Reentrant Drivers Are Distinguished From Passive Rotors by Sequential Computer Simulation of Ablation Using Personalized Non-Paroxysmal Atrial Fibrillation Heart Models Based on Late Gadolinium Enhancement Magnetic Resonance Imaging (LGE-MRI)

Introduction: Although rotor modulation targeting atrial fibrillation (AF) drivers or substrates has been proposed as one of the effective ablation strategies for non-paroxysmal AF (Non-PAF), previous meta-analyses demonstrated that ablation added to pulmonary vein isolation (PVI) did not result in the expected outcome. This is because the optimal method of detection of rotors and ablation strategy remain unclear. Recently, rotor detection using LGE-MRI-based computer simulations has been shown to be effective for Non-PAF ablation in clinical practice. Our study aimed to establish the minimal ablation strategy that, while correctly finding the rotors, also avoids iatrogenic atrial tachyarrhythmias due to excessive ablation. Hypothesis: By prioritizing and classifying detected rotors, reentrant drivers (RDs) of AF rather than passive rotors (PRs) could be identified, thereby offering an optimal ablation strategy. Methods: Personalized computational modeling of AF ablation was performed in 10 Non-PAF patient models based on fibrosis data from LGE-MRI. In each bi-atrial model, all rotors induced outside of PVI were investigated, and a number of ablation strategies were examined sequentially to classify rotors and achieve minimal ablation (figure). A rotor that terminated following ablation of another rotor was defined as PR. A rotor that persisted and needed to be ablated to achieve non-inducibility of the substrate was defined as RD. Results: Seven patients had rotors outside of PVI, with 6 having both PRs and RDs. Overall, 35 rotors were induced, 13 in left atrium and 22 in the right; 17 were RDs and 18 PRs. In addition, the density of fibrosis in the sites of RDs was significantly higher than in those of PRs (p=0.031). Conclusion: The sequential computer simulation strategy to predict ablation targets using a personalized AF model is promising in detecting different types of rotors and establishing the optimal minimum-lesion ablation strategy.