AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Assessment Using COR ECG Wearable Monitor

Recruiting now

Conditions studied: 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

In brief

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.

Key facts

Study ID
NCT06699056
Run by
Peerbridge Health, Inc
People needed
2000
Starts
2024-11-21
Expected to finish
2027-11-15
Last updated by the study team
2026-06-01

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: accepted.

You may qualify if…

You may not qualify if…

Where it is running

Full record on ClinicalTrials.gov

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