ACTIVE IMPLANTABLE MEDICAL DEVICE COMPRISING MEANS FOR THE DIAGNOSIS OF HEART FAILURE

• US 20130197378A1
• Filed: 01/31/2013
• Published: 08/01/2013
• Est. Priority Date: 01/31/2012
• Status: Active Grant

• Current Assignee: Sorin CRM USA (MicroPort Scientific Corp.)
• Original Assignee: Sorin CRM USA (MicroPort Scientific Corp.)
• Inventors: Giorgis, Lionel, Dumont, Jrme
• Granted Patent: US 8,938,286 B2
• Time in Patent Office: Days
• US Class Current: 600/509
• CPC Class Codes: A61B 5/02 Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
• 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 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/02455 provided with high/low alarm devices
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• 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: 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

Abstract

Methods, devices, and processor-readable storage media are provided for the diagnosis of heart failure. A method in this context includes collecting, using an implantable device, reference episodes, the reference episodes comprising, at least one of: electrical activity signals of a myocardium; myocardium hemodynamic activity signals, or indicators reflecting variation of physical parameters, variation of activity, and variation of hemodynamic phases between phases of effort and phases of recovery; generating an in-suspicion model-cycle and an off-suspicion model-cycle based on the reference episodes; and determining whether to generate an early heart failure alert, based on a difference between the in-suspicion model-cycle and the off-suspicion model-cycle.

Claims

1. A method of diagnosis of heart failure, the method comprising:

collecting, using an implantable device, reference episodes, the reference episodes comprising, at least one of;

electrical activity signals of a myocardium;

myocardium hemodynamic activity signals, or indicators reflecting variation of physical parameters, variation of activity, and variation of hemodynamic phases between phases of effort and phases of recovery;

generating an in-suspicion model-cycle and an off-suspicion model-cycle based on the reference episodes; and

determining whether to generate an early heart failure alert, based on a difference between the in-suspicion model-cycle and the off-suspicion model-cycle.

2. The method of claim 1, wherein the in-suspicion model-cycle or off-suspicion model-cycle comprises at least one endocardial electrogram beat recorded during an episode fulfilling predetermined conditions of heart rate and patient activity.

3. The method of claim 1, wherein the in-suspicion model-cycle or off-suspicion model-cycle comprises at least one beat of an endocardiac acceleration signal recorded during an episode fulfilling predetermined conditions of heart rate and patient activity.

4. The method of claim 1, wherein the in-suspicion model-cycle or off-suspicion model-cycle comprises at least one minute ventilation versus heart-rate characteristic pattern that was recorded during an episode comprising an effort phase followed by a recovery phase of the patient.

5. The method of claim 1, further comprising:

updating the in-suspicion model-cycle and off-suspicion model-cycle; and

storing the updated in-suspicion model-cycle and updated off-suspicion model-cycle.

6. The method of claim 5, wherein each of the in-suspicion model-cycle and the off-suspicion model-cycle is a combination of a previous model-cycle with at least one older model-cycle.

7. The method of claim 1, wherein generating the early heart failure alert comprises:

generating at least one index of a clinical condition of a patient, based on the difference between the in-suspicion model-cycle and the off-suspicion model-cycle; and

generating an early heart failure alert based on the at least one index.

8. The method of claim 7, wherein the at least one index of a clinical condition is updated periodically.

9. The method of claim 7, wherein the at least one index is calculated based on an average of model-cycles.

10. A device for the diagnosis of heart failure, comprising:

a processor, configured to;

collect data relating to reference episodes, the reference episodes comprising, at least one of;

electrical activity signals of a myocardium;

myocardium hemodynamic activity signals, or indicators reflecting variation of physical parameters, variation of activity, and variation of hemodynamic phases between phases of effort and phases of recovery;

generate an in-suspicion model-cycle and an off-suspicion model-cycle based on the reference episode data; and

determine whether to generate an early heart failure alert, based on a difference between the in-suspicion model-cycle and the off-suspicion model-cycle.

11. The device of claim 10, wherein the in-suspicion model-cycle or off-suspicion model-cycle comprises at least one endocardial electrogram beat recorded during an episode fulfilling predetermined conditions of heart rate and patient activity.

12. The device of claim 10, wherein the in-suspicion model-cycle or off-suspicion model-cycle comprises at least one beat of an endocardiac acceleration signal recorded during an episode fulfilling predetermined conditions of heart rate and patient activity.

13. The device of claim 10, wherein the in-suspicion model-cycle or off-suspicion model-cycle comprises at least one minute ventilation versus heart-rate characteristic pattern that was recorded during an episode comprising an effort phase followed by a recovery phase of the patient.

14. The device of claim 10, further comprising:

a memory;

wherein the processor is further configured to;

update the in-suspicion model-cycle and off-suspicion model-cycle; and

store, in the memory, the updated in-suspicion model-cycle and updated off-suspicion model-cycle.

15. The device of claim 14, wherein each of the in-suspicion model-cycle and the off-suspicion model-cycle is a combination of a previous model-cycle with at least one older model-cycle.

16. The device of claim 10, wherein generating the early heart failure alert comprises:

generating at least one index of a clinical condition of a patient, based on the difference between the in-suspicion model-cycle and the off-suspicion model-cycle; and

generating an early heart failure alert based on the at least one index.

17. The device of claim 16, wherein the at least one index of a clinical condition is updated periodically.

18. The device of claim 16, wherein the at least one index is calculated based on an average of model-cycles.

19. The device of claim 10, wherein the device is a pacing, resynchronization, or defibrillation device or an implant for diagnostic purposes.

20. A processor-readable, non-transitory storage medium having instructions stored thereon that, when executed by a processor, cause the processor to:

collect data relating to reference episodes, the reference episodes comprising, at least one of;

electrical activity signals of a myocardium;

myocardium hemodynamic activity signals, or indicators reflecting variation of physical parameters, variation of activity, and variation of hemodynamic phases between phases of effort and phases of recovery;

generate an in-suspicion model-cycle and an off-suspicion model-cycle based on the reference episode data; and

determine whether to generate an early heart failure alert, based on a difference between the in-suspicion model-cycle and the off-suspicion model-cycle.