Method and apparatus for personalized physiologic parameters

• US 9,173,615 B2
• Filed: 09/23/2014
• Issued: 11/03/2015
• Est. Priority Date: 04/05/2010
• Status: Active Grant

• Current Assignee: Medtronic Monitoring, Inc. (Medtronic Plc)
• Original Assignee: Medtronic Monitoring, Inc. (Medtronic Plc)
• Inventors: Katra, Rodolphe, Chakravarthy, Niranjan, Libbus, Imad
• Primary Examiner(s): TOWA, RENE T
• Application Number:
  US14/494,241
• Publication Number:
  US 20150105647A1
• Time in Patent Office: 406 Days
• Field of Search: 600/382, 600/481, 600/506, 600/529, 600/547
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0004 characterised by the type of physiological signal transmitted
• CPC Class Codes: A61B 5/0075 by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy A61B5/0071 takes precedence
• 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 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/0531 Measuring skin impedance
• CPC Class Codes: A61B 5/0537 Measuring body composition by impedance, e.g. tissue hydration or fat content
• CPC Class Codes: A61B 5/0809 by impedance pneumography
• CPC Class Codes: A61B 5/091 Measuring volume of inspired or expired gases, e.g. to determine lung capacity
• CPC Class Codes: A61B 5/1072 measuring distances on the body, e.g. measuring length, height or thickness A61B5/1076 takes precedence
• CPC Class Codes: A61B 5/1073 Measuring volume, e.g. of limbs
• CPC Class Codes: A61B 5/1075 for measuring dimensions by non-invasive methods, e.g. for determining thickness of tissue layer A61B8/0858 takes precedence
• CPC Class Codes: A61B 5/1079 using optical or photographic means
• CPC Class Codes: A61B 5/4872 Body fat
• CPC Class Codes: A61B 5/4875 Hydration status, fluid retention of the body
• CPC Class Codes: A61B 5/4878 Evaluating oedema
• CPC Class Codes: A61B 5/6832 using adhesives A61B5/259 takes precedence
• CPC Class Codes: A61B 5/6833 Adhesive patches
• CPC Class Codes: A61B 5/7225 Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation A61B5/30, A61B5/316 take precedence; transformers for specific applications H01F38/00; amplifiers H03F
• 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 5/7282 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: A61B 6/482 : involving multiple energy imaging
• CPC Class Codes: A61N 1/00 : Electrotherapy; Circuits therefor A61N2/00 takes precedence; irradiation apparatus A61N5/00

Abstract

Methods and apparatus combine patient measurement data with demographic or physiological data of the patient to determine an output that can be used to diagnose and treat the patient. A customized output can be determined based the demographics of the patient, physiological data of the patient, and data of a population of patients. In another aspect, patient measurement data is used to predict an impending cardiac event, such as acute decompensated heart failure. At least one personalized value is determined for the patient, and a patient event prediction output is generated based at least in part on the personalized value and the measurement data. For example, bioimpedance data may be used to establish a baseline impedance specific to the patient, and the patient event prediction output generated based in part on the relationship of ongoing impedance measurements to the baseline impedance. Multivariate prediction models may enhance prediction accuracy.

Claims

1. A method of predicting an impending patient cardiac event, the method comprising:

receiving from an adherent device impedance data measured with respect to a patient;

receiving from an input mechanism patient descriptive data indicating at least one characteristic of the patient;

establishing, via a processor, at least one patient-specific value for the patient based at least in part on the impedance data, wherein establishing at least one patient-specific value comprises processing the impedance data to establish a baseline impedance specific to the patient and a baseline breath parameter specific to the patient and computing a predicted impedance specific to the patient based on the patient descriptive data;

making ongoing measurements of the impedance via the adherent device and computing ongoing measurements of the breath parameter via the processor;

computing via the processor an impedance index indicative of a change in the impedance relative to the baseline impedance over time;

computing via the processor a breath index indicative of a change in the breath parameter relative to the baseline breath parameter over time;

computing via the processor a predicted impedance index indicative of a change in the impedance relative to the predicted impedance over time; and

generating based at least in part on comparisons of the impedance index, the breath index, and the predicted impedance index with respective preselected thresholds performed by the processor, a patient event prediction output predictive of the impending patient cardiac event.

