Extinction Dynamics of Cardiac Fibrillation

TL;DR

This paper introduces a stochastic population model to predict the duration of atrial fibrillation episodes, providing a computationally efficient method that aligns with the critical mass hypothesis and aids in therapeutic planning.

Contribution

It develops a novel stochastic modeling approach to estimate fibrillation duration, overcoming computational limitations of direct simulations.

Findings

Duration depends exponentially on tissue size

Model aligns with the critical mass hypothesis

Provides efficient predictions of fibrillation episodes

Abstract

During episodes of atrial fibrillation, the heart's electrical activity becomes disorganized and shows fragmenting spiral waves. To systematically address how this pattern terminates using spatially extended simulations exceeds current computational resources. To circumvent this limitation, we treat the number of spiral waves as a stochastic population with a corresponding birth-death equation and use techniques from statistical physics to determine the mean episode duration of atrial fibrillation. We show that this duration can be computed for arbitrary geometries in minimal computational time and that it depends exponentially on tissue size, consistent with the critical mass hypothesis which states that fibrillation requires a minimal organ size. Our approach can result in efficient and accurate predictions of mean episode duration, thus creating a potentially important step towards improved therapeutic interventions for atrial fibrillation.