BETWEEN-PATIENT COMPARISONS FOR RISK STRATIFICATION OF FUTURE HEART FAILURE DECOMPENSATION

• US 20080162182A1
• Filed: 12/27/2006
• Published: 07/03/2008
• Est. Priority Date: 12/27/2006
• Status: Active Grant

• Current Assignee: Cardiac Pacemakers Incorporated (Boston Scientific Corporation)
• Original Assignee: Cardiac Pacemakers Incorporated (Boston Scientific Corporation)
• Inventors: Carlson, Gerrard M., Sachanandani, Haresh G., Sweeney, Robert J., Siejko, Krzysztof Z., Peterson, Jon, Cazares, Shelley, Stalsberg, Kevin J.
• Granted Patent: US 8,768,718 B2
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: A61B 5/0031 Implanted circuitry
• CPC Class Codes: A61N 1/37282 characterised by communication with experts in remote locations using a network
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

This document discusses, among other things, systems and methods for using between-patient comparisons for risk stratification of future heart failure decompensation. A method comprises receiving patient data of a current patient, the patient data collected by a patient monitoring device; determining a reference group related to the patient; determining a reference group dataset selected from the reference group, wherein the dataset includes patient data that is of a similar type received from the patient monitoring device; generating a model of the reference group dataset; and automatically comparing the received physiological data to a model to derive an index for the patient.

Claims

1. A method comprising:

receiving patient data of a current patient, the patient data collected by a patient monitoring device;

determining a reference group related to the patient;

determining a reference group dataset selected from the reference group, wherein the dataset includes patient data that is of a similar type received from the patient monitoring device;

generating a model of the reference group dataset; and

automatically comparing the received physiological data to a model to derive an index for the patient.

2. The method of claim 1, wherein the reference group is selected from patients associated with a centralized patient management system.

3. The method of claim 1, wherein the reference group is selected from patients similar to the current patient using one or more criteria, wherein the criteria include age, gender, ethnicity, clinic, left ventricular ejection fraction (LVEF), New York Heart Association (NYHA) heart failure classification, results of a six-minute walk test (6MW), quality of life, heart failure etiology, body mass index (BMI), blood pressure, medication, co-morbidity, arrhythmia history, implant history, treatment compliance, health care system, circadian rhythm, geographic location, patients who have survived for a particular time frame, or patients who have not decompensated in a particular time frame.

4. The method of claim 3, comprising:

detecting a change of one or more of the criteria; and

updating the reference group using the changed criteria.

5. The method of claim 1, wherein the reference group includes one or more patients that are considered to be in stable condition using one or more criteria, wherein the criteria include patients who did not die, decompensate, or experience a decrease in quality life during a particular time period.

6. The method of claim 1, wherein generating the model includes using a probability function to generate the model of the reference group dataset.

7. The method of claim 6, wherein the probability function includes one or more cumulative distribution functions.

8. The method of claim 6 wherein the probability function includes one or more probability distribution functions.

9. The method of claim 1, comprising using the index to determine a similarity of the patient to the reference group.

10. The method of claim 1, comprising using the index to determine how likely the patient is to decompensate within a timeframe relative to a reference group'"'"'s likelihood of heart failure decompensation in the timeframe.

11. The method of claim 1, comprising using the index to determine how likely the patient is to die in a timeframe relative to a reference group'"'"'s likelihood of dying in the timeframe.

12. The method of claim 1, comprising using the index to determine how likely the patient'"'"'s quality of life is to change a timeframe relative to a reference group'"'"'s likelihood of quality of life changing in the timeframe.

13. The method of claim 1, comprising using the index to stratify the patient into one or more discrete groups associated with one or more risk levels.

14. The method of claim 13, wherein the risk levels are indicative of a likelihood of heart failure decompensation in a particular timeframe.

15. The method of claim 13, wherein using the index includes comparing the index to one or more threshold values to categorize the patient into a risk level.

16. The method of claim 15, comprising adjusting the threshold values automatically.

17. The method of claim 15, comprising adjusting the threshold values on a recurrent or periodic basis.

18. The method of claim 1, wherein receiving patient data of the current patient includes receiving patient data at an implantable medical device.

19. The method of claim 1, wherein the patient data includes patient compliance data.

20. The method of claim 1, wherein the patient data includes physiological data.

21. The method of claim 20, wherein the physiological data includes one or more of an electrocardiogram, an activity level, a heart rate variability, a heart rate, a heart sound, a blood pressure, an impedance, a body weight, or an O2 saturation.

22. The method of claim 1, comprising:

detecting a triggering event; and

updating the reference group when the triggering event is detected.

23. The method of claim 22, wherein the triggering event includes one or more of an interrogation of the patient monitoring device, an implantation of a new patient device, an introduction of a new patient device, an approval of a revised reference group, a periodic or recurring time interval, a change in a health state of one or more patients, a user command, or a request to access the reference group.

24. A system comprising:

a patient device comprising;

a communication module adapted to receive patient data associated with a patient;

a reference group module adapted to select and store reference group data corresponding to the patient; and

an analysis module adapted to;

generate a model of the reference group data; and

compare the patient data to the model to calculate an index for the patient.

25. The system of claim 24, comprising a centralized patient management system.

26. The system of claim 24, wherein the analysis module is adapted to generate the model of the reference group using a probability function.

27. The system of claim 26, wherein the analysis module is adapted to generate the model of the reference group using one or more cumulative distribution functions.

28. The system of claim 26, wherein the analysis module is adapted to generate the model of the reference group using one or more probability distribution functions.

29. The method of claim 24, wherein the analysis module is adapted to use the index to determine a similarity of the patient to the reference group.

30. The system of claim 24, wherein the analysis module is adapted to use the index to determine how likely the patient is to decompensate within a timeframe relative to a reference group'"'"'s likelihood of decompensation in the timeframe.

31. The system of claim 24, wherein the analysis module is adapted to use the index to stratify the patient into one or more risk levels, wherein the risk levels are indicative of a likelihood of heart failure decompensation in a particular timeframe.

32. The system of claim 24, wherein the patient device includes an implantable medical device.

33. The system of claim 24, wherein the patient data includes patient compliance data.

34. The system of claim 24, wherein the patient data includes physiological data.

35. An apparatus comprising:

means for receiving patient data of a current patient, the patient data collected by a patient monitoring device;

means for determining a reference group related to the patient;

means for determining a reference group dataset selected from the reference group, wherein the dataset includes patient data that is of a similar type received from the patient monitoring device;

means for generating a model of the reference group dataset; and

means for automatically comparing the received physiological data to a model to derive an index for the patient.