determining a score for predicting the patient's responsiveness to therapy includes receiving, by a hemodynamic monitoring device, sensed hemodynamic data representative of the patient's arterial pressure waveform. The method further includes deriving, by the hemodynamic monitoring device, magnitude data and trend data of hemodynamic parameters from the hemodynamic data, and determining, by the hemodynamic monitoring device, a score for predicting the patient's responsiveness to therapy based on the magnitude data and trend data of the hemodynamic parameters. The method further includes outputting, by the hemodynamic monitoring device, a representation of the score.

[0005] In another example, a system for monitoring a patient's arterial pressure and determining a score for predicting the patient's responsiveness to therapy includes a hemodynamic sensor, a system memory, a user interface, and a hardware processor. The hemodynamic sensor generates hemodynamic data representative of the patient's arterial pressure waveform. The system memory stores hemodynamic therapy scoring software code. The hardware processor is configured to execute the hemodynamic therapy scoring software code to derive magnitude data and trend data of hemodynamic parameters from the hemodynamic data representative of the patient's arterial pressure waveform. The hardware processor is further configured to execute the hemodynamic therapy scoring software code to determine a score for predicting the patient's responsiveness to therapy based on the magnitude data and trend data of the hemodynamic parameters and output a representation of the score via the user interface. BRIEF DESCRIPTION OF THE DRAWINGS

[0006]

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DETAILED DESCRIPTION OF THE INVENTION

[0007] As described herein, a hemodynamic monitoring system provides one or more quantitative metrics that can improve decision support for healthcare providers to perform timely and effective therapeutic interventions by determining one or more scores that predict a patient's responsiveness to a corresponding therapy. A hemodynamic monitoring system implementing the techniques of the present disclosure determines one or more scores as a combination of magnitude data and trend data derived from corresponding hemodynamic parameters. Thus, to more accurately predict a patient's current and future responsiveness to a corresponding therapy, one or more scores are determined based on both the values of the corresponding hemodynamic parameters and, for example, the trends over time of these hemodynamic parameters.

[0008] FIG. 1 is a perspective view of a hemodynamic monitoring device 10 that determines at least one score predicting a patient's responsiveness to a therapy. As shown in FIG. 1, the hemodynamic monitoring device 10 can present a display 12 that presents a graphical user interface including control elements (e.g., graphical control elements) that enable a user to interact with the hemodynamic monitoring device 10, as well as output display elements for presenting information to the user, such as at least one score predicting a patient's responsiveness to a corresponding therapy. The hemodynamic monitoring device 10 can include a plurality of input and / or output (I / O) connectors configured for wired connection (e.g., electrical connection and / or communication connection) to one or more peripheral components, such as one or more hemodynamic sensors described further below. For example, as shown in FIG. 1, the hemodynamic monitoring device 10 can include an I / O connector 14. Although the example of FIG. 1 shows five individual I / O connectors 14, it should be understood that in other examples, the hemodynamic monitoring device 10 can include fewer than five I / O connectors or six or more I / O connectors. In yet another example, the hemodynamic monitoring device 10 can communicate wirelessly with various peripheral devices without including the I / O connector 14.

[0009] As further described below, the hemodynamic monitoring device 10 includes one or more processors and a computer-readable memory storing hemodynamic therapy scoring software code. The hemodynamic therapy scoring code is executable to generate at least one score predicting a patient's responsiveness to therapy. For example, the hemodynamic monitoring device 10 can receive sensed hemodynamic data representing a patient's arterial pressure waveform, such as from one or more hemodynamic sensors connected to the hemodynamic monitoring device 10 via the I / O connector 14. The hemodynamic monitoring device 10 executes the hemodynamic therapy scoring software code to derive at least one hemodynamic parameter that may indicate the patient's potential hemodynamic state. For example, a decrease in cardiac preload can lower stroke volume, cardiac output, and arterial blood pressure, leading to organ perfusion impairment, which may be indicated by an increase in stroke volume variation (SVV) and can be treated by therapeutic delivery of a fluid such as saline (e.g., intravenous delivery). A decrease in cardiac afterload can lead to hypotension and potential defects in organ perfusion, which may be indicated by a decrease in systemic vascular resistance (SVR) and can be treated by therapeutic delivery of a vasopressor (i.e., vasoconstrictor). A decrease in myocardial contractility can lower ventricular ejection, cardiac output, and arterial blood pressure, leading to organ perfusion impairment, which may be indicated by a decrease in the maximum rate of rise of arterial pressure (often referred to as dP / dt max and can be treated by therapeutic delivery of an inotropic agent.

[0010] The hemodynamic monitoring device 10 executes the hemodynamic therapy scoring software code to determine one or more scores predicting the patient's responsiveness to the corresponding therapy. The one or more scores include, for example, a fluid therapy score indicating a decrease in cardiac preload (e.g., indicated by an increase in SVV) and predicting the patient's responsiveness to therapeutic delivery of a fluid, a vasopressor therapy score indicating a decrease in cardiac afterload (e.g., indicated by a decrease in SVR) and predicting the patient's responsiveness to therapeutic delivery of a vasopressor, a decrease in myocardial contractility (e.g., dP / dt maxIt can include a circulatory agent therapy score, or other scores, that indicate (as indicated by the decrease) and predict the patient's responsiveness to the therapeutic delivery of the circulatory agent.

[0011] As further described below, the hemodynamic monitoring device 10 can determine one or more scores as a combination of magnitude data and trend data derived from corresponding hemodynamic parameters. Thus, the hemodynamic monitoring device 10 can determine one or more scores based on both the value of the corresponding hemodynamic parameter and, for example, one or more trends over time of this hemodynamic parameter, in order to more accurately predict the patient's responsiveness to the corresponding therapy. The hemodynamic monitoring device 10 further executes hemodynamic therapy scoring software code to output a representation of the one or more scores. For example, as further described below, the representation of the score can include, for example, the display of the score on the display 12 of the hemodynamic monitoring device 10, the color coding of the score, the symbolic representation of the score, the auditory representation of the score, or other representations of the score.

[0012] Therefore, the hemodynamic monitoring device 10 implementing the technology of the present disclosure provides one or more scores for predicting the patient's responsiveness to the corresponding therapy, and can assist a clinician or other healthcare provider in determining timely and effective therapeutic measures for the indicated hemodynamic state, while reducing the information processing burden on the clinician, for example, during surgery or other clinical events.

[0013] FIG. 2 is a perspective view of a hemodynamic sensor 16 that can be attached to a patient to sense hemodynamic data representing the patient's arterial pressure. The hemodynamic sensor 16 shown in FIG. 2 is an example of a minimally invasive hemodynamic sensor and can be attached to the patient, for example, via a radial artery catheter inserted into the patient's arm. In other examples, the hemodynamic sensor 16 can be attached to the patient via a femoral artery catheter inserted into the patient's leg.

[0014] As shown in FIG. 2, the hemodynamic sensor 16 includes a housing 18, a fluid input port 20, a catheter-side fluid port 22, and an I / O cable 24. The fluid input port 20 is configured to be connected by a tube or other hydraulic connection to a fluid source such as a saline bag or other fluid input source. The catheter-side fluid port 22 is configured to be connected by a tube or other hydraulic connection to a catheter inserted into the patient's arm (i.e., a radial artery catheter) or a catheter inserted into the patient's leg (i.e., a femoral artery catheter). The I / O cable 24 is configured to be connected to the hemodynamic monitoring device 10 by, for example, one or more of the I / O connectors 14 (FIG. 1). The housing 18 of the hemodynamic sensor 16 surrounds one or more pressure transducers, communication circuits, processing circuits, and corresponding electronic components for sensing a fluid pressure corresponding to the arterial pressure of the patient transmitted to the hemodynamic monitoring device 10 (FIG. 1) by the I / O cable 24.

