Pattern recognition system for quantifying the likelihood of the contribution of multiple possible forms of chronic disease to patient reported dyspnea

• US 10,010,264 B2
• Filed: 07/25/2014
• Issued: 07/03/2018
• Est. Priority Date: 07/25/2013
• Status: Active Grant

• Current Assignee: Shape Medical Systems, Inc.
• Original Assignee: Shape Medical Systems, Inc.
• Inventors: Anderson, Stephen T., MacCarter, Dean J., Anderson, David M., Hofmeister, Andrew
• Primary Examiner(s): MALLARI, PATRICIA C
• Assistant Examiner(s): Ramachandran, Vasuda
• Application Number:
  US14/341,442
• Publication Number:
  US 20150032018A1
• Time in Patent Office: 1,439 Days
• 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 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/0826 Detecting or evaluating apnoea events A61B5/4818 takes precedence
• CPC Class Codes: A61B 5/0833 Measuring rate of oxygen consumption
• CPC Class Codes: A61B 5/0836 Measuring rate of CO2 production
• CPC Class Codes: A61B 5/087 Measuring breath flow
• CPC Class Codes: A61B 5/091 Measuring volume of inspired or expired gases, e.g. to determine lung capacity
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/7475 User input or interface means, e.g. keyboard, pointing device, joystick

Abstract

Systems and methods for quantifying the likelihood of the contribution of multiple possible forms of chronic disease to patient reported dyspnea can include the testing protocol having a flow/volume loop, performed at rest, flowed by the measurement of cardiopulmonary exercise gas exchange variables during rest, exercise and recovery as unique data sets. The data sets are analyzed using feature extraction steps to produce a pictorial image consisting of disease silos displaying the likelihood of the contribution of various chronic diseases to patient reported dyspnea. In some embodiments, the silos are split into subclass silos. In some embodiments, multiple chronic disease indexes are used to differentiate between sub-types of a particular chronic disease (e.g., differentiating WHO 1 PH from WHO 2 or WHO 3 PH). Test results are plotted serially to asses to provide feedback to the physician on the efficacy of therapy provided to the patient.

Claims

1. A system for characterizing contributions of physiological conditions to dyspnea in a patient, the system comprising:

a flow sensor configured to sense a respiratory flow of the patient;

an analyzer configured to determine a composition of at least a portion of the respiratory flow of the patient;

a computing device configured to;

receive heart rate data and gas exchange measurements, wherein the gas exchange measurements are based on breath-by-breath data captured by the flow sensor and the analyzer during a gas exchange test that includes at least a rest phase and an exercise phase;

repeatedly evaluate one or more of a respiratory exchange rate based on the received gas exchange measurements and a heart rate based on the received heart rate data on a breath-by-breath basis to determine when a predetermined target metric corresponding to exertion level is reached;

determine a plurality of contribution values that are each associated with a different physiological condition, wherein the plurality of contribution values are numeric values based on the gas exchange measurements captured on the breath-by-breath basis during the rest phase and the exercise phase and each of the plurality of contribution values corresponds to a likelihood that the associated physiological condition contributes to dyspnea in the patient; and

output the plurality of contribution values.

2. The system of claim 1, wherein the computing device is further configured to receive spirometric measurements including forced vital capacity (FVC), forced expiratory volume in one second (FEV1), and inspiratory capacity (IC).

3. The system of claim 2, wherein the spirometric measurements are received from the flow sensor.

4. The system of claim 2, further comprising a user input device, wherein the spirometric measurements are entered using the user input device.

5. The system of claim 2, wherein a first contribution value of the plurality of contribution values is based on the spirometric measurements and the gas exchange measurements.

6. The system of claim 1, wherein the analyzer is configured to determine a concentration of one or more components of the respiratory flow, wherein the components are selected from a group of components comprising:

oxygen; and

carbon dioxide.

7. The system of claim 6, wherein the computing device is further configured to calculate at least one physiological parameter based on the gas exchange measurements, wherein the at least one physiological parameter is selected from a group of physiological parameters comprising:

respiratory rate (RR);

ventilation (VE);

rate of oxygen uptake or consumption (VO₂);

carbon dioxide production (VCO₂);

ventilatory efficiency (ratio, VE/VCO₂);

ventilatory efficiency (slope, VE/VCO₂);

end tidal CO₂;

end tidal CO2/respiratory rate (RR);

oxygen uptake efficiency slope (ODES);

V/Q ratio; and

pulmonary capacitance.

8. The system of claim 1, wherein the physiological conditions associated with the plurality of contribution values are selected from a group of physiological conditions consisting of heart failure, pulmonary arterial hypertension, pulmonary venous hypertension, obstructed lung, restricted lung, obesity, general deconditioning, and acute decompensated heart failure.

9. The system of claim 1, wherein the computing device is further configured to transmit data based on the gas exchange measurements to a server computing device over a network.

10. The system of claim 1, wherein the computing device is a mobile computing device.

11. The system of claim 1, wherein the computing device includes a display device and is further configured to generate a user interface that includes a visual indicator, wherein a property of the visual indicator is determined based on a first contribution value of the plurality of contribution values.

12. The system of claim 11, wherein the user interface includes a second visual indicator, wherein a property of the second visual indicator is based on a second contribution value of the plurality of contribution values.

13. The system of claim 12, wherein the properties of the first and second visual indicators are selected from a group of properties comprising:

size;

height; and

color.

14. The system of claim 1, further comprising a computer-readable data storage device and wherein the computing device is configured to display at least one historical visual indicator, wherein a property of the at least one historical visual indicator is determined based on a historical contribution value from a historical gas exchange test of the patient, data related to the historical gas exchange test being stored in the computer-readable data storage device.

15. The system of claim 1, wherein the gas exchange test further includes a recovery period after the patient performs exercise during the exercise phase.

16. The system of claim 2, wherein the computing device is further configured to determine an FEV1/FVC ratio and to determine a contribution value associated with obstructed or restrictive lung disease based on the FEV1/FVC ratio.