Multicenter validation of a machine learning phase space electro-mechanical pulse wave analysis to predict elevated left ventricular end diastolic pressure at the point-of-care

Authors

Sanjeev P. Bhavnani, Division of Cardiovascular Medicine, Healthcare Innovation & Practice Transformation Laboratory, Scripps Clinic, San Diego, California, United States of America.
Rola Khedraki, Division of Cardiology, Section Advanced Heart Failure, Scripps Clinic, San Diego, California, United States of America.
Travis J. Cohoon, Division of Cardiovascular Medicine, Healthcare Innovation & Practice Transformation Laboratory, Scripps Clinic, San Diego, California, United States of America.
Frederick J. Meine, Novant Health New Hanover Regional Medical Center, Wilmington, North Carolina, United States of America.
Thomas D. Stuckey, Cone Health Heart and Vascular Center, Greensboro, North Carolina, United States of America.
Thomas McMinn, Austin Heart, Austin, Texas, United States of America.
Jeremiah P. Depta, Rochester Regional HealthFollow
Brett Bennett, Jackson Heart Clinic, Jackson, Mississippi, United States of America.
Thomas McGarry, Oklahoma Heart Hospital, Oklahoma City, Oklahoma, United States of America.
William Carroll, Cardiology Associates of North Mississippi, Tupelo, Mississippi, United States of America.
David Suh, Atlanta Heart Specialists, Atlanta, Georgia, United States of America.
John A. Steuter, Bryan Heart, Lincoln, Nebraska, United States of America.
Michael Roberts, Lexington Medical Center, West Columbia, South Carolina, United States of America.
Horace R. Gillins, CorVista Health, Inc., Washington, DC, United States of America.
Ian Shadforth, CorVista Health, Inc., Washington, DC, United States of America.
Emmanuel Lange, CorVista Health, Toronto, Ontario, Canada.
Abhinav Doomra, CorVista Health, Toronto, Ontario, Canada.
Mohammad Firouzi, CorVista Health, Toronto, Ontario, Canada.
Farhad Fathieh, CorVista Health, Toronto, Ontario, Canada.
Timothy Burton, CorVista Health, Toronto, Ontario, Canada.
Ali Khosousi, CorVista Health, Toronto, Ontario, Canada.
Shyam Ramchandani, CorVista Health, Toronto, Ontario, Canada.
William E. Sanders, CorVista Health, Inc., Washington, DC, United States of America.
Frank Smart, LSU Health Science Center, New Orleans, Louisiana, United States of America.

Department

Cardiology

Document Type

Article

Publication Title

PloS One

Abstract

BACKGROUND: Phase space is a mechanical systems approach and large-scale data representation of an object in 3-dimensional space. Whether such techniques can be applied to predict left ventricular pressures non-invasively and at the point-of-care is unknown.

OBJECTIVE: This study prospectively validated a phase space machine-learned approach based on a novel electro-mechanical pulse wave method of data collection through orthogonal voltage gradient (OVG) and photoplethysmography (PPG) for the prediction of elevated left ventricular end diastolic pressure (LVEDP).

METHODS: Consecutive outpatients across 15 US-based healthcare centers with symptoms suggestive of coronary artery disease were enrolled at the time of elective cardiac catheterization and underwent OVG and PPG data acquisition immediately prior to angiography with signals paired with LVEDP (IDENTIFY; NCT #03864081). The primary objective was to validate a ML algorithm for prediction of elevated LVEDP using a definition of ≥ 25 mmHg (study cohort) and normal LVEDP ≤ 12 mmHg (control cohort), using AUC as the measure of diagnostic accuracy. Secondary objectives included performance of the ML predictor in a propensity matched cohort (age and gender) and performance for an elevated LVEDP across a spectrum of comparative LVEDP (increments). Features were extracted from the OVG and PPG datasets and were analyzed using machine-learning approaches.

RESULTS: The study cohort consisted of 684 subjects stratified into three LVEDP categories, ≤ 12 mmHg (N = 258), LVEDP 13-24 mmHg (N = 347), and LVEDP ≥ 25 mmHg (N = 79). Testing of the ML predictor demonstrated an AUC of 0.81 (95% CI 0.76-0.86) for the prediction of an elevated LVEDP with a sensitivity of 82% and specificity of 68%, respectively. Among a propensity matched cohort (N = 79) the ML predictor demonstrated a similar result AUC 0.79 (95% CI: 0.72-0.8). Using a constant definition of elevated LVEDP and varying the lower threshold across LVEDP the ML predictor demonstrated and AUC ranging from 0.79-0.82.

CONCLUSION: The phase space ML analysis provides a robust prediction for an elevated LVEDP at the point-of-care. These data suggest a potential role for an OVG and PPG derived electro-mechanical pulse wave strategy to determine if LVEDP is elevated in patients with symptoms suggestive of cardiac disease.

First Page

e0277300

DOI

10.1371/journal.pone.0277300

Volume

17

Issue

11

Publication Date

11-15-2022

PubMed ID

36378672

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