Mechanisms of stochastic onset and termination of atrial fibrillation episodes: Insights using a cellular automaton model

TL;DR

This study uses a cellular automaton model on a realistic atrial geometry to simulate and analyze the stochastic initiation and termination of atrial fibrillation episodes, providing insights into underlying mechanisms and uncertainty propagation.

Contribution

It introduces a simplified, computationally efficient cellular automaton model on a topologically accurate atrial surface to study AF dynamics and stochastic behaviors.

Findings

Simulated stochastic AF initiation and termination events.

Generated statistical distributions of AF episode durations.

Identified complex dependence of termination on past AF trajectory.

Abstract

Mathematical models of cardiac electrical excitation are increasingly complex, with multiscale models seeking to represent and bridge physiological behaviours across temporal and spatial scales. The increasing complexity of these models makes it computationally expensive to both evaluate long term (>60 seconds) behaviour and determine sensitivity of model outputs to inputs. This is particularly relevant in models of atrial fibrillation (AF), where individual episodes last from seconds to days, and inter-episode waiting times can be minutes to months. Potential mechanisms of transition between sinus rhythm and AF have been identified but are not well understood, and it is difficult to simulate AF for long periods of time using state-of-the-art models. In this study, we implemented a Moe-type cellular automaton on a novel, topologically correct surface geometry of the left atrium. We used the model to simulate stochastic initiation and spontaneous termination of AF, arising from bursts of spontaneous activation near pulmonary veins. The simplified representation of atrial electrical activity reduced computational cost, and so permitted us to investigate AF mechanisms in a probabilistic setting. We computed large numbers (~10^5) of sample paths of the model, to infer stochastic initiation and termination rates of AF episodes using different model parameters. By generating statistical distributions of model outputs, we demonstrated how to propagate uncertainties of inputs within our microscopic level model up to a macroscopic level. Lastly, we investigated spontaneous termination in the model and found a complex dependence on its past AF trajectory, the mechanism of which merits future investigation.