AI analysis of ECGs to predict atrial fibrillation risk in ESUS patients with an implantable cardiac monitor.
Predicting Atrial Fibrillation in Patients With Post-implantable Cardiac Monitor Implementation : A Prospective, Long-term Follow-up Study Using Comprehensive AI ECG Analysis : Multicenter Prospective Study
This project tests whether an AI tool called SmartECG-AF can use a routine 12-lead ECG to predict which ESUS patients with an implantable cardiac monitor will develop atrial fibrillation.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 92 (estimated) |
| Ages | 30 Years and up |
| Sex | All |
| Sponsor | Inha University Hospital Academic / other |
| Locations | 5 sites (Ansan and 4 other locations) |
| Trial ID | NCT07347691 on ClinicalTrials.gov |
What this trial studies
This multicenter prospective observational study applies the SmartECG-AF deep learning algorithm to baseline 12-lead ECGs from patients with Embolic Stroke of Undetermined Source who have an implantable cardiac monitor (ICM). Patients will be classified by the AI score into a High Risk group and a Low-to-Intermediate Risk control group and then followed longitudinally using ICM recordings. The primary analysis compares time-to-first ICM-detected atrial fibrillation event between risk groups, and secondary analyses examine the relationship between AI-predicted risk and major adverse cardiovascular events (MACE). The study aims to validate whether AI-guided ECG risk stratification can identify occult AF and inform post-ESUS monitoring strategies.
Who should consider this trial
Good fit: Ideal candidates are adults (≥30 years) with ESUS who have undergone or are scheduled for ICM implantation, have a sinus-rhythm 12-lead ECG within two weeks of implant, and can provide informed consent.
Not a fit: Patients with a prior diagnosis of AF, nonfunctional ICMs, or ECGs that are too noisy or incompatible with the AI algorithm are unlikely to benefit from this approach.
Why it matters
Potential benefit: If successful, this could help identify ESUS patients at high risk of hidden AF so clinicians can target monitoring or treatment earlier to reduce recurrent strokes.
How similar studies have performed: Previous research has shown AI ECG algorithms can detect signatures associated with paroxysmal AF and predict future AF in other populations, but applying and validating this approach specifically in ESUS patients with ICMs remains novel.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * Patients aged 30 years or older. * Patients diagnosed with Embolic Stroke of Undetermined Source (ESUS) who have undergone or are scheduled for Implantable Cardiac Monitor (ICM) implantation. * Patients who have undergone at least one 12-lead ECG examination within 2 weeks before or after the date of ICM implantation. * Patients maintaining Sinus Rhythm on ECG at the time of enrollment. * Patients who have voluntarily signed the informed consent form. Exclusion Criteria: * Patients diagnosed with Atrial Fibrillation (AF) at least once prior to the date of enrollment. * Patients whose ICM battery status is at Elective Replacement Interval (ERI), making recording impossible. * Patients whose ECGs cannot be analyzed by the AI algorithm (SmartECG-AF) due to severe artifacts or noise, or are incompatible with digital analysis.
Where this trial is running
Ansan and 4 other locations
- Korea University Ansan Hospital — Ansan, South Korea (Recruiting)
- Inha University Hospital — Incheon, South Korea (Recruiting)
- Jeju National University Hospital — Jeju City, South Korea (Recruiting)
- Korea University Guro Hospital — Seoul, South Korea (Recruiting)
- Ajou University Hospital — Suwon, South Korea (Recruiting)
Study contacts
- Principal investigator: Yong-Soo Baek, MD, PhD — Inha University Hospital
- Study coordinator: Yong-Soo Baek, MD, PhD
- Email: existsoo@inha.ac.kr
- Phone: +82-32-890-2200
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.