HEART FAILURE PROGRESSION MONITORING BASED ON LV CONDUCTION PATTERN AND MORPHOLOGY TRENDS

• US 20190328256A1
• Filed: 04/26/2018
• Published: 10/31/2019
• Est. Priority Date: 04/26/2018
• Status: Active Grant

• Current Assignee: Pacesetter Incorporated (Abbott Laboratories)
• Original Assignee: Pacesetter Incorporated (Abbott Laboratories)
• Inventors: Badie, Nima, Mangual-Soto, Jan O., McSpadden, Luke C., Richer, Louis-Philippe, Gill, Jong
• Granted Patent: US 10,582,866 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/287 Holders for multiple electrodes, e.g. electrode catheters for electrophysiological study [EPS]
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/346 Analysis of electrocardiograms
• CPC Class Codes: A61B 5/35 by template matching
• CPC Class Codes: A61B 5/352 Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• CPC Class Codes: A61B 5/686 Permanently implanted devices, e.g. pacemakers, other stimulators, biochips A61B5/6861 takes precedence
• 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: A61N 1/36842 Multi-site stimulation in the same chamber
• CPC Class Codes: A61N 1/3702 Physiological parameters A61N1/365 takes precedence; evoked response A61N1/371
• CPC Class Codes: A61N 1/3704 Circuits specially adapted therefor, e.g. for sensitivity control
• CPC Class Codes: A61N 1/3925 Monitoring; Protecting
• 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

Computer implemented methods, devices and systems for monitoring a trend in heart failure (HF) progression are provided. The method comprises sensing left ventricular (LV) activation events at multiple LV sensing sites along a multi-electrode LV lead. The activation events are generated in response to an intrinsic or paced ventricular event. The method implements program instructions on one or more processors for automatically determining a conduction pattern (CP) across the LV sensing sites based on the LV activation events, identifying morphologies (MP) for cardiac signals associated with the LV activation events and repeating the sensing, determining and identifying operations, at select intervals, to build a CP collection and an MP collection. The method calculates an HF trend based on the CP collection and MP collection and classifies a patient condition based on the HF trend to form an HF assessment.

Claims

1. A computer implemented method for monitoring a trend in heart failure (HF) progression, the method comprising:

sensing left ventricular (LV) activation events at multiple LV sensing sites along a multi-electrode LV lead, where the activation events are generated in response to an intrinsic or paced ventricular event;

implementing program instructions on one or more processors for automatically;

determining a conduction pattern (CP) across the LV sensing sites based on the LV activation events;

identifying morphologies (MP) for cardiac signals associated with the LV activation events;

repeating the sensing, determining and identifying operations, at select intervals, to build a CP collection and an MP collection;

calculating an HF trend based on the CP collection and MP collection; and

classifying a patient condition based on the HF trend to form an HF assessment.

2. The method of claim 1, wherein the calculating the HF trend comprises:

calculating a CP-based trend indicator by applying an AT metric to the CP collection; and

calculating an MP-based trend indicator by applying an MP metric to the MP collection.

3. The method of claim 2, wherein the applying the AT metric comprises applying at least one of a dyssynchrony metric, conduction nonuniformity metric, conduction velocity metric, fastest conduction pathway metric or chronotropic incompetence metric.

4. The method of claim 2, wherein the applying the MP metric comprises applying at least one of an electrical synchrony metric, electrically viable local tissue metric, pacing depolarization integral metric, slope based electrical excited ability metric or template matching score metric.

5. The method of claim 4, wherein the applying the MP metric comprises applying the MP metric in connection with interelectrode differences between the morphologies associated with different LV sensing sites.

6. The method of claim 2, further comprising comparing the CP-based and MP-based trend indicators to corresponding thresholds, the classifying the patient condition to form the HF assessment based on the comparing.

7. The method of claim 1, wherein the calculating the HF trend comprises calculating a CP-based trend indicator and an MP-based trend indicator, and the classifying comprises comparing the CP-based and MP-based trend indicators to corresponding thresholds to classify the patient condition as one of improved, deteriorated or no change.

