AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Using COR ECG Wearable Monitor
Peerbridge Health, Inc
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.
Description
Objective This prospective study benchmarks the accuracy of CorEFS AI software in estimating ejection fraction (EF) severity categories using continuous ECG waveforms from the FDA-cleared Peerbridge Cor® ECG device, calibrated to the American Society of Echocardiography (ASE) scale. Background Heart failure (HF) remains a significant public health issue, particularly in older adults (75+), with high morbidity and mortality rates. Half of HF cases involve reduced EF (HFrEF), a condition associated with a 75% five-year mortality rate. Despite advancements in HF management, accessible, low-cost…