HEART FAILURE EVENT DETECTION AND RISK STRATIFICATION USING HEART RATE TREND

• US 20150342540A1
• Filed: 04/13/2015
• Published: 12/03/2015
• Est. Priority Date: 05/30/2014
• Status: Abandoned Application

• Current Assignee: Cardiac Pacemakers Incorporated (Boston Scientific Corporation)
• Original Assignee: Cardiac Pacemakers Incorporated (Boston Scientific Corporation)
• Inventors: An, Qi, Zhang, Yi, Thakur, Pramodsingh Hirasingh, Averina, Viktoria A.
• Application Number:
  US14/684,978
• Publication Number:
  US 20150342540A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• 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 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/0538 invasively, e.g. using a catheter
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/1102 Ballistocardiography
• CPC Class Codes: A61B 5/1116 Determining posture transitions
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/283 Invasive
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/332 Portable devices specially adapted therefor
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/4809 Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/686 Permanently implanted devices, e.g. pacemakers, other stimulators, biochips A61B5/6861 takes precedence
• CPC Class Codes: A61B 5/6869 Heart
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• 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 5/7282 : Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61N 1/3627 : for treating a mechanical deficiency of the heart, e.g. congestive heart failure or cardiomyopathy
• CPC Class Codes: A61N 1/36585 : controlled by two or more physical parameters
• CPC Class Codes: A61N 1/3702 : Physiological parameters A61N1/365 takes precedence; evoked response A61N1/371

Abstract

Systems and methods for detecting heart failure (HF) events or identifying patient at elevated risk of developing future HF events such as HF decompensation are described. A medical system can detect a contextual condition associated with a patient, including an environmental context or a physiologic context. The contextual condition includes information indicative or correlative of a change in metabolic demand. The system can include a heart rate (HR) analyzer circuit that extracts a HR feature from a cardiac activity signal, and perform multiple HR feature measurements in response to the detected patient contextual condition meeting a specified criterion. The system can calculate one or more signal metrics including a HR metric using the HR feature measurements. The system can detect an HF event using the signal metrics, or use the signal metrics to calculate a composite risk indicator indicative of the patient'"'"'s likelihood of developing a future HF event.

Claims

1. A system, comprising:

a context detector circuit configured to detect contextual condition associated with a patient, the contextual condition including information indicative or correlative of a change in metabolic demand of a patient;

a heart rate (HR) analyzer circuit configured to sense from the patient a cardiac activity signal, extract a HR feature from the sensed cardiac activity signal, and perform a plurality of measurements of the HR feature in response to the detected patient contextual condition meeting a specified criterion;

a target event indicator generator circuit configured to calculate one or more signal metrics using the plurality of measurements of the HR feature; and

a physiologic event detector circuit coupled to the target event indicator generator circuit, the physiologic event detector circuit configured to detect a target physiologic event using the one or more signal metrics.

2. The system of claim 1, wherein:

the target event indicator generator circuit is configured to calculate the one or more signal metrics including a statistical or morphological parameter extracted from the plurality of measurements of the HR feature; and

the physiologic event detector circuit is configured to detect the worsening heart failure (HF) using the one or more signal metrics.

3. The system of claim 2, wherein the target event indicator generator circuit is configured to calculate the one or more signal metrics including a central tendency of the plurality of measurements of the HR feature.

4. The system of claim 2, wherein the target event indicator generator circuit is configured to calculate the one or more signal metrics including a representative HR (HR_Rep) representing a specified percentile rank among the plurality of measurements of the HR feature, the specified percentile rank indicative of a relative number of HR measurements falling below or equal to the HR_Rep.

5. The system of claim 4, wherein the specified percentile rank includes a HR percentile greater than 50-th percentile.

6. The system of claim 1, wherein the context detector circuit includes a timer/clock circuit capable of determining time of a day, and wherein the HR analyzer circuit is configured to perform a plurality of measurements of the HR feature during specified time of a day indicative or correlative of an elevated metabolic demand of the patient.

7. The system of claim 6, wherein the HR analyzer circuit is configured to perform a plurality of measurements of the HR feature during a period of time excluding night of the day.

