System and method for analyzing a patient status for congestive heart failure for use in automated patient care

• US 7,972,274 B2
• Filed: 06/24/2004
• Issued: 07/05/2011
• 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:
  US10/876,118
• Publication Number:
  US 20040230127A1
• Time in Patent Office: 2,567 Days
• Field of Search: 600/508, 600/513, 128/920, 128/923, 128/924, 128/925
• 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 and method for providing diagnosis and monitoring of congestive heart failure for use in automated patient care is described. At least one recorded physiological measure is compared to at least one other recorded physiological measure on a substantially regular basis to quantify a change in patient pathophysiological status for equivalent patient information. An absence, an onset, a progression, a regression, and a status quo of congestive heart failure is evaluated dependent upon the change in patient pathophysiological status.

Claims

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

a medical device;

one or more patient records, wherein each patient record comprises;

information to identify a patient enrolled in automated patient care and comprising at least one of treatment profile and medical history;

collected device measures to provide raw physiometry for the patient that was regularly monitored and recorded by a the medical device; and

derived device measures calculated from the collected device measures to provide derivative physiometry;

a database configured to store the one or more patient records;

an interface configured to periodically receive the collected device measures;

a processing module, comprising;

a data processor configured to calculate the derived device measures; and

an analyzer configured to evaluate the collected and derived device measures and to quantify a pathophysiology indicative of congestive heart failure; and

hysteresis parameters to temporally define a threshold for changes in the pathophysiology, wherein the analyzer further analyzes the hysteresis parameters against one or more of the collected and derived device measures and determines an indicator of patient status from said analysis, wherein the hysteresis parameters allow said determination to not be changed upon temporary changes but upon the persistence of the change.

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

evaluation parameters configured to define a threshold for the pathophysiology in terms of at least one of a cardiovascular and cardiopulmonary disease; and

the indicator of patient status determined by the analyzer, wherein the analyzer analyzes one or more of the collected and derived device measures against the evaluation parameters.

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

a patient status determined by the analyzer, wherein the analyzer evaluates a plurality of the indicators of patient status to identify at least one of a change and a status quo of congestive heart failure.

4. An analysis system according to claim 1, wherein each patient care record further comprises a remote client processing voice feedback into a set of quality of life measures, the voice feedback having been spoken by the patient substantially contemporaneous to the recordation of the collected device measures.

5. 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.

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

programming parameters for the medical device, wherein the collected device measures are recorded subsequent to application of the programming parameters.

7. An analysis system according to claim 1, wherein the medical device comprises one of an implantable medical device and an external medical device.

8. A method for processing physiometry for use in remote automated congestive heart failure patient management, comprising:

regularly monitoring and recording collected device measures with a medical device;

storing patient records into a database, wherein the patient records each comprise;

information to identify a patient enrolled in automated patient care and comprising at least one of treatment profile and medical history;

collected device measures to provide raw physiometry for the patient; and

derived device measures based on the collected device measures to provide derivative physiometry;

periodically receiving the collected device measures with an interface; and

using a data processor to determine derived device;

using an analyzer to evaluate the collected and derived device measures;

specifying hysteresis parameters to temporally define a threshold for changes in the pathophysiology;

further determining an indicator of patient status by analyzing one or more of the collected and derived device measures against the hysteresis parameters; and

quantifying a pathphysiology indicative of congestive heath failure, wherein the hysteresis parameters allow said determination to not be changed upon temporary changes but upon the persistence of the change.

9. A method according to claim 8, further comprising:

specifying evaluation parameters to define a threshold for the pathophysiology in terms of at least one of a cardiovascular and cardiopulmonary disease; and

determining the indicator of patient status by analyzing one or more of the collected and derived device measures against the evaluation parameters.

10. A method according to claim 9, further comprising:

determining a patient status comprising evaluating a plurality of the indicators of patient status and identifying at least one of a change and a status quo of congestive heart failure.

11. A method according to claim 8, further comprising:

receiving quality of life measures, wherein the quality of life measures are in the form of voice feedback having been spoken by the patient contemporaneous to recordation of the collected device measures.

12. A method according to claim 8, 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.

13. A method according to claim 8, further comprising:

generating reprogramming parameters for the medical device, wherein the collected device measures are recorded subsequent to application of the reprogramming parameters.

14. A method according to claim 8, wherein the medical device comprises one of an implantable medical device and an external medical device.

15. The method according to claim 8, wherein the medical device comprises an implantable medical device.

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

an implantable medical device;

one or more patient records, wherein each patient record comprises;

information to identify a patient enrolled in automated patient care and comprising at least one of treatment profile and medical history;

collected device measures to provide raw physiometry for the patient that was regularly monitored and recorded by the medical device; and

derived device measures calculated from the collected device measures to provide derivative physiometry;

a database configured to store the one or more patient records;

an interface configured to periodically receive the collected device measures;

a processing module, comprising;

a data processor configured to calculate the derived device measures; and

an analyzer configured to evaluate the collected and derived device measures and to quantify a pathophysiology indicative of congestive heart failure; and

hysteresis parameters to temporally define a threshold for changes in the pathophysiology wherein the analyzer further analyzes the hysteresis parameters against one or more of the collected and derived device measures and determines an indicator of patient status from said analysis, wherein the hysteresis parameters allow said determination to not be changed upon temporary changes but upon the persistence of the change.

17. An analysis system according to claim 16, wherein each patient care record further comprises

a remote client processing voice feedback into a set of quality of life measures, the voice feedback having been spoken by the patient substantially contemporaneous to the recordation of the collected device measures.

18. An analysis system according to claim 16, 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.

19. An analysis system according to claim 16, further comprising:

programming parameters for the medical device, wherein the collected device measures are recorded subsequent to application of the programming parameters.