Computationally guided personalized targeted ablation of persistent atrial fibrillation
Patrick M. Boyle, Tarek Zghaib, Sohail Zahid, Rheeda L. Ali, Dongdong Deng, William H. Franceschi, Joe B. Hakim, Michael J. Murphy, Adityo Prakosa, Stefan L. Zimmerman, Hiroshi Ashikaga, Joseph E. Marine, Aravindan Kolandaivelu, Saman Nazarian, David D. Spragg, Hugh Calkins and Natalia A. Trayanova.
Nature biomedical engineering, (19 August 2019)
Atrial fibrillation (AF)—the most common arrhythmia—significantly increases the risk of stroke and heart failure. Although catheter ablation can restore normal heart rhythms, patients with persistent AF who develop atrial fibrosis often undergo multiple failed ablations, and thus increased procedural risks. Here, we present personalized computational modelling for the reliable predetermination of ablation targets, which are then used to guide the ablation procedure in patients with persistent AF and atrial fibrosis. First, we show that a computational model of the atria of patients identifies fibrotic tissue that, if ablated, will not sustain AF. Then, we report the results of integrating the target ablation sites in a clinical mapping system and testing its feasibility in ten patients with persistent AF. The computational prediction of ablation targets avoids lengthy electrical mapping and could improve the accuracy and efficacy of targeted AF ablation in patients while eliminating the need for repeat procedures.