Clinical Usefulness of Computational Modeling-Guided Persistent Atrial Fibrillation Ablation: Updated Outcome of Multicenter Randomized Study

Abstract

Objective: Catheter ablation of persistent atrial fibrillation (AF) is still challenging, no optimal extra-pulmonary vein lesion set is known. We previously reported the clinical feasibility of computational modeling-guided AF catheter ablation. Methods: We randomly assigned 118 patients with persistent AF (77.8% men, age 60.8±9.9 years) to the computational modeling-guided ablation group (53 patients) and the empirical ablation group (55 patients) based on the operators’ experience. For virtual ablation, four virtual linear and one electrogram-guided lesion sets were tested on patient heart computed tomogram-based models, and the lesion set with the fastest termination time was reported to the operator in the modeling-guided ablation group. The primary outcome was freedom from atrial tachyarrhythmias lasting longer than 30 seconds after a single procedure. Results: During 31.5±9.4 months, virtual ablation procedures were available in 95.2% of the patients (108/118). Clinical recurrence rate was significantly lower after a modeling-guided ablation than after an empirical ablation (20.8% vs. 40.0%, log-rank p=0.042). Modeling-guided ablation was independently associated with a better long-term rhythm outcome of persistent AF ablation (HR=0.29 [0.12-0.69], p=0.005). The rhythm outcome of the modeling-guided ablation showed better trends in males, non-obese patients with a less remodeled atrium (left atrial dimension<50mm), ejection fraction≥50%, and those without hypertension or diabetes (p<0.01). There were no significant differences between the groups for the total procedure time (p=0.403), ablation time (p=0.510), and major complication rate (p=0.900). Conclusions: Among patients with persistent AF, the computational modeling-guided ablation was superior to the empirical catheter ablation regarding the rhythm outcome. Trial registration: This study was registered with the ClinicalTrials.gov, number NCT02171364.