IP Library › Granted Patent US 12,194,237
Granted Patent B2
US 12,194,237 · App. 17/201,684 · Granted Jan 14, 2025

Clinical decision support system for patient-ventilator asynchrony detection and management

Inventors: Behnood Gholami (Hoboken, NJ); Timothy S. Phan (Brooklyn, NY)
Assignee: Autonomous Healthcare, Inc.
A61M16/024A61M16/026G16H20/40G16H40/63G16H50/20A61M2016/0027A61M2016/0033A61M16/0057A61M2205/3379A61M2205/3561A61M2205/3584A61M2205/502A61M2205/52
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Quick Facts
Patent No.
US 12,194,237
App. No.
17/201,684
Granted
Jan 14, 2025
Kind
B2
Abstract

The present disclosure describes a system that automatically detects patient-ventilator asynchrony and trends in patient-ventilator asynchrony. The present disclosure describes a framework that uses pressure, flow, and volume waveforms to detect patient-ventilator asynchrony and the presence of secretions in the ventilator circuit.

Claims (44)

1. A method for indicating patient-ventilator interaction, comprising:

a) receiving ventilator pressure, flow, and/or volume waveform data relating to a ventilator;

b) extracting a first feature set from the ventilator pressure, flow, and/or volume waveform data;

c) determining whether at least one feature is absent from the first feature set, which absence by way of a first classifier and/or labeling algorithm indicates an irregular breath;

d) generating a patient-ventilator interaction indicator waveform comprising a Delta waveform from said ventilator pressure and flow waveform data, wherein the Delta waveform represents a difference between normalized pressure, after correcting for positive end expiratory pressure (PEEP), and normalized flow waveforms;

e) extracting a second feature set from the Delta waveform and/or the ventilator pressure, flow, and/or volume waveform data;

f) determining at least one class and/or label by a second classifier and/or second labeling algorithm based at least on the second feature set; and

g) determining a presence or absence of one or more types of asynchronous patient-ventilator interactions based at least on the at least one class and/or label.

2. The method of claim 1 , wherein the ventilator pressure, flow, and/or volume waveform data are obtained directly or indirectly from the ventilator, directly or indirectly from another monitor or system for acquiring ventilator pressure, flow and/or volume waveform data, or from an archive of in vivo, ex vivo, in vitro and/or in silico data.

3. The method of claim 1 , further comprising:

providing a prompt for managing one or more of the types of asynchronous patient-ventilator interactions, if present.

4. The method of claim 1 , wherein the first classifier and/or labeling algorithm is a rule-based algorithm and/or machine-learning algorithm configured to determine the absence of the at least one feature from the first feature set.

5. The method of claim 4 , wherein the first classifier and/or labeling algorithm is configured to compare the_at least one feature in the first feature set with a predetermined threshold and to determine the absence of the at least one feature by being below the predetermined threshold.

6. The method of claim 1 , wherein the second classifier and/or labeling algorithm is a rule-based algorithm and/or machine-learning algorithm configured to classify the Delta waveform and/or the ventilator pressure, flow, and/or volume waveform data into one or more categories of asynchronous patient-ventilator interactions.

7. The method of claim 6 , wherein the second classifier and/or second labeling algorithm classifies the Delta waveform and/or the ventilator pressure, flow, and/or volume waveform data into one or more categories of asynchronous patient-ventilator interactions by comparing at least one feature in the second feature set with a predetermined threshold.

8. The method of claim 1 , wherein the types of asynchronous patient-ventilator interactions are chosen from one or more of inadequate ventilator support, double triggering, ineffective triggering, premature termination, delayed termination, flow starvation, air trapping, buildup of fluid in lungs and/or a ventilator circuit, and/or no asynchrony.

9. The method of claim 1 , wherein the extracting of the first feature set comprises computing a measure of similarity between the ventilator pressure, flow, and/or volume waveform data and a reference waveform or a series of reference waveforms.

10. The method of claim 1 , wherein the extracting of the second feature set comprises extracting one or more of the following features:

depth of valleys of the Delta waveform;

maximum value of the Delta waveform within inspiration phase;

area under the curve of the Delta waveform within inspiration phase;

maximum cross-correlation of the Delta waveform with a delivered tidal volume waveform;

area under the portion of the Delta waveform occurring within approximately the first third of the Delta waveform duration;

maximum value of the Delta waveform occurring within approximately the first third of the Delta waveform duration; and

locations of valleys of the Delta waveform.

