AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Assessment Using COR ECG Wearable Monitor
NCT06699056
Summary
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.
Eligibility
Inclusion Criteria: * Age ≥ 18 years * Able and eligible to wear a Holter monitor Exclusion Criteria: * Receiving mechanical respiratory or circulatory support, or renal support therapy, at the time of screening or during Visit #1 * Any condition that, in the investigator's opinion, could interfere with compliance with the study protocol or pose a safety risk to the participant * History of poor tolerance or severe skin reactions to ECG adhesive materials
Conditions16
Locations8 sites
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NCT06699056