Artificial Intelligence-Driven Algorithm for Drug Effect Prediction on Atrial Fibrillation: An in silico Population of Models Approach.

Ana Maria Sanchez De La Nava,Ángel Arenal,Francisco Fernández-Avilés,Felipe Atienza

doi:10.3389/fphys.2021.768468

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 us to identify proarrhythmic profiles based on pattern identification from in silico simulations.Methods: A population of models consisting of 127 electrophysiological profiles with a variation of nine electrophysiological variables (GNa, INaK, GK1, GCaL, GKur, IKCa, [Na]ext, and [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) atrium. The 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 was 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).Conclusion: Artificial intelligence 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.

Highlights

The first-line treatment for atrial fibrillation (AF) is antiarrhythmic drugs, undesirable proarrhythmic effects have been identified in some cases
From the complete population consisting of 127 different electrophysiological profiles, 80 profiles maintained the reentrant activity at baseline conditions in the normal tissue size and 87 in the dilated atrium
Complete quantification of the profiles maintaining reentry can be observed in Table 1, including the effect of the three simulated drugs

Summary

Introduction

The first-line treatment for atrial fibrillation (AF) is antiarrhythmic drugs, undesirable proarrhythmic effects have been identified in some cases. The response to these drugs is highly dependent on the specific baseline electrophysiological characteristics of the patient. Studies have explored electrophysiological variability to identify potential currents involved in AF triggering and maintenance (Ellinwood et al, 2017; Bai et al, 2020). These approaches usually focus at the unicellular level or present low variability at an electrophysiological level in the field of AF. Anatomical variability, and atrial size, have a strong influence on AF recurrence

Methods

Results

Conclusion