NCT07347691

AI-Based Prediction of Atrial Fibrillation in ESUS Patients With ICM

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

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 92 (estimated)
• Sponsor: Inha University Hospital · Academic / Other
• Sex: All
• Age: 30 Years
• Healthy volunteers: Not accepted

Summary

This study investigates patients with Embolic Stroke of Undetermined Source (ESUS) who have received an Implantable Cardiac Monitor (ICM). The main purpose is to evaluate the predictive value of an Artificial Intelligence ECG analysis tool, named SmartECG-AF. Participants will be classified into two groups based on the AI analysis: a "High Risk" group and a "Low to Intermediate Risk" (control) group. The study aims to compare the incidence rate of atrial fibrillation (AF) events over time between these two groups. Additionally, the study will analyze the relationship between the AI-predicted risk levels and the occurrence of major cardiovascular events during the follow-up period.

Detailed description

Embolic Stroke of Undetermined Source (ESUS) accounts for a significant proportion of ischemic strokes, and occult Atrial Fibrillation (AF) is considered a major etiology. While Implantable Cardiac Monitors (ICMs) are the gold standard for long-term rhythm monitoring, identifying patients at the highest risk for AF remains a clinical challenge. This multicenter, prospective study aims to validate the clinical utility of an artificial intelligence-based electrocardiogram analysis algorithm, "SmartECG-AF," in this specific population. The algorithm analyzes 12-lead ECGs recorded during sinus rhythm to detect subtle signs of electrical remodeling associated with paroxysmal AF. Enrolled patients with ESUS who have undergone ICM implantation will have their baseline ECGs analyzed by the SmartECG-AF algorithm. Based on the AI-generated probability score, patients will be stratified into a "High Risk" group and a "Low to Intermediate Risk" group. The study will longitudinally track these patients to compare the time-to-event for ICM-detected AF between the two groups. Additionally, the study will evaluate the correlation between the AI risk score and the incidence of Major Adverse Cardiovascular Events (MACE), providing evidence for AI-guided risk stratification in cryptogenic stroke management.

Conditions

• Embolic Stroke of Undetermined Source

Timeline

• Start date: 2025-11-19
• Primary completion: 2027-05-01
• Completion: 2028-05-01
• First posted: 2026-01-16
• Last updated: 2026-01-16

Locations

5 sites across 1 country: South Korea