Computational Modeling for Antiarrhythmic Drugs for Atrial Fibrillation According to Genotype.

The efficacy of antiarrhythmic drugs (AAD) can vary in patients with atrial fibrillation (AF), and the gene affects the responsiveness of AADs. We explored the virtual AAD (V-AAD) responses between wild-type and -deficient AF conditions by realistic AF modeling. We tested the V-AADs in AF modeling integrated with patients' 3D-computed tomography and 3D-electroanatomical mapping, acquired in 25 patients (68% male, 59.8 ± 9.8 years old, 32.0% paroxysmal type). The ion currents for the deficiency and each AAD (amiodarone, sotalol, dronedarone, flecainide, and propafenone) were defined based on previous publications. We compared the wild-type and deficiency in terms of the action potential duration (APD), conduction velocity (CV), maximal slope of restitution (Smax), and wave-dynamic parameters, such as the dominant frequency (DF), phase singularities (PS), and AF termination rates according to the V-AADs. The -deficient model exhibited a shorter APD ( < 0.001), a lower Smax ( < 0.001), mean DF ( = 0.012), PS number ( < 0.001), and a longer AF cycle length (AFCL, = 0.011). Five V-AADs changed the electrophysiology in a dose-dependent manner. AAD-induced AFCL lengthening ( < 0.001) and reductions in the CV ( = 0.033), peak DF ( < 0.001), and PS number ( < 0.001) were more significant in -deficient than wild-type AF. -deficient AF was easier to terminate with class IC AADs than the wild-type AF ( = 0.018). The computational modeling-guided AAD test was feasible for evaluating the efficacy of multiple AADs in patients with AF. AF wave-dynamic and electrophysiological characteristics are different among the -deficient and the wild-type genotype models.