Algorithms trained on simulated atrial fibrillation episodes predicted successful pharmacological cardioversion with >70% accuracy using either ionic current profiles or ECG metrics.
Can in-silico modeling of ionic current properties and ECG metrics predict pharmacological cardioversion of atrial fibrillation in structurally-healthy human atria?
In-silico models of structurally-healthy human atria demonstrate that AF inducibility is driven by discordant alternans and high excitability, and pharmacological cardioversion can be predicted with >70% accuracy using ionic or ECG properties.
Atrial fibrillation (AF) inducibility, sustainability and response to pharmacological treatment of individual patients are expected to be determined by their ionic current properties, especially in structurally-healthy atria. Mechanisms underlying AF and optimal cardioversion are however still unclear. In this study, in-silico drug trials were conducted using a population of human structurally-healthy atria models to 1) identify key ionic current properties determining AF inducibility, maintenance and pharmacological cardioversion, and 2) compare the prognostic value for predicting individual AF cardioversion of ionic current properties and electrocardiogram (ECG) metrics. In the population of structurally-healthy atria, 477 AF episodes were induced in ionic current profiles with both steep action potential duration (APD) restitution (eliciting APD alternans), and high excitability (enabling propagation at fast rates that transformed alternans into discordant). High excitability also favored 211 sustained AF episodes, so its decrease, through prolonged refractoriness, explained pharmacological cardioversion. In-silico trials over 200 AF episodes, 100 ionic profiles and 10 antiarrhythmic compounds were consistent with previous clinical trials, and identified optimal treatments for individual electrophysiological properties of the atria. Algorithms trained on 211 simulated AF episodes exhibited >70% accuracy in predictions of cardioversion for individual treatments using either ionic current profiles or ECG metrics. In structurally-healthy atria, AF inducibility and sustainability are enabled by discordant alternans, under high excitability and steep restitution conditions. Successful pharmacological cardioversion is predicted with 70% accuracy from either ionic or ECG properties, and it is optimal for treatments maximizing refractoriness (thus reducing excitability) for the given ionic current profile of the atria.
Dasí et al. (Thu,) conducted a other in Atrial fibrillation. In-silico antiarrhythmic compounds was evaluated on Prediction accuracy of cardioversion. Algorithms trained on simulated atrial fibrillation episodes predicted successful pharmacological cardioversion with >70% accuracy using either ionic current profiles or ECG metrics.