SYSTEM AND METHOD FOR ANALYZING A PATIENT STATUS FOR CONGESTIVE HEART FAILURE FOR USE IN AUTOMATED PATIENT CARE

• US 20130123651A1
• Filed: 12/31/2012
• Published: 05/16/2013
• Est. Priority Date: 11/16/1999
• Status: Active Grant

• Current Assignee: Cardiac Pacemakers Incorporated (Boston Scientific Corporation)
• Original Assignee: Cardiac Pacemakers Incorporated (Boston Scientific Corporation)
• Inventors: Bardy, Gust
• Granted Patent: US 9,232,900 B2
• Time in Patent Office: Days
• 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 5/316 Modalities, i.e. specific diagnostic methods
• 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 5/486 : 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. A medical device providing physiometry for use in remote automated congestive heart failure patient management, comprising:

one or more sensors to directly sense raw physiometry for a patient;

sensing circuitry to regularly monitor and record the raw physiometry as collected device measures;

memory to temporarily store the collected device measures pending interrogation by an externally interfaceable device; and

a telemetry interface to provide access to the collected device measures, wherein derived device measures are thereafter determined to provide derivative physiometry based on the collected device measures and the collected and derived device measures quantify a pathophysiology indicative of congestive heart failure.

2. The medical device of claim 1, further comprising:

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

3. The medical device of claim 1, wherein the medical device comprises one of an implantable medical device and an external medical device.

4. The medical device of claim 1, wherein one or more of the collected and derived device measures are selected from the group comprising self referencing and and general population physiometric measures.

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

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

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

determining derived device measures to provide derivative physiometry based on the collected device measures, wherein the collected and derived device measures quantify a pathophysiology indicative of congestive heart failure.

6. The method of claim 5, further comprising:

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

7. The method of claim 6, further comprising:

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

8. The method of claim 5, further comprising:

determining a patient status by evaluating a plurality of wellness indicators to identify at least one of a change and a status quo of congestive heart failure.

9. The method of claim 5, further comprising:

specifying hysteresis parameters to define a temporally-defined threshold for changes in the pathophysiology.

10. The method of claim 9, further comprising:

determining a wellness indicator by analyzing one or more of the collected and derived device measures against the hysteresis parameters.

11. The method of claim 10, wherein the hysteresis parameters allow the determination to not be changed upon temporary changes but upon the persistence of the change.

12. The method of claim 9, further comprising:

analyzing the hysteresis parameters against one or more of the collected and derived device measures; and

determining an indicator of patient status from analysis of the hysteresis parameters.

13. The method of claim 12, further comprising:

defining a threshold for the pathophysiology in terms of at least one of a cardiovascular and a cardiopulmonary disease.

14. The method of claim 12, further comprising:

evaluating the indicator of patient status to identify at least one of a change and a status quo of congestive heart failure.

15. The method of claim 12, wherein the hysteresis parameters allow the determination to not be changed upon temporary changes but upon the persistence of the change.

16. The method of claim 5, further comprising:

receiving quality of life measures to provide qualified voice feedback data electronically recorded by the patient contemporaneous to recordation of the collected device measures.

17. The method of claim 5 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.

18. The method of claim 5, further comprising:

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

19. The method of claim 5, wherein the medical device comprises one of an implantable medical device and an external medical device.

20. The method of claim 5, 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.