Feasibility of Auto-Quantified Epicardial Adipose Tissue in Predicting Atrial Fibrillation Recurrence After Catheter Ablation

Background: The aim of this study was to build an auto-segmented artificial intelligence model of the atria and epicardial adipose tissue (EAT) on computed tomography (CT) images, and examine the prognostic significance of auto-quantified left atrium (LA) and EAT volumes for AF. Methods and Results: This retrospective study included 334 patients with AF who were referred for catheter ablation (CA) between 2015 and 2017. Atria and EAT volumes were auto-quantified using a pre-trained 3-dimensional (3D) U-Net model from pre-ablation CT images. After adjusting for factors associated with AF, Cox regression analysis was used to examine predictors of AF recurrence. The mean (±SD) age of patients was 56±11 years; 251 (75%) were men, and 79 (24%) had non-paroxysmal AF. Over 2 years of follow-up, 139 (42%) patients experienced recurrence. Diabetes, non-paroxysmal AF, non-pulmonary vein triggers, mitral line ablation, and larger LA, right atrium, and EAT volume indices were linked to increased hazards of AF recurrence. After multivariate adjustment, non-paroxysmal AF (hazard ratio [HR] 0.6; 95% confidence interval [CI] 0.4-0.8; P=0.003) and larger LA-EAT volume index (HR 1.1; 95% CI 1.0-1.2; P=0.009) remained independent predictors of AF recurrence. Conclusions: LA-EAT volume measured using the auto-quantified 3D U-Net model is feasible for predicting AF recurrence after CA, regardless of AF type.