System and method for diagnosing and monitoring congestive heart failure

• US 8,366,629 B2
• Filed: 03/28/2011
• Issued: 02/05/2013
• Est. Priority Date: 11/16/1999
• Status: Expired due to Fees

• Current Assignee: Cardiac Pacemakers Incorporated (Boston Scientific Corporation)
• Original Assignee: Cardiac Pacemakers Incorporated (Boston Scientific Corporation)
• Inventors: Bardy, Gust H.
• Primary Examiner(s): EVANISKO, GEORGE ROBERT
• Application Number:
  US13/073,301
• Publication Number:
  US 20110172548A1
• Time in Patent Office: 680 Days
• Field of Search: 128/920, 128923-925, 600/508, 600/513, 600/515
• US Class Current: 600/508
• CPC Class Codes: A61B 2505/03 Intensive care
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/0004 characterised by the type of physiological signal transmitted
• CPC Class Codes: A61B 5/0006 ECG or EEG signals
• CPC Class Codes: A61B 5/0031 Implanted circuitry
• 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 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• 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 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/076 Permanent implantations telemetry using implanted circuitry A61B5/0031; implanted stimulators for therapy A61N1/3605, A61N1/362, A61N1/372
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/1112 Global tracking of patients, e.g. by using GPS
• CPC Class Codes: A61B 5/145 Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissuemeasuring of blood pressure or blood flow A61B5/02; non-radiation detecting or locating of foreign bodies in blood A61B5/06
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14546 for measuring analytes not otherwise provided for, e.g. ions, cytochromes
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/346 Analysis of electrocardiograms
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/36 : Detecting PQ interval, PR interval or QT interval
• CPC Class Codes: A61B 5/486 : Bio-feedback A61B5/375 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 5/7275 : 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 5/7278 : Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• 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: G16H 10/60 : for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 15/00 : ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 40/67 : for remote operation
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: Y10S 128/923 : by comparison of patient data to other data

Abstract

A system for diagnosing and monitoring congestive heart failure for automated remote patient care is presented. A database stores a plurality of monitoring sets relating to patient information recorded on a substantially continuous basis. A server retrieving and processing the monitoring sets includes a comparison module determining patient status changes by comparing at least one recorded measure from one of the monitoring sets to at least one other recorded measure from another of the monitoring sets with both recorded measures relating to a type of patient information, and an analysis module testing each patient status change for one of an absence, an onset, a progression, a regression, and a status quo of congestive heart failure against a predetermined indicator threshold corresponding to a type of patient information as the recorded measures. The indicator threshold corresponds to a quantifiable physiological measure of a pathophysiology indicative of congestive heart failure. Hysteresis parameters may be used as a temporally-defined threshold for changes in pathophysiology.

Claims

1. An analysis system for processing physiometry for use in remote automated congestive heart failure patient management, comprising:

a database storing;

identification of a patient;

medical history of the patient;

peer-group guidelines; and

collected device measures from a medical device;

an interface periodically receiving the collected device measures; and

a processing module, comprising;

a data processor determining derived device measures based on the collected device measures;

an analyzer evaluating the collected and derived device measures and quantifying a pathophysiology indicative of congestive heart failure; and

a feedback module configured for receiving hysteresis parameters and adapting the hysteresis parameters depending upon the patient medical history and the peer-group guidelines.

2. An analysis system according to claim 1, further comprising:

indicator thresholds for the pathophysiology in terms of at least one of a cardiovascular and cardiopulmonary disease; and

the processing module further determining a patient status indicator by analyzing one or more of the collected and derived device measures against the indicator thresholds.

3. An analysis system according to claim 2, further comprising:

the processing module identifying at least one of a change and a status quo of congestive heart failure.

4. An analysis system according to claim 2, wherein the processing module further determines the patient status indicator by analyzing one or more of the collected and derived device measures against the hysteresis parameters.

5. An analysis system according to claim 1, the feedback module further configured for receiving quality of life measures electronically recorded by the patient contemporaneous to recordation of the collected device measures.

6. An analysis system according to claim 1, wherein one or more of the collected and derived device measures are selected from the group comprising self-referencing, peer group, and general population physiometric measures.

7. An analysis system according to claim 1, further comprising:

a programmer configured for reprogramming parameters for the medical device, wherein the database stores the collected device measures subsequent to reprogramming.

8. The system of claim 1, the database further storing a treatment profile.