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.
Highlights
Atrial fibrillation (AF) is the most prevalent disorder of heart rhythm
Each run generates a series of AF episodes over several decades; typically we see gradual progression of AF from paroxysmal to permanent
These synthetic data differ from existing clinical AF data: real-world AF ECGs are sampled at several kilohertz over short monitoring periods from hours to months, whilst our data simplifies the ECG in to sequences of binary AF/sinus rhythm episodes
Summary
Atrial fibrillation (AF) is the most prevalent disorder of heart rhythm. It is characterised by an irregular or rapid heartbeat, is increasingly common as patients become older, and is associated with a five-fold increase in the incidence of ischaemic stroke [1]. A Stochastic Individual-Based Model of the Progression of Atrial Fibrillation clinicians with the most cost-effective care pathway for AF patients [2, 3]. Much is known about the biophysical mechanisms that initiate and sustain AF in animal hearts [4], but this knowledge is at present not fully taken into account when developing guidance for cost effective treatment at patient and population level. We present a stochastic individual-based model that aims to bridge biophysical models and population-level models used in policy making. The model has been developed to identify biophysical features that have the greatest impact on cost-effectiveness at a population level, and could be developed to influence the guidance provided to clinicians. The potential applications that a model like this could contribute to include: (i) Predicting or investigating the impact that different treatment strategies (e.g. rate/rhythm control, ablation) on the progression of AF in an individual; (ii) understanding the drivers of progression, that could be exploited by new therapeutic interventions; and (iii) measuring and quantifying progression in individuals to facilitate remote monitoring and tele-health applications
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