Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/12745
Title: A Stochastic individual-based model of the progression of atrial fibrillation in individuals and populations
Authors: Chang, ETY
Lin, YT
Galla, T
Clayton, RH
Eatock, J
Keywords: Atrial fibrillation;Stylised population-level model;Biophysical mechanisms
Issue Date: 2016
Publisher: Public Library of Science
Citation: PLoS One, 11(4):e0152349, (2016)
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.
URI: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152349
http://bura.brunel.ac.uk/handle/2438/12745
DOI: http://dx.doi.org/10.1371/journal.pone.0152349
ISSN: 1932-6203
Appears in Collections:Dept of Computer Science Research Papers

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