AI-ECG alerts to catch early atrial fibrillation risk
Leveraging AI-ECG Technology for Early Notification and Tracking of AF Development: a Randomized Control Trial
This trial tests whether an AI-powered ECG alert can help doctors find people at high risk of developing atrial fibrillation so they can be given continuous heart-rhythm monitoring.
Quick facts
| Phase | Not applicable |
|---|---|
| Study type | Interventional |
| Enrollment | 14726 (estimated) |
| Ages | 40 Days to 85 Days |
| Sex | All |
| Sponsor | National Defense Medical Center, Taiwan Academic / other |
| Locations | 1 site (Taipei) |
| Trial ID | NCT06847932 on ClinicalTrials.gov |
What this trial studies
This interventional study uses an AI algorithm applied to routine ECGs to flag patients at high risk of new atrial fibrillation, atrial flutter, or significant atrial arrhythmias. Flagged patients are recommended for continuous cardiac rhythm monitoring to detect incident arrhythmias. Eligible participants are hospital inpatients or outpatients with at least one ECG in the prior year and without prior AF/AFL, prior ablation, implantable cardiac devices, rhythm-control drugs, or indications for anticoagulation. The trial is conducted at Tri-Service General Hospital in Taipei and tracks whether AI-guided alerts lead to earlier identification of AF/AFL compared with usual care pathways.
Who should consider this trial
Good fit: Adults seen as inpatients or outpatients who have at least one ECG in the past year and do not have prior AF/AFL, prior AF/AFL ablation, implanted cardiac devices, rhythm-control antiarrhythmic drugs, or current indications for anticoagulation.
Not a fit: People with a prior or current diagnosis of AF/AFL, prior AF/AFL ablation, cardiac implantable devices, those already on rhythm-control medications, or those already requiring anticoagulation are unlikely to benefit from this screening-focused approach.
Why it matters
Potential benefit: If successful, this approach could enable earlier detection of atrial fibrillation and timely use of monitoring and treatment to reduce stroke risk.
How similar studies have performed: Previous studies have shown AI-ECG algorithms can predict future AF risk from routine ECGs, and while using AI alerts to direct continuous monitoring is relatively new, early pilot data are encouraging.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * Patients in the inpatient department or the outpatient department * Patients need to have at least one electrocardiogram within one year Exclusion Criteria: * Diagnosis of atrial fibrillation/atrial flutter * History of atrial fibrillation/atrial flutter catheter ablation * Patients with cardiac implantable electronic devices * Any documented electrocardiogram showed atrial fibrillation/atrial flutter and pacing rhythm * History of received rhythm control medications for atrial arrhythmia, including Class I and Class III antiarrhythmic drugs * Any reasons indicate for anti-coagulant agents, including vitamin K antagonist and non-vitamin K antagonist oral anticoagulant
Where this trial is running
Taipei
- Tri-Service General Hospital — Taipei, Taiwan (Recruiting)
Study contacts
- Principal investigator: Chin Lin, PhD — National Defense Medical Center
- Study coordinator: Chin Lin, PhD
- Email: xup6fup@mail.ndmctsgh.edu.tw
- Phone: +886-2-8792-3100 Ext. 18574
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.