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Updated: Oct 9, 2025
Author Spotlight: Developing a Translational Model for Atrial Fibrillation Research Across Species
Published on: November 21, 2023
Artificial Intelligence-Driven Algorithm for Drug Effect Prediction on Atrial Fibrillation: An in silico Population
Ana Maria Sanchez de la Nava1,2,3, Ángel Arenal1,2,4, Francisco Fernández-Avilés1,2,4
1Department of Cardiology, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IISGM), Madrid, Spain.
Insights
Artificial intelligence identified key electrophysiological patterns predicting proarrhythmic risk in atrial fibrillation (AF) patients. AI analysis revealed G- a critical factor and highlighted drug effects varying with patient profiles and atrial size.
Area of Science:
- Computational electrophysiology
- Artificial intelligence in medicine
- Cardiovascular research
Background:
- Antiarrhythmic drugs are standard atrial fibrillation (AF) treatment, but patient-specific factors and atrial size influence efficacy.
- Anatomical variability, particularly atrial size, significantly impacts AF recurrence rates.
- Personalized medicine approaches are needed to optimize AF treatment strategies.
Purpose of the Study:
- To develop and validate an artificial intelligence (AI) model for identifying proarrhythmic profiles in silico.
- To analyze the influence of electrophysiological variables and atrial size on AF inducibility.
- To assess the impact of specific antiarrhythmic drugs on different electrophysiological profiles.
Main Methods:
- Simulated 127 electrophysiological profiles using the Koivumaki atrial model with variations in nine key ionic variables.
- Implemented three drugs (isoproterenol, flecainide, verapamil) using a simple pore channel equation.
- Trained a Random Forest AI algorithm to predict AF inducibility based on simulated data, with k-fold cross-validation.
Main Results:
- Identified two distinct electrophysiological patterns associated with proarrhythmic behavior using AI.
- Found that the G- current was the most significant predictor of proarrhythmicity.
- Observed variable drug effects based on electrophysiological profiles and a higher fibrillation tendency in dilated atrial tissue.
Conclusions:
- AI algorithms offer a novel approach for identifying electrophysiological patterns in AF.
- AI can aid in analyzing antiarrhythmic drug effects across heterogeneous patient populations.
- This study demonstrates AI's potential for personalized risk stratification and treatment selection in AF.
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 (G , I , G , G , G , I , [Na] , and [K] 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 G 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.
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