Predicting Atrial Fibrillation in Patients With Post-implantable Cardiac Monitor Implementation Using AI ECG Analysis

What this Trial Is About

This research focuses on patients who have had an Embolic Stroke of Undetermined Source (ESUS) and have received an Implantable Cardiac Monitor (ICM). It evaluates the use of an artificial intelligence tool called SmartECG-AF, which analyzes standard 12-lead ECGs taken during normal heart rhythm to predict the risk of developing atrial fibrillation (AF). The study aims to understand if this AI tool can help identify which patients are more likely to experience AF and major cardiovascular events after ESUS. Participants will be divided into two groups based on the AI's risk assessment: a High Risk group and a Low to Intermediate Risk group. The study will follow these groups over time to compare the frequency and timing of AF events detected by the ICM. This multicenter, prospective study will also explore the relationship between the AI risk scores and the occurrence of major adverse cardiovascular events, helping to assess the value of AI-guided risk stratification. During the study, patients' baseline ECGs will be analyzed by the AI algorithm, and their heart rhythm will be monitored continuously through the ICM. Researchers will track the time to AF events and record any major cardiovascular incidents over a follow-up period of up to 12 months. Participants will be monitored for safety and clinical outcomes, providing data on how well the AI tool predicts cardiac risks in this population.

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