8. The system of claim 1, wherein the context detector circuit includes a sleep state detector configured to detect in the patient a time of transition from a sleep state to an awake state, and wherein the HR analyzer circuit is configured to perform a plurality of measurements of the HR feature in response to the detected transition from the sleep state to the awake state.

9. The system of claim 1, wherein the context detector circuit includes a posture sensor configured to detect a posture of the patient and to classify the posture as one of two or more posture states, and wherein the HR analyzer circuit is configured to perform a plurality of measurements of the HR feature in response to the detected posture being classified as a specified state indicative or correlative of an elevated metabolic demand.

10. The system of claim 1, wherein the context detector circuit includes one or more physiologic sensors configured to detect a change in metabolic demand of the patient during a specified period, and wherein the HR analyzer circuit is configured to perform a plurality of measurements of the HR feature in response to a detection of an increase in the metabolic demand.

11. The system of claim 1, wherein the context detector circuit includes an activity sensor configured to detect a physical activity or exertion level of the patient, wherein the HR analyzer circuit is configured to perform a plurality of measurements of the HR feature in response to the detected physical activity or exertion level exceeding a specified activity level threshold.

12. A system, comprising:

a signal analyzer circuit, including;

a context detector circuit configured to receive contextual condition associated with a patient, the contextual condition including time of a day or a physical activity or exertion level of the patient;

a HR analyzer circuit configured to receive a cardiac activity signal from the patient, extract a HR feature from the sensed cardiac activity signal, and perform a plurality of measurements of the HR feature in response to the received time of day meets a specified criterion or the received physical activity or exertion level exceeds a specified threshold value; and

a signal metrics generator circuit configured to calculate one or more signal metrics using the plurality of measurements of the HR feature; and

a risk stratifier circuit configured to generate a composite risk indicator (CRI) using the one or more signal metrics, the CRI indicative of the likelihood of the patient developing a future event indicative of a new disease or worsening an existing disease.

13. The system of claim 12, wherein the risk stratifier circuit is configured to generate two or more categorical risk levels using a comparison between the composite risk indicator and a reference measure, the two or more categorical risk levels indicative of elevated risk of the patient developing a future event indicative of worsening heart failure.

14. A method, comprising:

detecting a contextual condition associated with a patient, the contextual condition including information indicative or correlative of elevated metabolic demand of a patient;

sensing from the patient a cardiac activity signal and extracting a HR feature from the sensed cardiac activity signal;

measuring a plurality of measurements of the HR feature in response to the detected patient contextual condition meeting a specified criterion; and

calculating one or more signal metrics using the plurality of measurements of the HR feature.

15. The method of claim 14, wherein calculating the one or more signal metrics includes calculating a statistical or morphological parameter extracted from the plurality of measurements of the HR feature.

16. The system of claim 14, wherein calculating the one or more signal metrics includes calculating a representative HR (HR_Rep) representing an n-th percentile rank indicative of a relative amount of HR measurements among the plurality of measurements falling below or equal to the HR_Rep, the n-th percentile rank higher than 50-th percentile.

17. The method of claim 14, wherein detecting the contextual condition includes detecting time of a day, and wherein measuring the plurality of HR measurements includes measuring a plurality of measurements of the HR feature during specified time of a day indicative or correlative of an elevated metabolic demand of the patient.

18. The method of claim 14, wherein detecting the contextual condition includes detecting a physical activity or exertion level of the patient, and wherein measuring the plurality of HR measurements includes measuring a plurality of measurements of the HR feature in response to the detected physical activity or exertion level exceeding a specified activity level threshold.

19. The method of claim 14, wherein detecting the contextual condition includes, using one or more physiologic sensors, detecting at least one of a time of transition from a sleep state to an awake state, an increase in body temperature, an increase in heart rate, an increase in pressure, a decrease in physical activity or exertion level, or an increase in respiration rate.

20. The method of claim 14, further comprising at least one of:

detecting a target physiologic event indicative of worsening heart failure using the one or more signal metrics;

or

generating a composite risk indicator using the selected one or more signal metrics and classifying the patient into one of two or more categorical risk levels, the composite risk indicator indicative of the likelihood of the patient developing a future event indicative of worsening heart failure.