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.