This prospective, multicenter, cluster-randomized controlled study aims to evaluate the accuracy of an investigational artificial intelligence (AI) Software as a Medical Device (SaMD) designed to compute ejection fraction (EF) severity categories based on the American Society of Echocardiography's (ASE) 4-category scale. The software analyzes continuous ECG waveform data acquired by the FDA-cleared Peerbridge COR® ECG Wearable Monitor, an ambulatory patch device designed for use during daily activities. The AI software assists clinicians in cardiac evaluations by estimating EF severity, which reflects how well the heart pumps blood. In this study, EF severity determination will be made using 5-minute ECG recordings collected during a 15-minute resting period with participants seated upright. The results will be compared to EF severity obtained from an FDA-cleared, non-contrast transthoracic echocardiogram (TTE) predicate device. This comparison aims to validate the accuracy of the AI software.
Ventricular Ejection Fraction, LVF, LV Dysfunction, Atrial Enlargement, Conduction Defect, Heart Failure, Valvular Heart Disease, Ischemic Heart Disease, Cardiotoxicity, Myocardial Infarction, Dilated Cardiomyopathy, HFrEF - Heart Failure with Reduced Ejection Fraction, HFpEF - Heart Failure with Preserved Ejection Fraction, Syncope, Remodeling, Cardiac
This prospective, multicenter, cluster-randomized controlled study aims to evaluate the accuracy of an investigational artificial intelligence (AI) Software as a Medical Device (SaMD) designed to compute ejection fraction (EF) severity categories based on the American Society of Echocardiography's (ASE) 4-category scale. The software analyzes continuous ECG waveform data acquired by the FDA-cleared Peerbridge COR® ECG Wearable Monitor, an ambulatory patch device designed for use during daily activities. The AI software assists clinicians in cardiac evaluations by estimating EF severity, which reflects how well the heart pumps blood. In this study, EF severity determination will be made using 5-minute ECG recordings collected during a 15-minute resting period with participants seated upright. The results will be compared to EF severity obtained from an FDA-cleared, non-contrast transthoracic echocardiogram (TTE) predicate device. This comparison aims to validate the accuracy of the AI software.
AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Assessment Using COR ECG Wearable Monitor
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Orange County Heart Institute, Orange, California, United States, 92868
Peerbridge Health, Melbourne, Florida, United States, 32935
Henry Ford Hospital, Detroit, Michigan, United States, 48202
Mount Sinai Hospital, New York, New York, United States, 10019
Moses H. Cone Memorial Hospital, Greensboro, North Carolina, United States, 27401
South Heart Clinic, Weslaco, Texas, United States, 78596
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18 Years to
ALL
Yes
Peerbridge Health, Inc,
Andrea Natale, MD, PRINCIPAL_INVESTIGATOR, Texas Cardiac Arrhythmia Research Foundation
Johanna P Contreras, MD, PRINCIPAL_INVESTIGATOR, MOUNT SINAI HOSPITAL
Sachin Parikh, MD, PRINCIPAL_INVESTIGATOR, Henry Ford Hospital
Brian Kolski, MD, PRINCIPAL_INVESTIGATOR, Orange County Heart Institute
Daniel Bensimhon, MD, PRINCIPAL_INVESTIGATOR, Moses H. Cone Memorial Hospital
Sandeep Gulati, PhD, PRINCIPAL_INVESTIGATOR, Peerbridge Health, Inc
Frank Mazzola, MD, PRINCIPAL_INVESTIGATOR, South Heart Clinic
2025-09