8. The method of claim 1, wherein the sensing, determining and identifying operations are performed by an implantable medical device, while at least a portion of the calculating and classifying operations are performed by at least one of an external device and a remote server.

9. The method of claim 1, wherein the sensing, determining, identifying, calculating and classifying operations are performed by an implantable medical device, the method further comprising transmitting the HF assessment from the implantable medical device to at least one of an external device and a remote server.

10. A computer implemented method for monitoring a trend in heart failure (HF) progression in connection with left ventricular (LV) activation events sensed, over a select interval, at multiple LV sensing sites along a multi-electrode LV lead, where the activation events are generated in response to an intrinsic or paced ventricular event, the method comprising:

implementing program instructions on one or more processors for automatically;

obtaining a conduction pattern (CP) collection of conduction patterns across the LV sensing sites, and a morphology (MP) collection of MPs for cardiac signals associated with the LV activation events;

calculating an HF trend based on the CP collection and MP collection; and

classifying a patient condition based on the HF trend to form an HF assessment.

11. The method of claim 10, wherein the calculating the HF trend comprises automatically calculating, at a local external device and/or a remote server:

a CP-based trend indicator by applying an activation time (AT) metric to the CP collection; and

an MP-based trend indicator by applying an MP metric to the MP collection.

12. The method of claim 11, wherein the applying the AT metric comprises applying at least one of a dyssynchrony metric, conduction nonuniformity metric, conduction velocity metric, fastest conduction pathway metric or chronotropic incompetence metric.

13. The method of claim 11, wherein the applying the MP metric comprises applying at least one of an electrical synchrony metric, electrically viable local tissue metric, pacing depolarization integral metric, slope based electrical excited ability metric or template matching score metric.

14. The method of claim 10, wherein the obtaining the CP collection and MP collection comprises at least one of i) accessing memory of an external device or remote server that stores the CP collection and MP collection, ii) receiving the CP collection and MP collection over a wireless communications link between an implantable medical device and a local external device, or iii) receiving the CP collection and MP collection at a remote server over a network connection.

15. The method of claim 10, further comprising:

sensing the LV activation events at the multiple LV sensing sites along the multi-electrode LV lead;

determining the conduction pattern across the LV sensing sites based on the LV activation events;

identifying MPs for cardiac signals associated with the LV activation events; and

repeating the sensing, determining and identifying operations, at select intervals, to build the CP collection and the MP collection.

16. A system for monitoring a trend in heart failure (HF) progression, the system comprising:

a multi-electrode LV lead to sense left ventricular (LV) activation events at multiple LV sensing sites along the multi-electrode LV lead, where the activation events are to be generated in response to an intrinsic or paced ventricular event;

memory to store program instructions, and one or more processors that, when executing the program instructions, are configured to automatically;

determine a conduction pattern (CP) across the LV sensing sites based on the LV activation events;

identify morphologies (MP) for cardiac signals associated with the LV activation events;

repeat the sensing, determining and identifying operations, at select intervals, to build a CP collection and an MP collection;

calculate an HF trend based on the CP collection and MP collection; and

classify a patient condition based on the HF trend to form an HF assessment.

17. The system of claim 16, wherein the one or more processors are configured to calculate the HF trend by:

calculating a CP-based trend indicator by applying an activation time (AT) metric to the CP collection;

calculating an MP-based trend indicator by applying an MP metric to the MP collection; and

calculating the HF trend based on the CP-based and MP-based trend indicators.

18. The system of claim 16, wherein the one or more processors are configured to calculate the HF trend by comparing current and historic CP-based trend indicators and comparing current and historic MP-based trend indicators.

19. The system of claim 16, wherein the one or more processors are configured calculating the HF trend by calculating a CP-based trend indicator and an MP-based trend indicator, and the classifying comprises comparing the CP-based and MP-based trend indicators to corresponding thresholds to classify the patient condition as one of improved, deteriorated or no change.

20. The system of claim 16, further comprising an implantable medical device (IMD) coupled to the multi-electrode LV lead, and a local external device configured to wirelessly communicate with the IMD, the local external device configured to communicate over a network with a remote server, the one or more processors comprising at least a first processor housed within the IMD and configured to perform at least the determining and identifying operations.