AI-ECG strategy to identify people who may need implanted heart devices
Evaluation of an Artificial Intelligence-Enhanced Electrocardiogram Strategy Versus Standard Care to Identify Patients Requiring Cardiac Implantable Electronic Devices: A Randomized Controlled Trial
This trial will test whether using an artificial intelligence-enhanced ECG to alert doctors helps find patients who need pacemakers or other implanted heart devices sooner than usual care.
Quick facts
| Phase | Not applicable |
|---|---|
| Study type | Interventional |
| Enrollment | 11492 (estimated) |
| Ages | 65 Years to 90 Years |
| Sex | All |
| Sponsor | National Defense Medical Center, Taiwan Academic / other |
| Locations | 1 site (Taipei) |
| Trial ID | NCT07217236 on ClinicalTrials.gov |
What this trial studies
This is a randomized controlled trial that uses a previously validated deep learning algorithm to analyze 12-lead ECGs and classify participants as high risk for needing a cardiac implantable electronic device (CIED). Participants labeled high-risk by the AI are randomized to an intervention arm, where physicians are alerted and the participant is offered up to 7 days of ambulatory continuous ECG monitoring, or to a control arm that receives usual clinical care with AI results withheld. Final device indication determinations are made by an independent panel of experienced cardiologists blinded to the AI reports. The trial tests whether an AI-driven notification and prompt monitoring pathway increases timely identification of patients meeting CIED indications compared with standard care.
Who should consider this trial
Good fit: Adults with at least one 12-lead ECG within the past year who do not already have a CIED and do not meet the exclusion diagnoses are the ideal candidates for this trial.
Not a fit: Patients who already have a CIED or who have known sick sinus syndrome, high-grade or complete AV block, ventricular tachycardia/fibrillation, or a heart rate below 40 bpm on ECG are excluded and are unlikely to benefit from this intervention.
Why it matters
Potential benefit: If successful, this approach could help doctors identify patients who need pacemakers or other implanted heart devices earlier, enabling timelier treatment and potentially reducing complications from untreated conduction disease.
How similar studies have performed: AI-ECG methods have shown promise in detecting arrhythmias and conduction abnormalities in other studies, but using AI-ECG to proactively trigger ambulatory monitoring for CIED indications is a relatively novel application.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * At least one 12-lead ECG within 1 year Exclusion Criteria: * Diagnosis of sick sinus syndrome * Diagnosis of high-grade or complete atrioventricular block * Diagnosis of ventricular tachycardia or ventricular fibrillation * Post CIED implant * Heart rate below 40 beats per minute by 12-lead ECG
Where this trial is running
Taipei
- Tri-Service General Hospital — Taipei, Taiwan (Recruiting)
Study contacts
- Study coordinator: Chin Lin
- Email: xup6fup0629@gmail.com
- Phone: +886-2-8792-3100
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.