SYSTEM AND METHOD OF DETERMINING A SUSCEPTIBILITY TO CARDIORESPIRATORY INSUFFICIENCY

• US 20140107437A1
• Filed: 12/20/2013
• Published: 04/17/2014
• Est. Priority Date: 06/30/2011
• Status: Active Grant

• Current Assignee: University of Pittsburgh of The Commonwealth System of Higher Education
• Original Assignee: University of Pittsburgh of The Commonwealth System of Higher Education
• Inventors: PINSKY, Michael R.
• Granted Patent: US 10,631,792 B2
• Time in Patent Office: Days
• US Class Current: 600/301
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/02028 Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/02108 from analysis of pulse wave characteristics
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/029 Measuring or recording blood output from the heart, e.g. minute volume A61B8/065 takes precedence
• CPC Class Codes: A61B 5/14539 for measuring pH
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/7475 User input or interface means, e.g. keyboard, pointing device, joystick
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A system and method for determining a patient'"'"'s susceptibility to develop cardiorespiratory instability wherein physiological parameters are analyzed with respect to a dynamics systems model to produce and indicator associated with a probability that the patient will become unstable is provided.

Claims

1. A method for determining the susceptibility of a patient to develop cardiorespiratory instability relating to one or more physiological parameters, the method comprising:

monitoring one or more physiological parameters associated with the patient;

accessing, via one or more processors, a dynamics systems model directed to predicting a patient'"'"'s likelihood of developing cardiorespiratory instability from patient data that includes measurements of the one or more physiological parameters;

comparing, via the one or more processors, measurements of at least one of the monitored one or more physiological parameters from the patient with the dynamics systems model;

determining, via the one or more processors, a susceptibility of the patient to develop cardiorespiratory instability; and

,

indicating, via the one or more processors, the susceptibility of the patient to develop cardiorespiratory instability.

2. The method of claim 1, further comprising generating a report indicating the susceptibility of the patient to develop cardiorespiratory instability.

3. The method of claim 1, wherein the susceptibility comprises a susceptibility to develop cardiorespiratory instability between 0 and 30 minutes in the future.

4. The method of claim 3, wherein the susceptibility is indicated as a likelihood of developing at least one condition selected from hypovolemic, cardiogenic, or vasomotor tone dysfunction between 5 and 15 minutes in the future.

5. The method of claim 1 further including proposing, via the one or more processors, an additional physiological parameter of the patient to be monitored and a corresponding effect such monitoring would have on the accuracy of the indicated susceptibility of the patient to develop cardiorespiratory instability.

6. The method of claim 1, wherein the dynamics systems model includes a defined physiological signature characteristic for hypovolemic, cardiogenic, and vasomotor tone dysfunction of the shock state.

7. The method of claim 1, further including determining, via the one or more processors, responsiveness of the patient to an intervention including one or more of the following:

fluid infusion, inotropic drug therapy, or vasopressor drug therapy.

8. The method of claim 7 further including reporting the determination of responsiveness of the patient to an intervention.

9. The method of claim 7 further including automatically adjusting the monitoring of the physiological parameters of the patient in response to the determination of responsiveness.

10. The method of claim 1, further including determining, via the one or more processors, whether the patient is responsive to process-specific interventions by demonstrating improvement, or if other interventions should be applied.

11. The method of claim 10 further including reporting whether the patient is responsive to process-specific interventions.

12. The method of claim 7 further including automatically adjusting the monitoring of the physiological parameters of the patient in response to the determination of whether the patient is responsive to process-specific interventions.

13. The method of claim 1, further including determining, via the one or more processors, when resuscitation has effectively restored tissue perfusion and prevented the subsequent development of organ injury.

14. The method of claim 13 further including reporting the determination of when resuscitation has effectively restored tissue perfusion and prevented the subsequent development of organ injury.

15. The method of claim 13 further including automatically adjusting the monitoring of the physiological parameters of the patient in response to the determination of when resuscitation has effectively restored tissue perfusion and prevented the subsequent development of organ injury.

16. The method of claim 1, wherein monitoring the one or more physiological parameters includes monitoring one or more of the following:

arterial blood pressure—

i.e., systolic, diastolic—

mean arterial pressure, systolic pressure variation, pulse pressure variation, stroke volume variation;

right atrial pressure;

right ventricular pressure;

pulmonary artery pressure;

mean pulmonary artery pressure;

pulmonary artery wedge pressure;

left atrial pressure;

cardiac output;

cardiac index;

stroke volume;

stroke index;

stroke volume index;

systemic vascular resistance;

systemic vascular resistance index;

pulmonary vascular resistance;

pulmonary vascular resistance index;

left ventricular stroke work;

left ventricular stroke work index;

right ventricular stroke work;

right ventricular stroke work index;

coronary artery perfusion pressure;

right ventricular end-diastolic volume;

right ventricular end-systolic volume;

or, right ventricular ejection fraction.

17. The method of claim 16, wherein monitoring the one or more physiological parameters includes monitoring one or mare oxygenation parameters including:

partial pressure of arterial oxygen;

partial pressure of arterial CO₂;

bicarbonate;

pH;

arterial oxygen saturation;

mixed venous saturation;

arterial oxygen content;

venous oxygen content;

A-V oxygen content difference;

oxygen delivery;

oxygen delivery index;

oxygen consumption;

oxygen consumption index;

oxygen extraction ration;

or, oxygen extraction index.

18. The method of claim 17, wherein monitoring the one or more physiological parameters further includes monitoring one or more non-invasive vital signs including:

heart rate;

respiratory rate;

blood pressure;

peripheral arterial O₂ saturation (SpO₂);

or, temperature.

