Method and apparatus for personalized physiologic parameters

• US 8,965,498 B2
• Filed: 03/28/2011
• Issued: 02/24/2015
• Est. Priority Date: 04/05/2010
• Status: Active Grant

• Current Assignee: Medtronic Monitoring, Inc. (Medtronic Plc)
• Original Assignee: Corventis
• Inventors: Katra, Rodolphe, Chakravarthy, Niranjan, Libbus, Imad
• Primary Examiner(s): TOWA, RENE T
• Application Number:
  US13/073,745
• Publication Number:
  US 20110245711A1
• Time in Patent Office: 1,429 Days
• Field of Search: 600/382, 600/481, 600/506, 600/529, 600/547
• US Class Current: 600/547
• 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 system for predicting a 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;

receive the impedance data;

establish at least one personalized value for the patient based on the impedance data;

receive patient descriptive data indicating at least one descriptive characteristic of the patient;

establish a predicted impedance specific to the patient, based at least in part on the patient descriptive data; and

generate, based at least in part on the impedance data, the predicted impedance, and the at least one personalized value, a patient event prediction output predictive of a patient cardiac event.

2. The system of claim 1, wherein the adherent device further comprises:

at least two electrodes in contact with the patient'"'"'s skin; and

an electronics module that measures the impedance between at least two of the electrodes and transmits the impedance data representing the impedance to the processor.

3. The system of claim 2, wherein the adherent device comprises the processor.

4. The system of claim 1, further comprising:

a computer system that comprises the processor; and

a communication link over which the impedance data is transmitted from the adherent device to the computer system.

5. The system of claim 4, further comprising an intermediate device that receives the impedance data from the adherent device and relays the impedance data to the computer system.

6. The system of claim 4, wherein the adherent device transmits the impedance data to the computer system at least in part via a wireless link.

7. The system of claim 1, wherein the patient event prediction output is predictive of an acute decompensated heart failure event.

8. The system of claim 1, wherein the instructions, when executed by the processor, further cause the system to:

establish from the impedance data a baseline impedance specific to the patient; and

generate the patient event prediction output based at least on part on the relationship of ongoing impedance measurements to the baseline impedance.

9. The system of claim 1, wherein the instructions, when executed by the processor, further cause the system to:

establish from the impedance data a baseline breath parameter specific to the patient; and

generate the patient event prediction output based at least on part on the relationship of ongoing measurements of the breath parameter to the baseline breath parameter.

10. The system of claim 1, wherein the instructions, when executed by the processor, further cause the system to:

generate the patient event prediction output based at least on part on the relationship of ongoing measurements of the impedance to the patient'"'"'s predicted impedance.

11. The system of claim 1, wherein the instructions, when executed by the processor, further cause the system to:

establish from the impedance data a baseline impedance specific to the patient;

establish from the impedance data a baseline breath parameter specific to the patient; and

generate the patient event prediction output based at least on part on the relationship of ongoing measurements of the impedance to the baseline impedance, and the relationship of ongoing measurements of the breath parameter to the baseline breath parameter.

12. The system of claim 11, wherein the instructions, when executed by the processor, further cause the system to:

generate the patient event prediction output based at least in part on the relationship of ongoing measurements of the impedance to the patient'"'"'s predicted impedance.

13. The system of claim 12, wherein the instructions, when executed by the processor, further cause the system to:

compute an impedance index indicative of a change in the impedance relative to the baseline impedance over time;

compute a breath index indicative of a change in the breath parameter relative to the baseline breath parameter over time;

compute a predicted impedance index indicative of a change in the impedance relative to the predicted impedance over time; and

compare the impedance index, the breath index, and the predicted impedance index with respective preselected thresholds.

14. The system of claim 13, wherein the instructions, when executed by the processor, further cause the system to:

compute an amount of time for which at least one of the indices has exceeded its respective threshold.

15. The method of claim 14, wherein the instructions, when executed by the processor, further cause the system to generate 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 periods of preselected duration, and the breath index has exceeded its respective threshold at least once during the time period.

16. The system of claim 1, wherein the patient descriptive data indicate a body composition of the patient, and wherein the instructions, when executed by the processor, cause the system to establish the predicted impedance based at least in part on the indicated body composition of the patient.

17. The system of claim 1, wherein the patient descriptive data comprises a height and weight of the patient, and wherein the instructions, when executed by the processor, cause the system to establish the predicted impedance based at least in part on the height and weight of the patient.

18. The system of claim 1, wherein the instructions, when executed by the processor, cause the system to establish the predicted impedance based at least in part on a body mass index of the patient.

19. A system for predicting a 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;

receive the impedance data;

receive patient descriptive data indicating at least one descriptive characteristic of the patient;

establish a predicted impedance specific to the patient, based at least in part on the patient descriptive data;

establish from the impedance data a baseline impedance specific to the patient;

compute an impedance index indicative of a change in the impedance relative to the baseline impedance over time;

compare the impedance index with a preselected impedance index threshold;

compute an amount of time for which the impedance index has exceeded the impedance index threshold; and

generate, based at least in part on the impedance index and the predicted impedance, a patient event prediction output predictive of a patient cardiac event, only if the impedance index has exceeded the impedance index threshold for at least a preselected time.