Data_Sheet_1.DOCX

Abstract

Background: Antiarrhythmic drugs are the first-line treatment for atrial fibrillation (AF), but their effect is highly dependent on the characteristics of the patient. Moreover, anatomical variability, and specifically atrial size, have also a strong influence on AF recurrence. Objective: We performed a proof of concept study using artificial intelligence (AI) that enabled to identify proarrhythmic profiles based on pattern identification from in silico simulations. Methods: A population of models consisting on 127 electrophysiological profiles with variation of nine electrophysiological variables (GNa, INaK, GK1, GCaL, GKur, IKCa, [Na]ext,[K]ext and diffusion) was simulated using the Koivumaki atrial model on square planes corresponding to a normal (16 cm2) and dilated (22.5 cm2) atria. Simple pore channel equation was used for drug implementation including three drugs (isoproterenol, flecainide and verapamil). We analyzed the effect of every ionic channel combination to evaluate arrhythmia induction. A Random Forest algorithm was trained using the population of models and AF inducibility as input and output, respectively. The algorithm was trained with 80% of the data (N = 832) and 20% of the data was used for testing with a k-fold cross validation (k = 5). Results: We found two electrophysiological patterns derived from the AI algorithm that were associated with proarrhythmic behavior in most of the profiles, where GK1 was identified as the most important current for classifying the proarrhythmicity of a given profile. Additionally, we found different effects of the drugs depending on the electrophysiological profile and a higher tendency of the dilated tissue to fibrillate (Small tissue: 80 profiles vs Dilated tissue: 87 profiles). Conclusions: AI algorithms appear as a novel tool for electrophysiological pattern identification and analysis of the effect of antiarrhythmic drugs on a heterogeneous population of patients with AF.