19. A non-transitory computer-readable storage medium comprising computer-readable instructions stored thereon and to be executed on a processor of a system for determining the susceptibility of a patient to develop cardiorespiratory instability relating to one or more physiological parameters, the stored instructions comprising:

monitoring one or more physiological parameters associated with the patient;

analyzing at least one of the monitored one or more physiological parameters;

accessing a dynamics systems model directed to predicting a patient'"'"'s likelihood of developing cardiorespiratory instability;

comparing at least one of the analyzed physiological parameters with the dynamics systems model;

determining a susceptibility of the patient to develop cardiorespiratory instability; and

, indicating the susceptibility of the patient to develop cardiorespiratory instability.

20. The non-transitory computer-readable storage medium of claim 19, further including proposing an additional physiological parameter of the patient to be monitored and a corresponding effect such monitoring would have on the accuracy of the indicated susceptibility of the patient to develop cardiorespiratory instability.

21. The non-transitory computer-readable storage medium of claim 19, wherein the dynamics systems model includes a defined physiological signature characteristic for hypovolemic, cardiogenic, and vasomotor tone dysfunction of the shock state.

22. The non-transitory computer-readable storage medium of claim 19, wherein the dynamics systems mode'"'"' includes a probabilistic model of normality in a plurality of dimensions previously learned from a representative set of a plurality of patients at risk of developing cardiorespiratory insufficiency.

23. The non-transitory computer-readable storage medium of claim 19, further including determining responsiveness of the patient to an intervention.

24. A system for determining the susceptibility of a patient to develop cardiorespiratory instability relating to one or more physiologic parameters, the system comprising:

an integrated monitoring system including a processor, a display device, and one or more sensors, the one or more sensors are operatively connected to the patient to monitor one or more physiological parameters of the patient a memory coupled to the integrated monitoring system;

a memory device storing one or more dynamics systems models directed to predicting a patient'"'"'s likelihood of developing cardiorespiratory instability;

one or more processors configured to produce an index output that indicates a susceptibility of the patient to develop cardiorespiratory instability based at least in part on at least one of the monitored one or more physiological parameters of the patient and at least one of the one or more dynamics systems models; and

a display device configured to display the index output.

25. The system of claim 24 wherein the index output includes a proposal to monitor an additional physiological parameter of the patient and a corresponding effect such monitoring would have on the accuracy of the indicated susceptibility of the patient to develop cardiorespiratory instability.

26. The system of claim 24, wherein the one or more dynamics systems models includes a defined physiological signature characteristic for hypovolemic, cardiogenic, and vasomotor tone dysfunction of the shock state.

27. The system of claim 24, wherein the one or more dynamics systems models includes a probabilistic model of normality in a plurality of dimensions previously learned from a representative set of a plurality of patients at risk of developing cardiorespiratory insufficiency.

28. The system of claim 24, wherein the one or more dynamics systems models is directed to determining a minimal data set—

in terms of measure variables and their respective sampling frequencies and lead times—

required to predict cardiorespiratory insufficiency.

29. The system of claim 24, wherein the one or more dynamics systems models directed to determining a minimal data set required to predict cardiorespiratory insufficiency is constructed with a focus on increased specificity of increasing or changing measured variables, their sampling frequency, and lead time.

30. The system of claim 24, wherein the one or more dynamics systems models is directed to determining a minimal data set—

in terms of measure variables and their respective sampling frequencies and lead times—

required to determine an etiology of cardiorespiratory insufficiency.

31. The system of claim 30, wherein the one or more dynamics systems models directed to determining a minimal data set required to determine an etiology of cardiorespiratory insufficiency is constructed with a focus on increased specificity of increasing or changing measured variables, their sampling frequency, and lead time.

32. The system of claim 24, wherein the one or more dynamics systems models is directed to determining a minimal data set—

in terms of measure variables and their respective sampling frequencies and lead times—

required to predict that cardiorespiratory insufficiency has been resolved.

33. The system of claim 32, wherein the one or more dynamics systems models directed to determining a minimal data set required to predict that cardiorespiratory insufficiency has been resolved is constructed with a focus on increased specificity of increasing or changing measured variables, their sampling frequency, and lead time.

34. A method for determining the susceptibility of a patient to develop cardiorespiratory instability comprising:

(a) monitoring, over a time interval, one or more physiologic parameter selected from the group consisting of heart rate, blood oxygen saturation, and respiration rate and thereby generating a plurality of measurements of said parameter(s);

(b) calculating the test mean value and/or test standard deviation value over the interval for each parameter measured;

(c) accessing via one or more processors in a computer system having a memory, a dynamics system model that compares the mean values and/or standard deviation values of heart rate, blood oxygen saturation and/or respiration rates in patients that are either stable, unstable, or about to become unstable to generate model mean value(s) and/or model standard deviation value(s) for heart rate, blood oxygen saturation and/or respiration rate in unstable patients;

(d) comparing the test mean value(s) and/or test standard deviation value(s) calculated in (b) with the model mean value(s) and/or model standard deviation value(s) of patients who are about to become unstable in the dynamics system model;

(e) initiating additional or more frequent parameter monitoring where the comparison performed in (d) indicates that the patient is about to become unstable.

35. The method of claim 34 wherein a memory device stores a periodically updated set of clinical data including measurements of heart rate, blood oxygen saturation and/or respiration rate of patients such that the model mean value(s) and model standard deviation value(s) for patients that are stable, unstable, or about to become unstable are recalculated when or after the data is updated.

36. The method of claim 34 where the comparison of data or values is performed by analysis of variance (ANOVA).

37. The method of claim 34, where the parameter(s) is/are measured over a plurality of intervals and a test mean value and/or test standard deviation value is determined for each interval.

38. The method of claim 37 where the test values are compared between intervals.