[0015] In operation, a column of fluid (e.g., saline) is introduced from a fluid source (e.g., a saline bag), through the fluid input port 20, through the hemodynamic sensor 16, and toward the catheter-side fluid port 22 to a catheter inserted into the patient. The arterial pressure is transmitted through the fluid column to a pressure sensor within the housing 16 that senses the pressure of the fluid column. The hemodynamic sensor 16 converts the sensed pressure of the fluid column into an electrical signal by a pressure transducer and outputs the corresponding electrical signal to the hemodynamic monitoring device 10 (FIG. 1) through the I / O cable 24. Thus, the hemodynamic sensor 16 transmits to the hemodynamic monitoring device 10 (FIG. 1) analog sensor data (or a digital representation of the analog sensor data) representative of substantially continuous monitoring of the patient's arterial pressure between heartbeats.

[0016] FIG. 3 is a perspective view of a hemodynamic sensor 26 for sensing hemodynamic data representative of a patient's arterial pressure. The hemodynamic sensor 26 shown in FIG. 3 is an example of a non-invasive hemodynamic sensor and can be attached to a patient by one or more finger cuffs to sense data representative of the patient's arterial pressure. As shown in FIG. 3, the hemodynamic sensor 26 includes an inflatable finger cuff 28 and a cardiac reference sensor 30. The inflatable finger cuff 28 includes an inflatable blood pressure bladder configured to expand and contract under the control of a pressure regulator (not shown) pneumatically connected to the inflatable finger cuff 28. The inflatable finger cuff 28 also includes a light (e.g., infrared) transmitter and a light receiver electrically connected to the cardiac reference sensor 30 for measuring the changing volume of the artery of the finger.

[0017] In operation, the pressure controller continuously adjusts the pressure within the finger cuff to maintain a constant volume of the artery of the finger (i.e., the volume of the artery at no load) measured by the cardiac reference sensor 30 of the inflatable finger cuff 28 via the light transmitter and the light receiver. The pressure applied by the pressure controller to continuously maintain the volume at no load represents the blood pressure in the finger and is communicated by the pressure controller to the cardiac reference sensor 30. The cardiac reference sensor 30 converts a pressure signal representative of the blood pressure in the finger into hemodynamic data representative of the patient's arterial pressure waveform, which is transmitted to the hemodynamic monitoring device 10 (FIG. 1) through, for example, the I / O connector 14 (FIG. 1). Thus, the hemodynamic sensor 26 transmits sensor data representative of substantially continuous inter-beat monitoring of the patient's arterial pressure.

[0018] FIG. 4 is a block diagram of a hemodynamic monitoring system 32 that determines one or more scores predicting a patient's responsiveness to a corresponding therapy. As shown in FIG. 4, the hemodynamic monitoring system 32 includes a hemodynamic monitoring device 10 and a hemodynamic sensor 34. The hemodynamic monitoring system 32 can be implemented inside a patient treatment environment, such as an ICU, an OR, or other patient treatment environments. As shown in FIG. 4, the patient treatment environment can include a patient 36 and a healthcare provider 38 trained to utilize the hemodynamic monitoring system 32.

[0019] The hemodynamic monitoring device 10 can be an integrated hardware unit that includes, for example, a system processor 40, a system memory 42, a display 12, and an analog-to-digital converter (ADC) 44, as described above with reference to FIG. 1. In other examples, one or more components of the hemodynamic monitoring device 10 and / or any of the described functions can be distributed among multiple hardware units. For example, in some instances, the display 12 can be an individual display device operably coupled remotely to the hemodynamic monitoring device 10. Generally, although the hemodynamic monitoring device 10 is illustrated and described as an integrated hardware unit in the example of FIG. 4, it should be understood that this specification can include any combination of devices and components that are electrically connected, communicatively connected, or operably connected to perform the functions attributed to the hemodynamic monitoring device 10.

[0020] As shown in FIG. 4, the system memory 42 stores hemodynamic therapy scoring software code 48. The hemodynamic therapy scoring software code 48 includes a hemodynamic parameter module 50 and a therapy scoring module 52. The display 12 provides a user interface 54, and the user interface 54 includes control elements 56 that enable a user to interact with the hemodynamic monitoring device 10 and / or other components of the hemodynamic monitoring system 32. As shown in FIG. 4, the user interface 54 can also provide a graphical and / or audible output 58 that displays or presents one or more scores to a healthcare provider (e.g., healthcare worker 38) for predicting a patient 36's responsiveness to a corresponding therapy.

[0021] The hemodynamic sensor 34 can be attached to the patient 36 and senses hemodynamic data representing the patient 36's arterial pressure waveform. The hemodynamic sensor 34 is operably connected to the hemodynamic monitoring device 10 (e.g., electrically and / or communicatively connected by one or both of a wired connection or a wireless connection) and supplies the sensed hemodynamic data to the hemodynamic monitoring device 10. In some examples, hemodynamic data representing the patient 36's arterial pressure waveform is supplied to the hemodynamic monitoring device 10 as an analog signal by the hemodynamic sensor 34 and is converted to digital hemodynamic data representing the arterial pressure waveform by the ADC 44. In other examples, the hemodynamic sensor 34 can supply the sensed hemodynamic data to the hemodynamic monitoring device 10 in digital form, in which case the hemodynamic monitoring device 10 may not include or utilize the ADC 44. In yet other examples, hemodynamic data representing the patient 36's arterial pressure waveform can be supplied to the hemodynamic monitoring device 10 as an analog signal by the hemodynamic sensor 34 and analyzed by the hemodynamic monitoring device 10 in an analog manner.

[0022] The hemodynamic sensor 34 can be a non-invasive or minimally invasive sensor that is attached to the patient 36. For example, the hemodynamic sensor 34 can take the form of the minimally invasive hemodynamic sensor 16 (FIG. 2), the non-invasive hemodynamic sensor 26 (FIG. 3), or other minimally invasive or non-invasive hemodynamic sensors. In some examples, the hemodynamic sensor 34 can be non-invasively attached to an extremity such as the wrist, arm, finger, ankle, toe, or other extremity of the patient 36. Thus, the hemodynamic sensor 34 can take the form of a small, lightweight, and comfortable hemodynamic sensor suitable for long-term wear by the patient 36, and monitor the substantially continuous inter-beat intervals of the patient 36's arterial pressure over a long period such as several minutes or hours.

[0023] In a particular example, the hemodynamic sensor 34 can be configured to sense the arterial pressure of the patient 36 in a way that minimizes invasiveness. For example, the hemodynamic sensor 34 can be attached to the patient 36 by a radial artery catheter inserted into the arm of the patient 36. In other examples, the hemodynamic sensor 34 can be attached to the patient 36 via a femoral artery catheter inserted into the leg of the patient 36. The hemodynamic sensor 34 can similarly monitor the substantially continuous inter-beat intervals of the patient 36's arterial pressure over a long period such as several minutes or hours by such minimally invasive techniques.

