Screening to prevent heart failure (STOP-HF): expanding the focus beyond asymptomatic left ventricular systolic dysfunction.
Gillian Murtagh, Ian R Dawkins, Ronan O'Connell...
https://pubmed.ncbi.nlm.nih.gov/22416086Actively Recruiting
Age: 18Years +
All Genders
Healthy Volunteers
ID06699056
AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Using COR ECG Wearable Monitor
Led by Peerbridge Health, Inc · Updated on 2026-06-01
2000
Participants Needed
8
Research Sites
N/A
Total Duration
On this page
AI-Summary
What this Trial Is About
Researchers are evaluating an investigational artificial intelligence (AI) software designed to estimate the severity of ejection fraction (EF), which indicates how well the heart pumps blood. This prospective, multicenter, cluster-randomized controlled study compares EF severity categories determined by the AI software using continuous ECG waveform data to those measured by an FDA-cleared transthoracic echocardiogram (TTE). The study aims to provide a low-burden, cost-effective alternative for EF monitoring in heart failure and related heart conditions, especially where traditional imaging access is limited. Participants will use the FDA-cleared Peerbridge COR4 ECG Wearable Monitor, a patch device worn during daily activities, to collect ECG data. During a 15-minute resting session while seated upright, 5-minute ECG segments will be recorded and analyzed by the AI software to estimate EF severity based on the American Society of Echocardiography's scale. The study includes two subprotocols: one with 30 minutes of ECG recording including 15 minutes analyzed, and another allowing up to 7 days of device use with periodic sitting sessions. The EF severity from the AI software will be compared against results from echocardiography. Participants will be enrolled at multiple sites, providing paired data points consisting of simultaneous or near-simultaneous ECG recordings and echocardiograms. They will follow a standardized 15-minute seated session protocol using the wearable device, pressing an event button to mark the session start and end. Data collection includes medical histories, 12-lead ECGs, and device logs. The main outcome measures focus on agreement between the AI software's EF severity categories and those from echocardiography over an average of 9 months. Safety and compliance will be monitored throughout the study period.
CONDITIONS
Brief Title
AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Assessment Using COR ECG Wearable Monitor
Who Can Participate
Age: 18Years +
All Genders
Healthy Volunteers
Eligibility Criteria
You may qualify if you...
- Age 18 years or older
- Able and eligible to wear a Holter monitor
You will not qualify if you...
- Receiving mechanical respiratory, circulatory, or renal support therapy at screening or Visit #1
- Any condition that could interfere with study compliance or pose safety risks, as judged by the investigator
- History of poor tolerance or severe skin reactions to ECG adhesive materials
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
1
2
3
Your Study Journey
Screening
Duration - 2 to 4 weeks
Participants are screened for eligibility to participate in the trial.
1 visit (in-person or telehealth)
Monitoring
Duration - Up to 7 days
Participants wear the Peerbridge Cor® ECG device to collect continuous ECG data used for ejection fraction severity assessment.
1 in-clinic setup visit or telehealth setup; ECG recording sessions up to 7 days
Diagnostic Evaluation
Duration - Same day as ECG recording or within 3 hours
Participants undergo transthoracic echocardiography (Echo) to provide a reference standard for ejection fraction severity.
1 visit (in-person) for Echo and simultaneous 12-lead ECG
Trial Site Locations
Total: 8 locations
1
Orange County Heart Institute
Orange, California, United States, 92868
Actively Recruiting
2
Peerbridge Health
Pasadena, California, United States, 91107
Actively Recruiting
3
Henry Ford Hospital
Detroit, Michigan, United States, 48202
Actively Recruiting
4
Hackensack University Medical Center
Hackensack, New Jersey, United States, 07601
Actively Recruiting
5
Mount Sinai Hospital
New York, New York, United States, 10019
Actively Recruiting
6
Moses H. Cone Memorial Hospital
Greensboro, North Carolina, United States, 27401
Actively Recruiting
7
Texas Cardiac Arrhythmia Research Foundation
Austin, Texas, United States, 78705
Actively Recruiting
8
South Heart Clinic
Weslaco, Texas, United States, 78596
Actively Recruiting
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Research Team
S
Sandeep Gulati, PhD
C
Chris Darland, MBA
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
1
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Published Research Related To This Trial
Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging.
Roberto M Lang, Luigi P Badano, Victor Mor-Avi...
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Mohammad Alhamaydeh, Richard Gregg, Abdullah Ahmad...
https://pubmed.ncbi.nlm.nih.gov/32554161Electrocardiographic Predictors of Heart Failure With Reduced Versus Preserved Ejection Fraction: The Multi-Ethnic Study of Atherosclerosis.
Wesley T O'Neal, Matylda Mazur, Alain G Bertoni...
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Hung-Yi Chen, Chin-Sheng Lin, Wen-Hui Fang...
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Demilade Adedinsewo, Rickey E Carter, Zachi Attia...
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Xiaoxi Yao, David R Rushlow, Jonathan W Inselman...
https://pubmed.ncbi.nlm.nih.gov/33958795Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images.
Veer Sangha, Arash A Nargesi, Lovedeep S Dhingra...
https://pubmed.ncbi.nlm.nih.gov/37489538Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: a prospective, observational, multicentre study.
Patrik Bachtiger, Camille F Petri, Francesca E Scott...
https://pubmed.ncbi.nlm.nih.gov/34998740Prediction of heart failure patients with distinct left ventricular ejection fraction levels using circadian ECG features and machine learning.
Sona M Al Younis, Leontios J Hadjileontiadis, Ahsan H Khandoker...
https://pubmed.ncbi.nlm.nih.gov/38739639