Why the study?
To explore the anti-AF mechanisms of antiarrhythmic drugs and spatial changes in AF wave-dynamics using a realistic computational model.
Do antiarrhythmic drugs alter the spatial wave-dynamics (dominant frequency and its coefficient of variation) in a computational model of atrial fibrillation?
Do antiarrhythmic drugs alter the spatial wave-dynamics (dominant frequency and its coefficient of variation) in a computational model of atrial fibrillation?
Computational modeling demonstrates that antiarrhythmic drugs reduce dominant frequency predominantly in the pulmonary veins, leading to AF termination or defragmentation under spatially unstable conditions.
Answers are grounded in this paper and cite their sources.
These computational findings should not alter AAD use in AF patients; leaves open translation of regional DF effects to clinical outcomes.
Background: We previously reported that a computational modeling-guided antiarrhythmic drug (AAD) test was feasible for evaluating multiple AADs in patients with atrial fibrillation (AF). We explored the anti-AF mechanisms of AADs and spatial change in the AF wave-dynamics by a realistic computational model. Methods: We used realistic computational modeling of 25 AF patients (68% male, 59.8 ± 9.8 years old, 32.0% paroxysmal AF) reflecting the anatomy, histology, and electrophysiology of the left atrium (LA) to characterize the effects of five AADs (amiodarone, sotalol, dronedarone, flecainide, and propafenone). We evaluated the spatial change in the AF wave-dynamics by measuring the mean dominant frequency (DF) and its coefficient of variation [dominant frequency-coefficient of variation (DF-COV)] in 10 segments of the LA. The mean DF and DF-COV were compared according to the pulmonary vein (PV) vs. extra-PV, maximal slope of the restitution curves (Smax), and defragmentation of AF. Results: The mean DF decreased after the administration of AADs in the dose dependent manner ( p < 0.001). Under AADs, the DF was significantly lower ( p < 0.001) and COV-DF higher ( p = 0.003) in the PV than extra-PV region. The mean DF was significantly lower at a high Smax (≥1.4) than a lower Smax condition under AADs. During the episodes of AF defragmentation, the mean DF was lower ( p < 0.001), but the COV-DF was higher ( p < 0.001) than that in those without defragmentation. Conclusions: The DF reduction with AADs is predominant in the PVs and during a high Smax condition and causes AF termination or defragmentation during a lower DF and spatially unstable (higher DF-COV) condition.
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Hwang et al. (2021) studied this question.
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