[0024] The system processor 40 is configured to execute hemodynamic therapy scoring software code 48 that implements a hemodynamic parameter module 50 and a therapy scoring module 52 to generate one or more scores for predicting the responsiveness of the patient 36 to a corresponding therapy. Examples of the system processor 40 can include any one or more of a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other equivalent discrete logic circuit or integrated logic circuit.

[0025] The system memory 42 can be configured to store information within the operating hemodynamic monitoring device 10. In some examples, the system memory 42 is described as a computer-readable storage medium. In some examples, the computer-readable recording medium can include a non-transitory medium. The term "non-transitory" can indicate that the recording medium is not embodied in a carrier wave or a propagated signal. In a particular example, the non-transitory recording medium can store data that can change over time (e.g., RAM or cache). The system memory 42 can include volatile computer-readable memory and non-volatile computer-readable storage devices. Examples of volatile memory can include random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), and other forms of volatile memory. Examples of non-volatile storage devices can include, for example, magnetic hard disks, optical disks, flash memory, or forms of electrically programmable memory (EPROM) or electrically erasable programmable memory (EEPROM).

[0026] The display 12 can be a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, or other display device suitable for providing information to the user in a graphical form. The user interface 54 can include graphical control elements and / or physical control elements that enable user input to interact with other components of the hemodynamic monitoring system 32 and / or the hemodynamic monitoring device 10. In some examples, the user interface 54 can take the form of a graphical user interface (GUI) that presents graphical control elements on a touch-sensitive and / or presence-sensitive display screen of the display 12, for example. In such examples, user input can be received in the form of gesture inputs such as touch gestures, scroll gestures, zoom gestures, or other gesture inputs. In a particular example, the user interface 54 can take the form of and / or include physical buttons, keys, knobs, or other physical control elements configured to receive user input and interact with components of the hemodynamic monitoring system 32.

[0027] The operation of the hemodynamic monitoring system 32 for generating one or more risk scores for predicting a patient's 36 responsiveness to a corresponding therapy is further described below in combination with the flowchart of FIG. 5. As shown in FIG. 5, the hemodynamic monitoring device 10 receives sensed hemodynamic data representing the arterial pressure waveform of the patient 36 (step 60). For example, the hemodynamic sensor 34 can sense hemodynamic data representing the arterial pressure waveform of the patient 36. The hemodynamic sensor 34 can supply the hemodynamic data (as analog sensor data, for example) to the hemodynamic monitoring device 10. The ADC 44 can convert the analog hemodynamic data into digital hemodynamic data representing the arterial pressure waveform of the patient 36.

[0028] The hemodynamic monitoring device 10 derives magnitude data of hemodynamic parameters from hemodynamic data representing the arterial pressure waveform of the patient 36 (step 62). For example, as further described below, the hemodynamic monitoring device 10 can execute hemodynamic therapy scoring software code 48 to implement a hemodynamic parameter module 50 and derive hemodynamic parameter data from the arterial pressure waveform data. Such hemodynamic parameter data can include, for example, any one or more of SVV data, SVR data, dP / dt max data, or other hemodynamic parameter data. The magnitude data of one or more hemodynamic parameters can take the form of, for example, the values of the hemodynamic parameters derived over time. For example, as described above, an increase in SVV can indicate a decrease in cardiac preload, which can be treated by the therapeutic delivery of fluid. The hemodynamic parameter module 50 can derive magnitude data of the time-varying SVV from the hemodynamic data representing the arterial pressure waveform of the patient 36, and such magnitude data of SVV can be used to determine a score for predicting the responsiveness of the patient 36 to the therapeutic delivery of a fluid such as saline. Similarly, the hemodynamic parameter module 50 can derive magnitude data of the time-varying SVR that can be used to determine a score for predicting the responsiveness of the patient 36 to the therapeutic delivery of a vasopressor such as norepinephrine, dopamine, and epinephrine, or other vasoconstrictors. The hemodynamic parameter module 50 can derive magnitude data of the time-varying dP / dt max that can be used to determine a score for predicting the responsiveness of the patient 36 to the therapeutic delivery of an inotropic agent such as dobutamine, milrinone, amrinone, calcium sensitizers, or other inotropic agents.

[0029] The hemodynamic monitoring device 10 derives trend data of hemodynamic parameters (step 64). For example, the hemodynamic monitoring device 10 can execute hemodynamic therapy scoring software code 48 and implement a hemodynamic parameter module 50 to derive trend data of hemodynamic parameters. Such trend data can take the form of, for example, the best fit line (or multiple best fit lines) of hemodynamic parameter data related to one or more defined time intervals, as further described below. The hemodynamic parameter module 50 includes trend data of SVV (or trend data of PPV, or trend data of SV, or trend data of SVI) that can be used to determine a score for predicting the responsiveness of patient 36 to therapeutic delivery of fluid, trend data of SVR (or trend data of SVRI) that can be used to determine a score for predicting the responsiveness of patient 36 to therapeutic delivery of vasopressors, and dP / dt max trend data (or trend data of CI, or trend data of CO) from which any one or more of them can be derived.

[0030] The hemodynamic monitoring device 10 determines a size subscore based on the derived magnitude data of the hemodynamic parameters (step 66). For example, as further described below, the hemodynamic monitoring device 10 executes the hemodynamic therapy scoring software code 48 to implement the therapy scoring module 52, and determines the size subscore as a normalized value between the lower normalized threshold (e.g., 0) at the lower size threshold of the magnitude data and the upper normalized threshold (e.g., 1) at the upper size threshold of the magnitude data. The lower size threshold and the upper size threshold of the magnitude data can be functions of the hemodynamic parameters. For example, as further described below, the lower size threshold for the SVV parameter data can be 7% (corresponding to the lower normalized numerical threshold, e.g., 0), and the upper size threshold for the SVV parameter data can be 13% (corresponding to the upper normalized numerical threshold, e.g., 1). The therapy scoring module 52 can determine the size subscore for any one or more of the SVV parameter data, SVR parameter data, and dP / dt max For any one or more of the ,

[0031] , parameter data, the size subscore can be determined (e.g., as a normalized value).

[0031] The hemodynamic monitoring device 10 determines a trend subscore based on the derived trend data of the hemodynamic parameters (step 68). For example, as further described below, the hemodynamic monitoring device 10 executes the hemodynamic therapy scoring software code 48 to implement the therapy scoring module 52, and determines the trend subscore as an aggregation of one or more weighted (and in some examples further normalized) best-fit lines related to one or more defined time intervals of the hemodynamic parameter data. The therapy scoring module 52 can be used for SVV parameter data, PPV parameter data, SV parameter data, SVI parameter data, SVR parameter data, SVRI parameter data, CO parameter data, CI parameter data, and dP / dt maxA tendency subscore can be determined for any one or more of the parameter data.

[0032] The hemodynamic monitoring device 10 determines one or more scores based on subscores of magnitudes and related tendency subscores that respectively predict the responsiveness of the patient 36 to the corresponding therapy (step 70). For example, the hemodynamic monitoring device 10 executes hemodynamic therapy scoring software code 48 and implements a therapy scoring module 52 to determine one or more scores as a combination of a subscore of magnitude and a related tendency subscore, such as a multiplicative product of the subscore of magnitude and the subscore of tendency.

[0033] The hemodynamic monitoring device 10 outputs the representation of each of the one or more scores (step 72). For example, as will be further described below, the hemodynamic monitoring device 10 can output any one or more of a numerical representation of each score, a color-coded numerical representation of each score, a symbolic representation of each score, an auditory representation of each score, or other representations of each score (e.g., by a display 12, one or more speaker devices, or other output devices).

