RISK STRATIFICATION BASED HEART FAILURE DETECTION ALGORITHM

• US 20130116578A1
• Filed: 12/26/2012
• Published: 05/09/2013
• 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: An, Qi, Thakur, Pramodsingh Hirasingh, Averina, Viktoria A., Zhang, Yi, Sweeney, Robert J.
• Granted Patent: US 9,968,266 B2
• Time in Patent Office: Days
• US Class Current: 600/484
• CPC Class Codes: A61B 5/0031 Implanted circuitry
• CPC Class Codes: A61B 5/02007 Evaluating blood vessel condition, e.g. elasticity, compliance
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/4842 Monitoring progression or stage of a disease
• 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: A61N 1/37282 characterised by communication with experts in remote locations using a network
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G16H 40/63 for local 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: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A system comprises a risk analysis module and a worsening heart failure (WHF) detection module. The risk analysis module measures at least one first physiological parameter of a subject using a physiological sensor of an ambulatory medical device, and determines a heart failure (HF) risk score for the subject according to the at least one measured first physiological parameter. The HF risk score indicates susceptibility of the subject to experiencing a HF event. The WHF detection module measures at least one second physiological parameter of the subject using the same or different physiological sensor, and generates an indication of prediction that the subject will experience a WHF event when the at least one second physiological parameter satisfies a WHF detection algorithm. The risk analysis module adjusts generation of the indication by the WHF detection algorithm according to the determined HF risk score.

Claims

1. A system comprising:

a risk analysis module configured to;

measure at least one first physiological parameter of a subject using a physiological sensor of an ambulatory medical device; and

determine a heart failure (HF) risk score for the subject according to the at least one measured first physiological parameter, wherein the HF risk score indicates susceptibility of the subject to experiencing a worsening heart failure (WHF) event; and

a WHF detection module configured to;

measure at least one second physiological parameter of the subject using the same or different physiological sensor, wherein the at least one second physiological parameter is distinct from the at least one first physiological parameter; and

generate an indication of prediction that the subject will experience a WHF event when the at least one second physiological parameter satisfies a WHF detection algorithm,

wherein the risk analysis module is configured to adjust generation of the indication by the WHF detection algorithm according to the determined HF risk score, and

wherein the WHF detection module is configured to generate the indication using the adjusted WHF detection algorithm and providing the indication to at least one of a user or process.

2. The system of claim 1,

wherein the WHF detection module is configured to generate the indication of prediction of a WHF event when the at least one second physiological parameter satisfies at least one detection criterion of the WHF detection algorithm, and

wherein the risk analysis module is configured to adjust the at least one detection criterion of the WHF detection algorithm according to the determined HF risk score.

3. The system of claim 2, wherein the risk analysis module is configured to continuously vary at least one parameter of the WHF detection algorithm as a function of the at least one first physiological parameter measurement.

4. The system of claim 1, wherein the risk analysis module is configured to select a detection algorithm as the WHF detection algorithm from among a plurality of detection algorithms according to the determined HF risk score.

5. The system of claim 4, wherein the risk analysis module is configured to:

enable a logistic regression model as the WHF detection algorithm when the HF risk score is determined to be in a first HF risk score range; and

enable a neural network model as the WHF detection algorithm when the HF risk score is determined to be in a second HF risk score range.

6. The system of claim 1, wherein the risk analysis module is configured to:

compare the determined HF risk score with at least one risk score threshold; and

assign a HF risk category to the subject according to the comparison.

7. The system of claim 1, including:

one or more ambulatory medical devices, wherein at least one of the ambulatory medical devices include at least one of;

a heart sound sensor and wherein the at least one first physiological parameter includes a measured amplitude of the S3 heart sound;

a respiration sensor and wherein the at least one first physiological parameter includes a measured day-to-day variation in respiratory rate of the subject;

a cardiac signal sensing circuit and wherein the at least one first physiological parameter includes a time interval measured between two fiducial features occurring in one or more cardiac cycles;

a biomarker sensor configured to generate an indication or a surrogate indication of a biomarker indicative of a WHF event, and wherein the at least one first physiological parameter includes at least one of a direct measure or a surrogate measure of the biomarker;

a jugular vein distention (JVD) sensor configured to generate an indication of JVD or a surrogate indication of JVD; and

a counter circuit and a timer circuit, and wherein the at least one first physiological parameter includes a number of HF events detected for the subject within a specified time window.

