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

ID#: NCT06699056

Age: 18 years - 66+

Gender: All

Healthy Subjects: Accepts Healthy Volunteers

Recruitment Status: Recruiting

Start Date: November 21, 2024

End Date: September 01, 2025

Contact Information:
Sandeep Gulati, PhD
8182162958
Chris Darland, MBA
814-572-7138
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
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