[0034] Accordingly, the hemodynamic monitoring device 10 implementing the technology of the present disclosure provides one or more scores that predict the responsiveness of the patient 36 to the corresponding therapy, thereby reducing the information processing burden on the participating medical personnel and providing decision-making support for the timely and effective delivery of therapeutic treatments for the presented hemodynamic state to such medical personnel. Moreover, the provided scores are determined not only from the magnitude data of the derived hemodynamic parameters but also from the tendency data, and can assist in indicating the future responsiveness of the patient to the corresponding therapy, thereby further improving the decision-making support provided by the corresponding scores.

[0035] FIG. 6 is a block diagram illustrating a further example of a hemodynamic monitor 10 displaying three separate scores, each predicting the patient 36's responsiveness to a respective therapy. As shown in FIG. 6, the hemodynamic monitor 10 displays a fluid score 74, a vasopressor score 76, and a vasopressor score 78 on the display 12. The fluid score 74 (having a value of "94" in the example of FIG. 6) represents a score determined by the hemodynamic monitor 10 based on the SVV (or PPV, or SVI, or SV) parameter data, predicting the patient 36's responsiveness to therapeutic delivery of a fluid. The vasopressor score 76 (having a value of "56" in the example of FIG. 6) represents a score determined by the hemodynamic monitor 10 based on the SVR parameter data, predicting the patient 36's responsiveness to therapeutic delivery of a vasopressor. The vasopressor score 78 (having a value of "31" in the example of FIG. 6) represents a score determined by the hemodynamic monitor 10 based on the dP / dt max 6 shows and describes three separate scores (74, 76, and 78), it should be understood that in other examples, the hemodynamic monitoring device 10 can determine and provide (e.g., display) any one or more of the fluid score 74, the vasopressor score 76, and the vasopressor score 78.

[0036] As shown in FIG. 6, each of the fluid score 74, the pressor score 76, and the inotropic score 78 has a value between a smaller numerical threshold of 0 and a larger numerical threshold of 100. In other examples, the hemodynamic monitoring device 10 can determine and / or provide the scores 74, 76, and 78 as numerical values between other smaller and larger numerical thresholds, such as values in the range of 0 to 1, values in the range of -1 to 1, values in the range of 0 to 10, or other defined ranges. Similarly, in the example of FIG. 6, the larger numerical threshold (e.g., 100) corresponds to a prediction of high responsiveness to the corresponding therapy, and the smaller numerical threshold (e.g., 0) corresponds to a prediction of low responsiveness to the corresponding therapy. However, in other examples, such correspondence of the predicted responsiveness to the smaller and larger numerical thresholds can be reversed (e.g., the smaller threshold corresponds to a prediction of high responsiveness, and the larger threshold corresponds to a prediction of low responsiveness).

[0037] In some examples, the hemodynamic monitoring device 10 can display scores 74, 76, and 78 as scores color-coded based on the values of the respective scores. For example, the hemodynamic monitoring device 10 can display the fluid score 74 in a first color (e.g., green) in response to a determination that the fluid score 74 is within a first range of values corresponding to a predicted low responsiveness of patient 36 to a therapeutic delivery of fluid (e.g., values from 0 to 40, or values of another range). Similarly, the hemodynamic monitoring device 10 can display the fluid score 74 in a second color (e.g., yellow) in response to a determination that the fluid score 74 is within a second range of values corresponding to a predicted moderate responsiveness of patient 36 to a therapeutic delivery of fluid (e.g., values from 40 to 80, or values of another range). The hemodynamic monitoring device 10 can display the fluid score 74 in a third color (e.g., red) in response to a determination that the fluid score 74 is within a third range of values corresponding to a predicted high responsiveness of patient 36 to a therapeutic delivery of fluid (e.g., values from 80 to 100, or values of another range). It should be understood that the exemplary ranges described herein (i.e., the first range from 0 to 40, the second range from 40 to 80, and the third range from 80 to 100) are merely exemplary and can vary (and in some examples be adjustable) to different ranges depending on the desired sensitivity of the score for graphically displaying the predicted responsiveness of the patient to the corresponding therapy.

[0038] In some examples, the hemodynamic monitoring device 10 can activate a sensory alert in response to determining that any one or more of scores 74, 76, and 78 have reached a threshold alert criterion, such as a criterion indicating a predicted high responsiveness of patient 36 to a corresponding therapy (e.g., a value exceeding 80, or other threshold criterion). The sensory alert can take the form of a visual alert, an audible alert, a tactile alert, or other types of sensory alerts. For example, the sensory alert can be activated as a flashing graphic and / or a colored graphic presented by the display 12, an alert sound such as a siren or a repeating tone, a tactile alert configured to vibrate the hemodynamic monitoring device 10 or convey a physical impact perceivable by a participating healthcare provider or other user, or any combination of other types of sensory alerts.

[0039] Accordingly, the hemodynamic monitoring device 10 can provide one or more scores to indicate to a clinician or other healthcare professional both the presence of a therapy predicted for a patient to respond and the identity of such therapy. As such, the hemodynamic monitoring device 10 can facilitate the participating healthcare professional in determining to provide timely and efficient therapeutic treatment to stabilize or prevent the patient's potential hemodynamic condition.

[0040] FIG. 7 is a chart showing the relationship between a hemodynamic parameter 80 and a corresponding therapy 82. As shown in FIG. 7, the hemodynamic parameter 80 can include, among other things, SVV, PPV, SVI, SV, SVR, SVRI, dP / dt max , CO, and CI. The therapy 82 can include, among other things, the therapeutic delivery of fluids, vasopressors, and inotropic agents. As shown in FIG. 7 and as described above, an increase in SVV and / or PPV (or a decrease in SVI or SV) can indicate a decrease in cardiac preload, which can be treated by a corresponding therapeutic delivery of fluids to the patient. A decrease in SVR and / or SVRI can indicate a decrease in cardiac afterload, which can be treated by a corresponding therapeutic delivery of vasopressors to the patient. dP / dtmax A decrease in CO, and / or CI may indicate a decrease in myocardial contractility, which is treatable by the corresponding therapeutic delivery of a circulatory agent to the patient.

[0041] Figures 8A - 8M show an example of a hemodynamic monitoring device 10 (Figs. 1, 4, and 6) for determining a fluid therapy score that predicts a patient's responsiveness to the therapeutic delivery of a fluid. The example of Figs. 8A - 8M will be described below with reference to the fluid therapy score, but it should be understood that it is applicable to other therapy scores such as a vasopressor therapy score that predicts a patient's responsiveness to the therapeutic delivery of a vasopressor and a circulatory agent therapy score that predicts a patient's responsiveness to the therapeutic delivery of a circulatory agent. Moreover, the example of Figs. 8A - 8M will be described below with reference to the fluid therapy score based on SVV parameter data, but it should be understood that it is applicable to the determination of a fluid therapy score based on any one or more of SVV parameter data, PPV parameter data, SVI parameter data, and SV parameter data.