2. The method of claim 1, wherein receiving patient descriptive data comprises receiving an indicator of an amount of fat in the patient'"'"'s body.

3. The method of claim 1, wherein receiving patient descriptive data comprises receiving a height and weight of the patient.

4. The method of claim 1, wherein receiving patient descriptive data comprises receiving a body mass index of the patient.

5. The method of claim 1, wherein the patient event prediction output is predictive of an impending acute decompensated heart failure event.

6. The method of claim 5, wherein the acute decompensated heart failure event is any event or combination of events selected from the group consisting of 1) any heart failure-related emergency room visit by the patient or hospitalization of the patient requiring administration of intravenous diuretics, inotrope, or ultrafiltration for fluid removal;

2) a change in diuretic directed by a health care provider, wherein the change is one or more of a) a change in the prescribed diuretic type, b) and increase in dose of an existing diuretic, or c) the addition of another diuretic; and

     3) an acute decompensated heart failure event for which the outcome is death of the patient.

7. The method of claim 1, wherein receiving impedance data measured from the patient comprises:

affixing an adherent device to the patient, the adherent device comprising electrodes in electrical contact with the patient'"'"'s skin, and the adherent device generating an impedance output indicating an impedance between at least two of the electrodes; and

receiving the impedance output.

8. The method of claim 1, wherein the baseline impedance is established using impedance data measured from the patient during a period of low patient activity.

9. The method of claim 8, wherein the baseline impedance is established using impedance data measured from the patient while the patient is at rest.

10. The method of claim 1, wherein the baseline breath parameter is established using impedance data measured from the patient during a period of low patient activity.

11. The method of claim 10, wherein the baseline breath parameter is established using impedance data measured from the patient while the patient is at rest.

12. The method of claim 1, wherein the breath parameter relates to the volume of air breathed by the patient.

13. The method of claim 1, wherein the predicted impedance is computed from the patient'"'"'s height and weight or from the patient'"'"'s body mass index.

14. The method of claim 1, wherein generating the patient event prediction output comprises generating the event prediction output based at least in part on a relationship of ongoing measurements of the impedance to the predicted impedance.

15. The method of claim 1, wherein computing the patient prediction output further comprises computing an amount of time for which at least one of the indices has exceeded its respective threshold.

16. The method of claim 1, further comprising generating the patient prediction output predicting a cardiac event when the impedance index and the predicted impedance index have both exceeded their respective thresholds for time period of preselected duration, and the breath index has exceeded its respective threshold at least once during the time period.

17. A system for predicting an impending cardiac event of a patient, the system comprising:

an adherent device attachable to a patient to electronically measure impedance data from the patient;

an input mechanism for receiving patient descriptive data indicating at least one descriptive characteristic of the patient; and

a processor and a tangible memory readable by the processor;

wherein the memory stores instructions that, when executed by the processor, cause the system to;

compute a baseline impedance specific to the patient and a baseline breath parameter specific to the patient based on the received impedance data;

compute a predicted impedance specific to the patient based on the patient descriptive data;

receive ongoing measurements of impedance data measured from the patient and in response to ongoing impedance data, compute an impedance index indicative of a change in the impedance relative to the baseline impedance, compute a breath index indicative of a change in the breath parameter relative to the baseline breath parameter over time, and compute a predicted impedance index indicative of a change in the impedance relative to the predicted impedance over time; and

generate a patient event prediction output predictive of the impending patient cardiac event based at least in part on comparisons of the impedance index, the breath index, and the predicted impedance index with respective preselected thresholds.

18. The system of claim 17, wherein the received patient descriptive data includes at least one of an indicator of an amount of fat in the patient'"'"'s body, a height and weight of the patient, and a body mass index of the patient.

19. The system of claim 17, wherein the adherent device includes at least two electrodes coupled to circuitry to measure an impedance of the patient.

20. The system of claim 17, wherein the baseline impedance and baseline breath parameter is established using impedance data measured from the patient during a period of low patient activity or rest.