title: A Stochastic Individual-Based Model of the Progression of Atrial Fibrillation in Individuals and Populations.
abstract: Models that represent the mechanisms that initiate and sustain atrial fibrillation (AF) in the heart are computationally expensive to simulate and therefore only capture short time scales of a few heart beats. It is therefore difficult to embed biophysical mechanisms into both policy-level disease models, which consider populations of patients over multiple decades, and guidelines that recommend treatment strategies for patients. The aim of this study is to link these modelling paradigms using a stylised population-level model that both represents AF progression over a long time-scale and retains a description of biophysical mechanisms. We develop a non-Markovian binary switching model incorporating three different aspects of AF progression: genetic disposition, disease/age related remodelling, and AF-related remodelling. This approach allows us to simulate individual AF episodes as well as the natural progression of AF in patients over a period of decades. Model parameters are derived, where possible, from the literature, and the model development has highlighted a need for quantitative data that describe the progression of AF in population of patients. The model produces time series data of AF episodes over the lifetimes of simulated patients. These are analysed to quantitatively describe progression of AF in terms of several underlying parameters. Overall, the model has potential to link mechanisms of AF to progression, and to be used as a tool to study clinical markers of AF or as training data for AF classification algorithms.
authors.affiliation: Insigneo Institute for in-silico Medicine and Department of Computer Science, The University of Sheffield, Sheffield S1 4DP, United Kingdom.
authors.forename: Eugene T Y
authors.initials: ET
authors.lastname: Chang
authors.affiliation: School of Physics and Astronomy, The University of Manchester, Manchester M13 9PL, United Kingdom.
authors.forename: Yen Ting
authors.initials: YT
authors.lastname: Lin
authors.affiliation: School of Physics and Astronomy, The University of Manchester, Manchester M13 9PL, United Kingdom.
authors.forename: Tobias
authors.initials: T
authors.lastname: Galla
authors.affiliation: Insigneo Institute for in-silico Medicine and Department of Computer Science, The University of Sheffield, Sheffield S1 4DP, United Kingdom.
authors.forename: Richard H
authors.initials: RH
authors.lastname: Clayton
authors.affiliation: Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, Middlesex, United Kingdom.
authors.forename: Julie
authors.identifier: http://orcid.org/0000-0002-8963-8312
authors.initials: J
authors.lastname: Eatock
mesh_terms: D000328:Adult; D000465:Algorithms; D001281:Atrial Fibrillation / Q000473:pathology*; D018450:Disease Progression; D004562:Electrocardiography / Q000379:methods; D006339:Heart Rate / Q000502:physiology; D006801:Humans; D008875:Middle Aged