[0042] As shown in Fig. 8A, the hemodynamic monitoring device 10 can determine a score that predicts a patient's responsiveness to a corresponding therapy as a combination (e.g., multiplicative product) of a magnitude sub - score (shown as "AbsoluteScore" in Fig. 8A) determined based on the magnitude data and trend data of hemodynamic parameters and a trend sub - score (shown as "TrendScore" in Fig. 8A). The hemodynamic monitoring device 10 can determine any one or more scores that predict a patient's responsiveness to any one or more corresponding therapies. In the example of Fig. 8A, the hemodynamic monitoring device 10 determines a fluid therapy score (shown as "FluidScore" in Fig. 8A) as the multiplicative product of a magnitude sub - score of the fluid (shown as "AbsoluteFluidScore" in Fig. 8A) and a trend sub - score of the fluid (shown as "FluidTrendScore" in Fig. 8A).

[0043] FIG. 8B shows an example for determining the size subscore of the fluid in FIG. 8A (shown as "AbsoluteFluidScore" in FIG. 8B). As shown in FIG. 8B, the hemodynamic monitoring device 10 can determine the size subscore of the fluid as a normalized value between a smaller normalized threshold having a value of 0 at a smaller size threshold of 7% of SVV and a larger normalized threshold having a value of 1 at a larger size threshold of 13%, although other normalized value thresholds and size thresholds are also possible.

[0044] In the example of FIG. 8B, the hemodynamic monitoring device 10 linearly interpolates between the smaller normalized threshold and the larger normalized threshold to determine the size subscore of the fluid for intermediate SVV values that are greater than the smaller size threshold (e.g., 7% in the example of FIG. 8B) and less than the larger size threshold (e.g., 13% in the example of FIG. 8B). In other examples, the hemodynamic monitoring device 10 does not need to linearly interpolate and can determine the size subscore of the fluid for intermediate SVV values based on non-linear interpolation. The hemodynamic monitoring device 10 utilizes the size subscore of the fluid to determine a fluid score that predicts the patient's responsiveness to the therapeutic delivery of the fluid, as further described below.

[0045] FIG. 8C shows an example for determining the trend subscore of the fluid in FIG. 8A. As shown in FIG. 8C, the hemodynamic monitoring device 10 can determine the trend subscore of the fluid (shown as "FluidTrendScore" in FIG. 8C) based on the aggregation of the normalized slopes of the best-fit lines of hemodynamic parameters (in this example, SVV corresponding to the therapeutic delivery of the fluid) associated with one or more defined time intervals. For example, the hemodynamic monitoring device 10 can determine the trend subscore of the fluid by the following equation:

[0046]

Equation

[0047] Here, nWindows is the number of defined time intervals, NormSlopeValue is the normalized slope of the best-fit line of the hemodynamic parameter related to the corresponding defined time interval, and WindowWeight is the weight value related to the corresponding time interval.

[0048] FIGS. 8D to 8J show an example for determining trend data using the best-fit lines related to five defined time intervals of the SVV parameter data. FIG. 8D shows a graph of exemplary SVV parameter data plotted as a percentage of SVV as a function of time (minutes). As shown in FIG. 8D, the hemodynamic monitoring device 10 can evaluate the SVV parameter using the initial evaluation point 84. In the example of FIG. 8D, the initial evaluation point 84 corresponds to the percentage of SVV (10% SVV) at the time of 676 minutes. In the example of FIG. 8D, the initial evaluation point 84 is taken as 676 minutes, but it should be understood that this is not the latest time when the SVV data is obtained, and any time when the SVV data is obtained can be adopted for the initial evaluation point 84. For example, in a specific example, when new hemodynamic parameter data becomes available, the initial evaluation point can be determined as the latest time when the hemodynamic parameter data (SVV in this example) is obtained so that the exemplary operations of FIGS. 8D to 8J are repeatedly executed.

[0049] FIG. 8E is a graph of the SVV parameter data, showing a plurality of defined time intervals of the SVV data. Specifically, the example of FIG. 8E includes five individual time intervals, namely, period I1 including 1-minute SVV data from 675 minutes to 676 minutes (i.e., with respect to 676 minutes corresponding to the initial evaluation point 84), period I2 including 2-minute SVV data from 674 minutes to 676 minutes, period I3 including 3-minute SVV data from 673 minutes to 676 minutes, period I4 including 4-minute SVV data from 672 minutes to 676 minutes, and period I5 including 5-minute SVV data from 671 minutes to 676 minutes.

[0050] The example of FIG. 8E shows five separate time intervals (I1 to I5), but in other examples, the hemodynamic monitoring device 10 can utilize any one or more of the time intervals. For example, in some examples, the hemodynamic monitoring device 10 can utilize a single defined time interval to derive trend data of a hemodynamic parameter (e.g., SVV). In other examples, the hemodynamic monitoring device 10 can utilize two, three, four, or five or more defined time intervals to derive trend data. Additionally, although the example of FIG. 8E shows the use of time intervals having fractional periods, the hemodynamic monitoring device 10 does not necessarily have to utilize fractional periods in all examples. For example, the hemodynamic monitoring device 10 can use defined time intervals of 10 seconds, 15 seconds, 30 seconds, 90 seconds, or time intervals having other non-integer fractions (or seconds) to derive trend data of hemodynamic parameters.

[0051] FIG. 8F shows an example of the hemodynamic monitoring device 10 for determining trend data of SVV for the time interval I1. As shown in FIG. 8F, the hemodynamic monitoring device 10 can use the SVV data included in the period I1 (e.g., the previous 1 minute of SVV data) as the slope of the best-fit line passing through the SVV data included in the time interval I1, and for example, use linear regression to determine trend data regarding SVV. However, other linear or non-linear trend determinations are possible by using a polynomial trend line, an exponential trend line, or other trend determinations. In the example of FIG. 8F, the hemodynamic monitoring device 10 determines the slope of the best-fit line passing through the SVV data included in the period I1 to have a numerical value of +2.

[0052] Figures 8G to 8J show an example of the hemodynamic monitoring device 10 for determining the trend data of SVV regarding periods I2 to I5. For example, in the example of Figure 8G, the hemodynamic monitoring device 10 determines that the slope of the best-fit line passing through the SVV data included in period I2 (that is, the SVV data from 674 minutes to 676 minutes) has a numerical value of +1.1. In Figure 8H, the hemodynamic monitoring device 10 determines that the slope of the best-fit line passing through the SVV data included in period I3 has a numerical value of +0.3. In the example of Figure 8I, the hemodynamic monitoring device 10 determines that the slope of the best-fit line passing through the SVV data included in period I4 has a numerical value of -0.15. In Figure 8J, the hemodynamic monitoring device 10 determines that the slope of the best-fit line passing through the SVV data included in period I5 has a numerical value of -0.2.

[0053] Figure 8K shows an example of the hemodynamic monitoring device 10 for normalizing the slope values determined in relation to periods I1 to I5. As shown in Figure 8K, the hemodynamic monitoring device 10 can normalize each of the plurality of determined slope values to generate a plurality of normalized slope values, for example, between a numerical value of +1 and -1, although other normalized ranges are also possible. In some examples, the hemodynamic monitoring device 10 can normalize the plurality of determined slope values based on previous patient data. In other examples, the hemodynamic monitoring device 10 can normalize the plurality of determined slope values based on a range of determined slope values while referring to a range of normalized values.

[0054] In the example of FIG. 8K, the hemodynamic monitoring device 10 normalizes the determined slope of the best-fit line passing through the SVV data included in period I1 as having a numerical value of +1. The hemodynamic monitoring device 10 normalizes the determined slope of the best-fit line passing through the SVV data included in period I2 as having a numerical value of +1. The hemodynamic monitoring device 10 normalizes the determined slope of the best-fit line passing through the SVV data included in period I3 as having a numerical value of +0.76. The hemodynamic monitoring device 10 normalizes the determined slope of the best-fit line passing through the SVV data included in period I4 as having a numerical value of -0.34. The hemodynamic monitoring device 10 normalizes the determined slope of the best-fit line passing through the SVV data included in period I5 as having a numerical value of -0.58.

