AI-Enabled Ejection Fraction Assessment Using COR ECG Wearable Monitor
This study is looking at a new way to measure how well your heart pumps blood, called ejection fraction (EF). It uses an artificial intelligence (AI) software with the Peerbridge COR™ ECG Wearable Monitor, a device you wear. The goal is to see if this AI software can accurately tell the severity of your heart's pumping ability, similar to how an ultrasound (echocardiography) does. This could offer an easier, more accessible way to check heart function. We are looking for 2000 participants aged 18 or older who are able to wear a Holter monitor. The study will measure the agreement between the AI software's results and ultrasound results over about 9 months.
- Study design
- This is an observational study involving 2000 participants. It aims to compare the AI software's results with standard ultrasound measurements.
- What's involved
- You would wear the Peerbridge COR™ device and sit for 15 minutes during the ECG acquisition. The study will last for an average of 9 months.
- Compensation
- Not stated in the trial record.
- Follow-up
- The primary measurement will be taken through study completion, which is an average of 9 months.
AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.
AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Assessment Using COR ECG Wearable Monitor
At a glance
Conditions
NCT06699056
Where you'd take part
This study runs at 8 sites. They're the same protocol — you choose where, and that choice sets who your contact draft is addressed to.
Hackensack University Medical Center
Hackensack, New Jerseystudy coordinator listed
Recruiting
Henry Ford Hospital
Detroit, Michiganstudy coordinator listed
Recruiting
Moses H. Cone Memorial Hospital
Greensboro, North Carolinastudy coordinator listed
Recruiting
Mount Sinai Hospital
New York, New Yorkstudy coordinator listed
Recruiting
Orange County Heart Institute
Orange, Californiastudy coordinator listed
Recruiting
Peerbridge Health
Pasadena, Californiastudy coordinator listed
Recruiting
South Heart Clinic
Weslaco, Texasstudy coordinator listed
Recruiting
Texas Cardiac Arrhythmia Research Foundation
Austin, Texasstudy coordinator listed
Recruiting
Sites open and close at different times, so the status above is per site — it can differ from the study's overall status.
Study leadership
- Andrea Natale, MD · PRINCIPAL_INVESTIGATOR · Texas Cardiac Arrhythmia Research Foundation
- Johanna P Contreras, MD · PRINCIPAL_INVESTIGATOR · MOUNT SINAI HOSPITAL
- Sachin Parikh, MD · PRINCIPAL_INVESTIGATOR · Henry Ford Hospital
- Brian Kolski, MD · PRINCIPAL_INVESTIGATOR · Orange County Heart Institute
- Daniel Bensimhon, MD · PRINCIPAL_INVESTIGATOR · Moses H. Cone Memorial Hospital
- Sandeep Gulati, PhD · PRINCIPAL_INVESTIGATOR · Peerbridge Health, Inc
- Frank Mazzola, MD · PRINCIPAL_INVESTIGATOR · South Heart Clinic
- Sameer Jamal, MD · PRINCIPAL_INVESTIGATOR · Hackensack Meridian Health
- Andrea Natale, MD · PRINCIPAL_INVESTIGATOR · HCA Los Robles Hospital & Medical Center
Who to contact
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Inclusion
Exclusion
What this trial measures
- Agreement of CorEFS Software EF Severity Categories Using Peerbridge COR™ ECG Data with ASE EF Severity Categories Established by Ultrasound EchocardiographyThrough study completion, average of 9 months.
The primary endpoint of this trial is to demonstrate substantial agreement between EF severity categories determined by the CorEFS Software using 5 minutes of Peerbridge COR™ ECG data and the subject's EF severity category established through ultrasound echocardiography, the gold standard for EF classification. The study includes four co-primary endpoints, representing agreement measures within each of the four EF severity categories defined by the American Society of Echocardiography (ASE) Scale (Normal, Mildly Abnormal, Moderately Abnormal, Severely Abnormal). For each category the endpoint is the proportion of participants correctly classified by the test device relative to the reference standard. The goal is to demonstrate at least 80% agreement within each EF severity category.