SYSTEM AND METHODS FOR AUTOMATIC DIFFERENTIAL DIAGNOSIS OF WORSENING HEART FAILURE

• US 20150327776A1
• Filed: 04/28/2015
• Published: 11/19/2015
• Est. Priority Date: 05/15/2014
• Status: Abandoned Application

• Current Assignee: Cardiac Pacemakers Incorporated (Boston Scientific Corporation)
• Original Assignee: Cardiac Pacemakers Incorporated (Boston Scientific Corporation)
• Inventors: Zhang, Yi, Wariar, Ramesh, Thakur, Pramodsingh Hirasingh, Averina, Viktoria A., An, Qi, Sweeney, Robert J., Thompson, Julie A.
• Application Number:
  US14/698,178
• Publication Number:
  US 20150327776A1
• Time in Patent Office: Days
• US Class Current: 1/1
• 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 5/0809 by impedance pneumography
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/4842 Monitoring progression or stage of a disease
• 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 5/74 Details of notification to user or communication with user or patient indicating measured values G01D7/00; user input means input or output arrangements for computers G06F3/00
• CPC Class Codes: A61B 7/04 Electric stethoscopes microphones, acoustic transducers therefor H04R
• 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

Abstract

Devices and methods for differentially diagnosing between worsening heart failure (HF) and other diseases or medical conditions are described. A medical system can receive patient information including one or more physiologic signals, and detect a respective physiologic feature from each of the one or more received physiologic signals. The medical system can include a differential diagnosis circuit that generates one or more signal metrics, receive two or more candidate conditions associated with the change in patient physical or physiological status, and determine a respective diagnostic score for each of the candidate conditions. The diagnostic score can indicate likelihood the change in the patient physical or physiologic status being caused by the corresponding candidate condition. A user interface can be provided to generate a presentation of the detected physiologic features and the diagnostic scores associated with the candidate conditions.

Claims

1. A system, comprising:

a patient information receiver circuit configured to receive patient information including one or more physiologic signals obtained from a patient;

a physiologic data analyzer circuit configured to detect, from each of the one or more received physiologic signals, a respective physiologic feature indicative of patient physical or physiologic status; and

a differential diagnosis circuit configured to;

using the physiologic features, generate one or more signal metrics indicative or correlative of a change in the patient physical or physiologic status;

receive two or more candidate conditions associated with the change in patient physical or physiological status; and

determine, for the two or more candidate conditions, a respective diagnostic score using the one or more signal metrics, the diagnostic score indicative of likelihood the change in the patient physical or physiologic status being caused by the corresponding candidate condition.

2. The system of claim 1, wherein the differential diagnosis circuit is configured to receive two or more candidate conditions including a worsening heart failure (HF) event, a pulmonary disease, a renal disease, an indication of sleep disordered breathing, an atrial or ventricular arrhythmia, an indication of a surgical procedure, or an indication of infection.

3. The system of claim 1, wherein the differential diagnosis circuit is configured to determine the respective diagnostic score using a rule-based model.

4. The system of claim 3, wherein the rule-based model includes a multi-level decision tree model, and the differential diagnosis circuit determines the respective diagnostic score in response to the one or more signal metrics respectively meeting a specified criterion.

5. The system of claim 3, wherein the differential diagnosis circuit determines the respective diagnostic score using at least a temporal relationship between a first signal metric and a different second signal metric.

6. The system of claim 1, wherein the differential diagnosis circuit is configured to determine the respective diagnostic score using a probabilistic model including a descriptor of statistical distribution of the one or more signal metrics, and wherein the diagnostic score includes a probability value of the patient experiencing, or developing in future, one of the two or more candidate conditions.

7. The system of claim 1, wherein the differential diagnosis circuit is configured to compute one or more signal metrics using a plurality of measurements of the physiologic signal feature, the one or more signal metrics including a statistical metric or a morphological metric.

8. The system of claim 1, wherein the patient information receiver circuit is configured to receive a thoracic impedance signal using an impedance sensor, and the differential diagnosis circuit is configured to compute a change in one or more of a respiration strength measurement, a respiration rate, or a respiration pattern using the thoracic impedance signal.

9. The system of claim 1, wherein the patient information receiver circuit is configured to receive a heart sound (HS) signal using a heart sound sensor, and the differential diagnosis circuit is configured to compute a change in S3 heart sound intensity.

10. The system of claim 1, wherein the patient information receiver circuit is configured to receive the patient information including one or more of chronic disease information, prior medical procedure or therapy information, patient demographic information, patient vital sign information, patient symptom information, or patient comorbidity condition.

11. The system of claim 1, further comprising a user interface configured to provide a presentation of one or more of the detected physiologic features or the diagnostic scores associated with the two or more candidate conditions.

12. The system of claim 11, wherein:

the differential diagnosis circuit is further configured to determine a most probable diagnosis using a comparison among the diagnostic scores, the most probable diagnosis associated with the highest diagnostic score; and

the user interface is configured to provide the presentation including the most probable diagnosis.

13. A method of operating a medical system, the method comprising:

receiving patient information including one or more physiologic signals obtained from a patient;

detecting from each of the one or more received physiologic signals a respective physiologic feature indicative of patient physical or physiologic status;

generating, by using the physiologic features, one or more signal metrics indicative or correlative of a change in the patient physical or physiologic status;

receiving two or more candidate conditions associated with the change in patient physical or physiological status; and

determining, for the two or more candidate conditions, a respective diagnostic score using the one or more signal metrics, the diagnostic score indicative of likelihood the change in the patient physical or physiologic status being caused by the corresponding candidate condition.

14. The method of claim 13, wherein receiving two or more candidate conditions includes receiving two or more of a worsening heart failure (HF) event, a pulmonary disease, a renal disease, an indication of sleep disordered breathing, an atrial or ventricular arrhythmia, an indication of a surgical procedure, or an indication of infection.

15. The method of claim 13, wherein determining the respective diagnostic score includes calculating the diagnostic score using a rule-based model.

16. The method of claim 15, wherein determining the respective diagnostic score includes calculating the diagnostic score using at least a temporal relationship between a first signal metric and a different second signal metric.

17. The method of claim 13, wherein determining the respective diagnostic score includes calculating the diagnostic score using a probabilistic model including a descriptor of statistical distribution of the one or more signal metrics, the diagnostic score includes a probability value of the patient experiencing, or developing in future, one of the two or more candidate conditions.

18. The method of claim 13, wherein generating one or more signal metrics includes receiving a plurality of measurements of the physiologic signal feature, and generating a statistical metric or a morphological metric using the plurality of the measurements of the physiologic signal feature.

19. The method of claim 13, wherein receiving the patient information includes receiving one or more of chronic disease information, prior medical procedure or therapy information, patient demographic information, patient vital sign information, patient symptom information, or patient comorbidity condition.

20. The method claim 13, further comprising determining a most probable diagnosis using a comparison among the diagnostic scores, the most probable diagnosis associated with the highest diagnostic score, and generating a presentation including one or more of the detected physiologic features, the diagnostic scores associated with the two or more candidate conditions, or the most probable diagnosis.