8. The system of claim 1, wherein the risk analysis module is configured to:

measure a plurality of physiological parameters that are distinct from the at least one second physiological parameter; and

determine the HF risk score using the plurality of physiological parameters.

9. The system of claim 8, including:

a scoring module configured to determine individual reliability scores for each of the plurality of physiological parameters, wherein a higher reliability score is assigned to a physiological parameter having a more consistent measurement; and

wherein the risk analysis module is configured to combine the individual HF risk scores according to the determined reliability scores to form a composite HF risk score.

10. The system of claim 9, wherein the scoring module is configured to discard a HF risk score for a physiological parameter measurement when a reliability score determined for the physiological parameter measurement falls below a specified reliability score threshold value.

11. The system of claim 1, including:

a user interface in electrical communication with the risk analysis module,

wherein the risk analysis module is configured to;

receive an indication of severity of an HF condition of the subject via the user interface; and

determine the HF risk score according to the first physiological parameter measurement and the received indication of severity.

12. The system of claim 1, including:

the ambulatory medical device, wherein the ambulatory medical device includes the WHF detection module;

an external medical device, wherein the external medical device includes the risk analysis module and a communication module in electrical communication with the risk analysis module, wherein the communication module is configured to communicate information with the ambulatory medical device, and

wherein the risk analysis module is configured to communicate an adjustment to the WHF detection algorithm to the ambulatory medical device.

13. A method comprising:

measuring at least one first physiological parameter of a subject using a physiological sensor of an ambulatory medical device;

determining a heart failure (HF) risk score of the subject according to the measured at least one first physiological parameter, wherein the HF risk score indicates susceptibility of the subject experiencing a worsening heat failure (WHF) event;

measuring at least one second physiological parameter of the subject with the same or different physiological sensor, wherein the at least one second physiological parameter is distinct from the at least one first physiological parameter;

generating an indication of prediction that the subject will experience a WHF event when the at least one second physiological parameter satisfies a device-based WHF detection algorithm;

adjusting prediction by the WHF detection algorithm according to the determined HF risk score; and

generating an indication of a predicted WHF event when predicting the WHF event using the adjusted WHF detection algorithm and providing the indication to at least one of a user or process.

14. The method of claim 13,

wherein the WHF event is predicted when the at least one second physiological parameter satisfies at least one detection criterion of the WHF detection algorithm, and

wherein adjusting prediction by the WHF detection algorithm includes adjusting the at least one second physiological parameter detection criterion of the WHF detection algorithm according to the determined HF risk score.

15. The method of claim 13, wherein adjusting prediction by the WHF detection algorithm includes varying at least one WHF detection algorithm parameter continuously as a function of the determined HF risk score.

16. The method of claim 13, wherein adjusting the WHF detection algorithm includes selecting a detection algorithm from a plurality of WHF detection algorithms according to the determined HF risk score.

17. The method of claim 13, wherein assessing the risk of the HF event includes assigning a HF risk category to the subject according to a comparison of the determined HF risk score with at least one risk score threshold.

18. The method of claim 13, wherein the first physiological parameter includes at least one of:

amplitude of the S3 heart sound;

a number of HF events experienced by the subject within a specified time window;

day-to-day variation of respiratory rate;

at least one of a direct measure or a surrogate measure of a biomarker indicative of a WHF event;

a time interval between two fiducial features occurring in one or more cardiac cycles; and

a measure of jugular vein distention (JVD).

19. The method of claim 13,

wherein measuring at least one first physiological parameter includes measuring a plurality of physiological parameters distinct from the at least one second physiological parameter, and

wherein determining a HF risk score of the subject includes determining the HF risk score using the plurality of physiological parameters.

20. The method of claim 13, including:

receiving an indication of severity of an HF condition of the subject into the ambulatory medical device via a user interface, and

wherein determining a HF risk score of the subject includes determining the HF risk score according to the at least one first physiological parameter measurement and the received indication of severity.