AI Software Estimates Heart Pumping Strength From a Wearable ECG Patch
Tests AI software estimating heart pumping strength from ECG patch recordings in adults.
Plain-language title generated from the registry record
Registry title: AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Assessment Using COR ECG Wearable Monitor
Key facts
- Where
- Austin, Detroit, Greensboro, Hackensack, New York, Orange, Pasadena, and Weslaco, United StatesSee the 8 sites
Where in United States
- Texas Cardiac Arrhythmia Research FoundationAustin, United StatesTaking people
- Henry Ford HospitalDetroit, United StatesTaking people
- Moses H Cone Memorial HospitalGreensboro, United StatesTaking people
- Hackensack University Medical CenterHackensack, United StatesTaking people
- Mount Sinai HospitalNew York, United StatesTaking people
- Orange County Heart InstituteOrange, United StatesTaking people
- Peerbridge HealthPasadena, United StatesTaking people
- South Heart ClinicWeslaco, United StatesTaking people
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Peerbridge Health, Inc
- Trial code
- NCT06699056
- Record updated
- 1 June 2026
- Original description
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.Read full original descriptionShow less
AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Using COR ECG Wearable Monitor
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.
Inclusion: Age ≥ 18 years; Able and eligible to wear a Holter monitor.
Exclusion: 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.