[0055] Therefore, the hemodynamic monitoring device 10 can determine one or more normalized slopes of one or more best-fit lines of hemodynamic parameters while referring to one or more corresponding defined time intervals. The hemodynamic monitoring device 10 utilizes the normalized slopes of one or more best-fit lines to determine a score of a trend related to the hemodynamic parameter.

[0056] Figure 8L shows an example in which the hemodynamic monitoring device 10 applies a weighting coefficient to each of the normalized slopes of the best-fit lines of the SVV parameter data, normalizes them, and then aggregates the weighted slopes to determine a subscore for the fluid trend. As shown in Figure 8L, the hemodynamic monitoring device 10 can apply a weighting coefficient to each of the normalized slopes of the best-fit line, for example, by multiplying each of the respective normalized slopes by a corresponding weighting coefficient (shown as "window weight" in Figure 8L). For example, as shown in Figure 8L, a weighting coefficient of 0.3 can be applied to the normalized slope value associated with period I1 (i.e., a defined time interval including the SVV data for the previous one minute), a weighting coefficient of 0.2 can be applied to the normalized slope value associated with period I2 (i.e., a defined time interval including the SVV data for the previous two minutes), a weighting coefficient of 0.1 can be applied to the normalized slope value associated with period I3 (i.e., a defined time interval including the SVV data for the previous three minutes), a weighting coefficient of 0.05 can be applied to the normalized slope value associated with period I4 (i.e., a defined time interval including the SVV data for the previous four minutes), and a weighting coefficient of 0.02 can be applied to the normalized slope value associated with period I5 (i.e., a defined time interval including the SVV data for the previous five minutes).

[0057] It should be understood that the weighting coefficients described with reference to Figure 8L are merely examples of exemplary weighting coefficients, and the values of such weighting coefficients can be different from those described with reference to the example of Figure 8L. For example, the weighting coefficients applied by the hemodynamic monitoring device 10 can be the same or different, and can be determined experimentally based on averaged (or aggregated) past patient data, for example, such that the subscore for the fluid trend being determined matches an effective clinical treatment for known patient outcomes related to past patient data. In some examples, such as the example of Figure 8L, the normalized slope values associated with the most recent time intervals can be weighted more strongly than the normalized slope values associated with less recent time intervals.

[0058] As further shown in FIG. 8L, the hemodynamic monitoring device 10 can aggregate, for example, the weighted slope values after normalization by the above formula 1. In the example of FIG. 8L, the hemodynamic monitoring device 10 aggregates the weighted slope values after normalization to generate a subscore of the tendency of the exemplary fluid having a numerical value of 1.73.

[0059] FIG. 8M shows an example in which the hemodynamic monitoring device 10 determines a fluid therapy score (shown as “FluidScore” in FIG. 8M) as a combination of a subscore of the fluid magnitude (shown as “AbsoluteFluidScore” in FIG. 8M) and a subscore of the fluid tendency (shown as “FluidTrendScore” in FIG. 8M). In the example of FIG. 8M, the hemodynamic monitoring device 10 determines the subscore of the fluid magnitude as having a numerical value of 0.5 as described above with reference to FIG. 8B (i.e., by linearly interpolating between the smaller magnitude threshold for the 7% SVV parameter and the larger magnitude threshold for the 13% SVV parameter based on the 10% SVV value at the initial evaluation point 84).

[0060] The hemodynamic monitoring device 10 determines the fluid therapy score as the multiplicative product of the subscore of the fluid magnitude and the subscore of the fluid tendency, and generates a fluid therapy score having a numerical value of 0.865. In the example of FIG. 8M, the hemodynamic monitoring device 10 outputs the expression of the fluid therapy score as a numerical value of 87, but in other examples, the hemodynamic monitoring device 10 can output different expressions of the score. For example, the hemodynamic monitoring device 10 can output the unprocessed score (e.g., a value of 0.867), the rounded unprocessed score (e.g., a value of 0.87, a value of 0.9, or other rounded scores), or other displays of the score. As further described above, in a particular example, the hemodynamic monitoring device 10 can output the expression of the score as a color-coded score, a symbolic expression of the score, or other expressions represented by a score indicating the predicted responsiveness of the patient to the therapy.

[0061] Accordingly, the hemodynamic monitoring device 10 for implementing the technology of the present disclosure can provide one or more scores for predicting a patient's responsiveness to a corresponding therapy, and provides decision-making support to assist a clinician or other medical personnel in determining timely and effective therapeutic measures for the presented hemodynamic state. As described above, the examples in FIGS. 8A-8M are described with reference to the determination of a fluid therapy score for predicting a patient's responsiveness to a therapeutic delivery of a fluid. However, the technology in FIGS. 8A-8M is also applicable to the determination of other therapy scores, such as a vasopressor therapy score for predicting a patient's responsiveness to a therapeutic delivery of a vasopressor and a inotropic agent therapy score for predicting a patient's responsiveness to a therapeutic delivery of an inotropic agent.

[0062] FIG. 9A is a chart showing a further example for determining a magnitude subscore related to a therapeutic intravenous delivery of a fluid to a patient. As shown in FIG. 9A, the hemodynamic monitoring device 10 can determine a magnitude subscore related to a therapeutic delivery of a fluid to a patient based on any one or more of SVV, PPV, SVI, and SV. For example, as similarly described above with reference to FIG. 8B, the hemodynamic monitoring device 10 can determine a magnitude subscore of the fluid (shown as "Fluid AbsoluteScore" in FIG. 9A) as a normalized value based on the value of SVV.

[0063] As further shown in FIG. 9A, the hemodynamic monitoring device 10 can determine the magnitude subscore of the fluid as a normalized value based on the respective values of any one or more of PPV, SVI, and SV. For example, as shown in FIG. 9A, the hemodynamic monitoring device 10 can determine the magnitude subscore of the fluid as a normalized value between a lower normalized threshold having a numerical value of 0 at a lower magnitude threshold of 7% of PPV and an upper normalized threshold having a numerical value of 1 at an upper magnitude threshold of 13% of PPV. The hemodynamic monitoring device 10 can determine the magnitude subscore of the fluid as the larger of 40 ml / m 2At the SVI magnitude threshold of 0, the smaller normalized threshold having a value of 0, and the smaller 30 ml / m 2 At the SVI magnitude threshold of 1, the larger normalized threshold having a value of 1, it can be determined as the normalized value between them. The hemodynamic monitoring device 10 can determine the size subscore of the fluid as the normalized value between the smaller normalized threshold having a value of 0 at the larger SV magnitude threshold of 80 ml and the larger normalized threshold having a value of 1 at the smaller SV magnitude threshold of 60 ml. As described above with reference to the example of FIG. 8B, the larger normalized threshold, the smaller normalized threshold, the larger magnitude threshold, and the smaller magnitude threshold described with reference to FIG. 9A are merely examples, and other normalized value thresholds and magnitude thresholds are also possible.

