Modelling the progression of atrial fibrillation: A stochastic individual-based approach

TL;DR

This paper introduces a stochastic individual-based model for atrial fibrillation progression, integrating physiological insights to analyze disease dynamics at the patient level over a lifetime.

Contribution

It presents a novel stochastic modeling approach that combines mechanistic physiology with population-level analysis for AF progression.

Findings

Model simulates patient-specific AF and normal rhythm durations.

Provides statistical insights into AF progression at population level.

Lays groundwork for personalized medicine applications.

Abstract

We propose a stochastic individual-based model of the progression of atrial fibrillation (AF). The model operates at patient level over a lifetime and is based on elements of the physiology and biophysics of AF, making contact with existing mechanistic models. The outputs of the model are times when the patient is in normal rhythm and AF, and we carry out a population-level analysis of the statistics of disease progression. While the model is stylised at present and not directly predictive, future improvements are proposed to tighten the gap between existing mechanistic models of AF, and epidemiological data, with a view towards model-based personalised medicine.