11. A system for detecting patient-ventilator interaction comprising:

a detection module comprising computer-executable instructions stored on a non-transitory computer-readable storage medium in operable communication with one or more computer processors, the detection module configured for acquiring mechanical ventilator airway pressure, flow, and/or volume waveform data; and

a patient-ventilator interaction indicator module in operable communication with the detection module, the patient-ventilator interaction indicator module comprising computer-executable instructions stored in a non-transitory computer-readable storage medium in operable communication with the one or more computer processors for:

a) extracting a first feature set from a ventilator pressure, flow, and/or volume waveform data;

b) determining whether at least one feature is absent from the first feature set, which absence by way of a first classifier and/or labeling algorithm indicates an irregular breath;

c) generating a patient-ventilator interaction indicator waveform comprising a Delta waveform from said ventilator pressure and flow waveform data, wherein the Delta waveform represents a difference between normalized pressure, after correcting for positive end expiratory pressure (PEEP), and normalized flow waveforms;

d) extracting a second feature set from the Delta waveform and/or the ventilator pressure, flow, and/or volume waveform data;

e) determining at least one class and/or label by a second classifier and/or second labeling algorithm based at least on the second feature set; and

f) determining the presence or absence of one or more types of asynchronous patient-ventilator interactions based at least on the at least one class and/or label.

12. The system of claim 11 , wherein the detection module is configured for acquiring a mechanical ventilator airway pressure, flow, and/or volume waveform data directly or indirectly from the mechanical ventilator, directly or indirectly from another monitor or system for producing the mechanical ventilator airway pressure, flow, and/or volume waveform data, or directly or indirectly from an archive of in vivo, ex vivo, in vitro and/or in silico data.

13. The system of claim 11 , wherein one or more of the computer processors is embedded in a mechanical ventilator, or another monitor or system or a networked computer system.

14. The system of claim 11 , wherein the patient-ventilator interaction module further generates an asynchrony index based on the types of asynchronous patient-ventilator interactions present or the absence thereof.

15. The system of claim 11 , further including a graphical user interface comprising:

at least one window for displaying detection of one or more asynchronous patient-ventilator interactions and/or irregular breaths;

one or more elements within the at least one window for communicating the detected asynchronous patient-ventilator interactions and/or the irregular breath, recommendations for mitigating the detected asynchronous patient-ventilator interactions, educational information on the detected asynchronous patient-ventilator interactions, and/or communicating an asynchrony index.

16. The system of claim 11 , further including a transmission module to transmit the determined and indicated types of asynchronous patient-ventilator interactions or absence thereof or the presence of the irregular breath to a remote server and/or smartphone and/or tablet.

17. The system of claim 16 , wherein the patient-ventilator interaction indicator module further generates an asynchrony index based on the types of asynchronous patient-ventilator interactions present or the absence thereof and the transmission module transmits a notification to a user when the asynchrony index exceeds a pre-determined threshold.

18. The system of claim 11 , wherein the first classifier and/or first labeling algorithm is a rule-based algorithm and/or machine-learning algorithm configured to determine the absence of the at least one feature from the first feature set and/or configured to compare the at least one feature in the first feature set with a predetermined threshold and to determine the absence of the at least one feature by being below the predetermined threshold.

19. The system of claim 11 , wherein the second classifier and/or second labeling algorithm is a rule-based algorithm and/or machine-learning algorithm configured to classify the Delta waveform and/or the ventilator pressure, flow, and/or volume waveform data into one or more categories of asynchronous patient-ventilator interactions.

Assignments (2)
CONFIRMATORY LICENSE Recorded Apr 2, 2025
From: AUTONOMOUS HEALTHCARE, INC.
To: NATIONAL SCIENCE FOUNDATION
Reel/Frame 070705/0592 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 8, 2024
From: GHOLAMI, BEHNOOD; PHAN, TIMOTHY S.
To: AUTONOMOUS HEALTHCARE, INC.
Reel/Frame 068834/0619 →
Continuity (4)
Continuation 17014778 · Sep 8, 2020
Continuation 16762224
Provisional Application 62583558 · Nov 9, 2017
Related Publication 20210220587A1 · Jul 22, 2021
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