[0064] FIG. 9B is a chart showing an example for determining the size subscore related to the therapeutic intravenous delivery of a circulatory agent to a patient. The hemodynamic monitoring device 10, as described above in the same manner with reference to FIGS. 8B and 9A (i.e., corresponding to the determination of the size subscore related to the therapeutic delivery of fluid to a patient), based on any one or more values of dP / dt max , CO, and CI, can determine the size subscore of the circulatory agent (shown as "AbsoluteScore of Circulatory Agent" in FIG. 9B) as a normalized value. For example, as shown in FIG. 9B, the hemodynamic monitoring device 10 determines the size subscore of the circulatory agent as the smaller normalized threshold having a value of 0 at the larger magnitude threshold of the PPV of dP / dt of 700 mmHg / second max and the larger normalized threshold having a value of 1 at the dP / dt of 400 mmHg / second maxIt can be determined as a normalized value between the larger normalized threshold value having a numerical value of 1 and the smaller size threshold value. The hemodynamic monitoring device 10 can determine the subscore of the circulatory agent size as a normalized value between the smaller normalized threshold value having a numerical value of 0 at the larger size threshold value of 6 L / minute of CO and the larger normalized threshold value having a numerical value of 1 at the smaller size threshold value of 4 L / minute of CO. The hemodynamic monitoring device 10 can determine the subscore of the circulatory agent size as a normalized value between the smaller normalized threshold value having a numerical value of 0 at the larger size threshold value of 3 L / minute / m 2 of CI and the larger normalized threshold value having a numerical value of 1 at the smaller size threshold value of 2 L / minute / m 2 of CI. It should be understood that the larger normalized threshold value, the smaller normalized threshold value, the larger size threshold value, and the smaller size threshold value described with reference to FIG. 9B are merely examples, and other normalized value thresholds and size thresholds are also possible.

[0065] FIG. 9C is a chart showing an example for determining the size subscore related to the therapeutic intravenous delivery of a vasopressor to a patient. As described above in the same manner, the hemodynamic monitoring device 10 can determine the subscore of the vasopressor size (shown as "AbsoluteScore of Vasopressor" in FIG. 9C) as a normalized value based on any one or more values of SVR and SVRI. For example, the hemodynamic monitoring device 10 can determine the subscore of the vasopressor size as a normalized value between the smaller normalized threshold value having a numerical value of 0 at the larger size threshold value of 1100 dynes·sec·cm -5 of SVR and the larger normalized threshold value having a numerical value of 1 at the smaller size threshold value of 800 dynes·sec·cm -5 of SVR. The hemodynamic monitoring device 10 can determine the subscore of the vasopressor size as a normalized value between the larger size threshold value of 2200 dynes·sec·cm -5 ·m2 At the smaller normalized threshold value having a numerical value of 0 at the magnitude threshold of SVRI, and the smaller 1600 dynes·sec·cm -5 ·m 2 It can be determined as a normalized value between the larger normalized threshold value having a numerical value of 1 at the magnitude threshold of SVRI.

[0066] As described similarly with reference to the fluid therapy score in the examples of FIGS. 8A to 8M, the hemodynamic monitoring device 10 can use the sub-score of the vasopressor magnitude and the sub-score of the circulatory agent magnitude to determine one or more of the vasopressor therapy score and the circulatory agent therapy score. For example, the hemodynamic monitoring device 10 determines a sub-score of the vasopressor trend based on trend data derived from the patient's SVR parameter data (or SVRI parameter data), and can generate a vasopressor therapy as a combination of the sub-score of the vasopressor magnitude and the sub-score of the vasopressor trend. The hemodynamic monitoring device 10 determines a sub-score of the circulatory agent trend based on trend data derived from the patient's dP / dt max parameter data (or CO parameter data, or CI parameter data), and can generate a therapy score for the circulatory agent as a combination of the sub-score of the circulatory agent magnitude and the sub-score of the circulatory agent trend.

[0067] Accordingly, as described herein, the hemodynamic monitoring device can provide one or more scores that predict a patient's responsiveness to a corresponding therapy, thereby reducing the information processing burden on the participating medical personnel and providing decision-making support for the timely and effective delivery of therapeutic treatments to such medical personnel for the indicated hemodynamic state. The scores provided are determined not only from the magnitude data of the derived hemodynamic parameters but also from the trend data, and can assist in indicating the patient's future responsiveness to the corresponding therapy, thereby further improving the decision-making support provided by the corresponding scores.

[0068] The present invention has been described with reference to exemplary embodiments, but it will be understood by those skilled in the art that various modifications can be made without departing from the scope of the present invention, and its elements can be replaced with equivalents. Additionally, many modifications can be made to adapt a particular situation or material to the teachings of the present invention without departing from its essential scope. Therefore, the present invention is not intended to be limited to the specific embodiments disclosed, but is intended to include all embodiments included in the appended claims.

Explanation of Reference Numerals

[0069] 10 Hemodynamic monitoring device 12 Display 32 Hemodynamic monitoring system 34 Hemodynamic sensor 36 Patient 38 Medical staff 40 System processor 42 System memory 44 Analog-to-digital converter (ADC) 48 Therapy scoring software code 50 Hemodynamic parameter module 52 Therapy scoring module 54 User interface 56 Control element 58 Output 74 Fluid score 76 Vasopressor score 78 Circulatory agent score 80 Hemodynamic parameters 82 Therapy

Claims

Claim 1 A method for monitoring a patient's arterial pressure and determining a score for predicting the patient's responsiveness to a corresponding therapy, comprising: a step in which a hemodynamic monitoring device receives sensed hemodynamic data representing the patient's arterial pressure waveform; a step in which the hemodynamic monitoring device derives magnitude data and trend data over time of hemodynamic parameters from the hemodynamic data; a step in which the hemodynamic monitoring device determines the score for predicting the patient's responsiveness to the therapy based on the magnitude data and the trend data of the hemodynamic parameters; a step in which the hemodynamic monitoring device outputs a representation of the score; wherein the step of determining the score based on the magnitude data and the trend data of the hemodynamic parameters comprises: a step of determining a magnitude subscore based on the magnitude data of the hemodynamic parameters; a step of determining a trend subscore based on the trend data of the hemodynamic parameters; a step of determining the score as a combination of the magnitude subscore and the trend subscore; wherein the step of determining the magnitude subscore comprises determining the magnitude subscore as a normalized value between a smaller normalized threshold at a smaller magnitude threshold of the magnitude data of the hemodynamic parameters and a larger normalized threshold at a larger magnitude threshold of the magnitude data of the hemodynamic parameters; the step of determining the trend subscore comprises determining the trend subscore by aggregating slopes of a plurality of weighted normalized best-fit lines associated with a plurality of defined time intervals. A method. Claim 2 The method according to claim 1, wherein the step of determining the score as a combination of the magnitude subscore and the trend subscore comprises determining the score as a product of the magnitude subscore and the trend subscore. Claim 3 As the normalized value, the step of determining the size subscore includes interpolating between the smaller normalized threshold and the larger normalized threshold in response to determining that the size data of the hemodynamic parameter is larger than the smaller size threshold and smaller than the larger size threshold. The method according to claim 1.

4. The method according to claim 1, wherein the step of deriving the trend data of the hemodynamic parameter includes determining the slope of the best-fit line of the hemodynamic parameter related to the defined time interval.

5. The method according to claim 4, wherein the step of determining the trend subscore further includes normalizing the slope of the best-fit line to determine the normalized slope of the best-fit line of the hemodynamic parameter related to the defined time interval.

6. The method according to claim 5, wherein the step of determining the trend subscore further includes applying a weighting coefficient to the normalized slope of the best-fit line to determine the weighted normalized slope of the best-fit line of the hemodynamic parameter related to the defined time interval.

