AI-ECG strategy to identify people who may need implanted heart devices
Official title: Evaluation of an Artificial Intelligence-Enhanced Electrocardiogram Strategy Versus Standard Care to Identify Patients Requiring Cardiac Implantable Electronic Devices: A Randomized Controlled Trial
Run by National Defense Medical Center, Taiwan · NCT07217236 · Data updated 2026-11-12
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
| Study type | Interventional |
|---|---|
| Ages | 65 Years to 90 Years |
| Sex | All |
| Enrollment | 11492 (estimated) |
| Sponsor | National Defense Medical Center, Taiwan Academic / other |
| Locations | 1 site (Taipei) |
| Status | Recruiting |
| Started | November 1, 2025 |
| Main results expected | October 31, 2026 |
| Study ends | October 31, 2026 |
| Registry record updated | December 12, 2025 |
| Trial ID | NCT07217236 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a 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.
✕Probably 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.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
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.
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 gone: 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.
Who can join: the full rules
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 it's running
-
Tri-Service General Hospital
Taipei, Taiwan Recruiting
Study contacts
- Study coordinator: Chin Lin
- Email: xup6fup0629@gmail.com
- Phone: +886-2-8792-3100
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.