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 s 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 longterm 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, nonobese patients with a less remodeled atrium (left atrial dimension < 50 mm), 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).

Computational Modeling for Antiarrhythmic Drugs for Atrial Fibrillation According to Genotype

“…With the development of high-speed parallel computing hardware system (Boyle et al, 2019 ), AF simulation modeling has become more efficient for clinical applications. In particular, we previously reported that AF catheter ablation results can be predicted and improved by using computational modeling before the procedure (Kim et al, 2019 ). In this study, we created the realistic atrial model by reflecting personalized electroanatomy and modulating the specific ion currents for AADs.…”

Clinical Outcomes of Computational Virtual Mapping-Guided Catheter Ablation in Patients With Persistent Atrial Fibrillation: A Multicenter Prospective Randomized Clinical Trial

“…The CUVIA-AF 1 trial was the only prospective RCT of computational modeling-guided AFCA published to date ( 10 , 11 ). Kim et al ( 10 ) conducted a preprocedural virtual ablation to test the optimal linear lesion set using AF modeling (CUVIA version 1.0) ( 32 ), which reflected the patient-specific atrial anatomy, and adopted the best lesion set during the clinical AFCA procedure, which was superior to the empirical ablation in patients with PeAF.…”

“…In other words, the use of the current sequential atrial substrate mapping technology with a multielectrode catheter is not adequate to find the AF driver, as in contrast to the remarkable improvements made in the catheter technology for a long-lasting PV isolation (PVI). Recently, computational modeling has demonstrated the potential applicability in cardiac arrhythmia interventions ( 10 , 11 ). Computational modeling allows for high-density, entire-chamber mapping while reflecting the personalized atrial anatomy and physiology ( 12 ).…”

“…Computational modeling allows for high-density, entire-chamber mapping while reflecting the personalized atrial anatomy and physiology ( 12 ). It enables a mechanism-based virtual ablation test targeting various AF wave-dynamic parameters and the prediction of the clinical outcomes by a reproducible condition control ( 10 , 11 , 13 , 14 ). We previously reported the clinical feasibility and effectiveness of AF ablation lesion sets chosen using an in silico ablation relative to that of empirically chosen ablation lesion sets in patients with PeAF in a multicenter prospective clinical study ( 10 , 11 ).…”

“…It enables a mechanism-based virtual ablation test targeting various AF wave-dynamic parameters and the prediction of the clinical outcomes by a reproducible condition control ( 10 , 11 , 13 , 14 ). We previously reported the clinical feasibility and effectiveness of AF ablation lesion sets chosen using an in silico ablation relative to that of empirically chosen ablation lesion sets in patients with PeAF in a multicenter prospective clinical study ( 10 , 11 ). In this study, we improved the existing computational modeling software (CUVIA version 2.5; Laonmed Inc., Seoul, Korea), which enabled one to conduct an entire-chamber mapping of the AF drivers based on the acquired substrate map during atrial pacing.…”