7. The method according to claim 1, wherein the step of deriving the trend data of the hemodynamic parameter includes determining the plurality of slopes of the plurality of best-fit lines of the hemodynamic parameter related to the plurality of defined time intervals.

8. The method according to claim 1, wherein the hemodynamic parameter is one of stroke volume variation (SVV), pulse pressure variation (PPV), stroke volume (SV), or stroke volume index (SVI), and the therapy is intravenous delivery of physiological saline to the patient.

9. The hemodynamic monitoring device derives size data and trend data of one of systemic vascular resistance (SVR) or systemic vascular resistance index (SVRI) from the hemodynamic data; The hemodynamic monitoring device determines a second score for predicting the patient's responsiveness to intravenous delivery of a vasopressor based on the size data and the trend data of the one of SVR or SVRI; The hemodynamic monitoring device outputs a representation of the second score; The method according to claim 8, further comprising.

10. The blood flow dynamics monitoring device derives magnitude data and trend data of one of the maximum rate of increase in arterial pressure (dP / dt max ), cardiac output (CO), or cardiac index (CI) from the blood flow dynamics data, and wherein the hemodynamic monitoring device determines a third score for predicting the responsiveness of the patient to intravenous delivery of a circulatory agent based on the magnitude data and the trend data of one of dP / dt max , CO, or CI; The step of the hemodynamic monitoring device outputting the expression of the third score The method according to claim 9, further comprising.

11. The method according to claim 1, wherein the hemodynamic parameter is one of systemic vascular resistance (SVR) or systemic vascular resistance index (SVRI), and the therapy is intravenous delivery of a vasopressor to the patient.

12. The blood flow dynamics monitoring device derives magnitude data and trend data of the maximum rising speed (dP / dt max ) of arterial pressure from the blood flow dynamics data, and wherein the hemodynamic monitoring device determines a second score for predicting the responsiveness of the patient to intravenous delivery of a circulatory agent based on the magnitude data and the trend data of dP / dt max and the step of determining a second score for predicting the responsiveness of the patient to intravenous delivery of a circulatory agent based on the magnitude data and the trend data of dP / dt The step of the hemodynamic monitoring device outputting the expression of the second score The method according to claim 11, further comprising.

13. wherein the hemodynamic parameter is one of the maximum rate of increase in arterial pressure (dP / dt max ), cardiac output (CO), or cardiac index (CI), and the therapy is intravenous delivery of a circulatory agent to the patient, the method of claim 1.

14. A system for monitoring a patient's arterial pressure and determining a score for predicting the patient's responsiveness to a corresponding therapy, comprising: A hemodynamic sensor that generates hemodynamic data representing the patient's arterial pressure waveform; A system memory that stores hemodynamic therapy scoring software code; A user interface; A hardware processor that executes the hemodynamic therapy scoring software code to Derive magnitude data and trend data over time of a hemodynamic parameter from the hemodynamic data representing the patient's arterial pressure waveform; Based on the magnitude data and the trend data of the hemodynamic parameter, determine the score for predicting the patient's responsiveness to the therapy; Output an expression of the score via the user interface A hardware processor configured as such And comprising The hardware processor executes the hemodynamic therapy scoring software code to Based on the magnitude data of the hemodynamic parameter, determine a magnitude subscore; Based on the trend data of the hemodynamic parameter, determine a trend subscore; Determining the score as a combination of the magnitude subscore and the trend subscore Thus, based on the magnitude data and the trend data of the hemodynamic parameter, the score is configured to be determined. The hardware processor is configured to execute the hemodynamic therapy scoring software code to determine the size subscore as a normalized value between the smaller normalized threshold at the smaller size threshold of the size data of the hemodynamic parameter and the larger normalized threshold at the larger size threshold of the size data of the hemodynamic parameter. A system, wherein the hardware processor is configured to execute the hemodynamic therapy scoring software code to aggregate a plurality of slopes of a plurality of weighted normalized best-fit lines associated with a plurality of defined time intervals to determine the subscore of the trend. **Claim 15** The system according to claim 14, wherein the hardware processor is configured to execute the hemodynamic therapy scoring software code to determine the score as a multiplicative product of the size subscore and the trend subscore, thereby determining the score as a combination of the size subscore and the trend subscore. **Claim 16** The system according to claim 14, wherein the hardware processor is configured to execute the hemodynamic therapy scoring software code to derive the trend data of the hemodynamic parameter by determining the slope of the best-fit line of the hemodynamic parameter associated with the defined time interval. **Claim 17** The system according to claim 16, wherein the hardware processor is configured to execute the hemodynamic therapy scoring software code to normalize the slope of the best-fit line to determine the subscore of the trend and to determine the normalized slope of the best-fit line of the hemodynamic parameter associated with the defined time interval. **Claim 18** The system according to claim 17, wherein the hardware processor is configured to execute the hemodynamic therapy scoring software code to apply a weighting coefficient to the normalized slope of the best-fit line to determine the subscore of the trend and to determine the weighted normalized slope of the best-fit line of the hemodynamic parameter associated with the defined time interval. **Claim 19** The system according to claim 14, wherein the hardware processor is configured to derive the trend data of the hemodynamic parameters by executing the hemodynamic therapy scoring software code to determine the plurality of slopes of the plurality of best-fit lines of the hemodynamic parameters associated with the plurality of defined time intervals.

20. The system according to claim 14, wherein the hemodynamic parameter is one of stroke volume variation (SVV), pulse pressure variation (PPV), stroke volume (SV), or stroke volume index (SVI), and the therapy is intravenous delivery of physiological saline to the patient.

21. The hardware processor executes the hemodynamic therapy scoring software code to derive magnitude data and trend data of one of systemic vascular resistance (SVR) or systemic vascular resistance index (SVRI) from the hemodynamic data, determine a second score for predicting the patient's responsiveness to intravenous delivery of a vasopressor based on the magnitude data and the trend data of the one of SVR or SVRI, and output a representation of the second score via the user interface The system according to claim 20, further configured as described above.

22. The hardware processor executes the hemodynamic therapy scoring software code to From the hemodynamic data, magnitude data and trend data of one of the maximum rising speeds of arterial pressure (dP / dt max ), cardiac output (CO), or cardiac index (CI) are derived, dP / dt max Based on the magnitude data and the trend data of the one of CO, or CI, determine a third score for predicting the responsiveness of the patient to the intravenous delivery of the circulatory agent, output a representation of the third score via the user interface The system according to claim 21, further configured as described above.

23. The system according to claim 14, wherein the hemodynamic parameter is one of systemic vascular resistance (SVR) or systemic vascular resistance index (SVRI), and the therapy is intravenous delivery of a vasopressor to the patient.

24. The hardware processor executes the hemodynamic therapy scoring software code to From the hemodynamic data, the magnitude data and trend data of one of the maximum rising rates of arterial pressure (dP / dt max ), cardiac output (CO), or cardiac index (CI) are derived, dP / dt max Based on the magnitude data and the trend data of the one of CO, or CI, determine a second score for predicting the responsiveness of the patient to intravenous delivery of the circulatory agent, output a representation of the second score via the user interface The system according to claim 23, further configured as described above.

25. wherein the hemodynamic parameter is one of a maximum rate of increase in arterial pressure (dP / dt max ), cardiac output (CO), or cardiac index (CI), and the therapy is intravenous delivery of a circulation-acting drug to the patient, the system of claim 14.

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