Artificial intelligence estimated electrocardiographic age as a recurrence predictor after atrial fibrillation catheter ablation
Hanjin Park, Oh‐Seok Kwon, Jaemin Shim, Daehoon Kim, Je‐Wook Park, Yun-Gi Kim, Hee Tae Yu, Tae‐Hoon Kim, Jae‐Sun Uhm, Jong‐Il Choi, Boyoung Joung, Moon‐Hyoung Lee, Hui‐Nam Pak
Abstract
The application of artificial intelligence (AI) algorithms to 12-lead electrocardiogram (ECG) provides promising age prediction models. We explored whether the gap between the pre-procedural AI-ECG age and chronological age can predict atrial fibrillation (AF) recurrence after catheter ablation. We validated a pre-trained residual network-based model for age prediction on four multinational datasets. Then we estimated AI-ECG age using a pre-procedural sinus rhythm ECG among individuals on anti-arrhythmic drugs who underwent de-novo AF catheter ablation from two independent AF ablation cohorts. We categorized the AI-ECG age gap based on the mean absolute error of the AI-ECG age gap obtained from four model validation datasets; aged-ECG (≥10 years) and normal ECG age (<10 years) groups. In the two AF ablation cohorts, aged-ECG was associated with a significantly increased risk of AF recurrence compared to the normal ECG age group. These associations were independent of chronological age or left atrial diameter. In summary, a pre-procedural AI-ECG age has a prognostic value for AF recurrence after catheter ablation.