Left atrial appendage volume estimated by artificial intelligence predicts atrial fibrillation recurrence after cryobaloon ablation

Abstract Background/Introduction The volume of the left atrial appendage (LAA), as measured in contrast-enhanced cardiac computed tomography (CCTA), has been previously reported as a predictor of long-term recurrence of atrial fibrillation (AF) following cryoballoon ablation procedures. Unfortunately manual assessment of LAA volume, is tedious and time-consuming, making it impractical for clinical practice. The recent advent of artificial intelligence (AI)-based methods offers a potential solution to this limitation. Purpose This study aims to evaluate the utility of LAA volume, as automatically measured by a state-of-the-art AI model for the fully automatic segmentation of LAA in CCTA, in predicting AF recurrence following cryoballoon ablation procedures. Methods We retrospectively analyzed a cohort of patients who underwent cryoballoon ablation for pulmonary vein isolation at our facility between 2010 and 2016. AF recurrence was determined through in-person visits and Holter monitoring at 3, 6, and 12 months post-ablation, followed by annual visits. The first three months post-ablation were considered a blanking period. An open-source deep neural network AI model [1] was utilized to perform automatic segmentation and calculation of LAA and left atrium (LA) volumes in. Receiver-operating characteristic curves were used to obtain optimal cut-off values for increased LAA and LA volumes. We then assessed the association of increased LAA and LA volume, and the LAA to LA volume ratio with recurrence-free survival using the Cox regression model. Results The cohort consisted of 212 patients (median age 62, inter-quartile range [IQR]: 55–65), with 25% having persistent AF before the procedure. Subjects were observed for a median period of 12 months (IQR: 12–36), with 70 of them (33%) experiencing a documented recurrent AF episode. The optimal thresholds for predicting AF recurrence were established as 9.8ml for LAA volume, 124ml for LA volume, and 0.086 for the LAA to LA volume ratio, respectively. An increased LAA volume demonstrated the strongest association with AF recurrence (hazard ratio [HR]: 2.6, 95% confidence interval [CI]: 1.55–4.2), followed by an increased LA volume (HR: 2.1, 95% CI: 1.3–3.4), persistence of AF (HR: 2, 95% CI: 1.2–3.3), and increased LAA to LA volume ratio (HR: 1.9, 95% CI: 1.1–3). Kaplan-Meier plots illustrating AF-free survival, are presented in Figure 1. After adjusting for age, persistent AF, left ventricular ejection fraction, and body surface area, all AI measurements remained significantly associated with a higher AF recurrence (Table 1). Conclusions Increased LAA volume, as assessed automatically by AI in CCTA, is a useful predictor of AF recurrence after catheter ablation for atrial fibrillation. The use of freely available, AI-assisted analysis of CCTA could be instrumental in assessing LAA volume, among other predictors, for long-term ablation success.Figure 1.