Artificial intelligence-estimated electrocardiographic sex as a recurrence predictor after atrial fibrillation catheter ablation.

ABSTRACT:

Aims

We explored whether artificial intelligence (AI)-enabled electrocardiographic (ECG) sex discrepancy would predict atrial fibrillation (AF) recurrence after catheter ablation for paroxysmal AF.

Methods and results

The AI-ECG sex prediction model was developed from the MIMIC-IV and externally validated on CODE-15% (AUC 0.89) and UK Biobank (AUC 0.92) cohorts. After validation, we estimated AI-ECG sex from pre-procedural sinus rhythm ECGs among paroxysmal AF patients scheduled for catheter ablation using data from a pooled AF ablation cohort (n = 4385) in South Korea. ECG sex discrepancy was defined as ECG sex probability of more than 50% for the opposite sex. During a median follow-up of 24 months, 1094 recurrences developed [median age 60 (52-67) years; women 29.0

SUBMITTER: Park H 

PROVIDER: S-EPMC12282368 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature