A Stochastic Individual-Based Model of the Progression of Atrial Fibrillation in Individuals and Populations
Authored by Tobias Galla, Eugene T Y Chang, Yen Ting Lin, Richard H Clayton, Julie Eatock
Date Published: 2016
DOI: 10.1371/journal.pone.0152349
Sponsors:
United Kingdom Engineering and Physical Sciences Research Council (EPSRC)
Platforms:
No platforms listed
Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
Model code not found
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.
Tags
initiation
Coupled chemical-reactions
Insights
Virtual human atria
Electrophysiological
characteristics
Molecular-mechanisms
Computer-model
Lifetime risk
Hatch score
Arrhythmogenesis