A Stochastic Individual-Based Model of the Progression of Atrial Fibrillation in Individuals and Populations.
A Stochastic Individual-Based Model of the Progression of Atrial Fibrillation in Individuals and Populations.
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DOI：
10.1371/journal.pone.0152349
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Eatock J
中科院分区：
文献类型：
--
作者：
Chang ET;Lin YT;Galla T;Clayton RH;Eatock J
代表心脏中引发和维持心房颤动 (AF) 机制的模型的模拟计算成本很高，因此只能捕获几次心跳的短时间尺度。因此，很难将生物物理机制嵌入到考虑数十年患者群体的政策级疾病模型和为患者推荐治疗策略的指南中。本研究的目的是使用程式化的群体水平模型将这些建模范式联系起来，该模型既代表了长期的房颤进展，又保留了对生物物理机制的描述。我们开发了一种非马尔可夫二元转换模型，结合了 AF 进展的三个不同方面：遗传倾向、疾病/年龄相关重塑和 AF 相关重塑。这种方法使我们能够模拟个体 AF 发作以及患者几十年来 AF 的自然进展。模型参数尽可能从文献中得出，模型的开发强调了对描述患者群体中 AF 进展的定量数据的需求。该模型生成模拟患者一生中 AF 发作的时间序列数据。对这些数据进行分析，以根据几个基本参数定量描述 AF 的进展。总体而言，该模型有潜力将 AF 机制与进展联系起来，并可用作研究 AF 临床标志物的工具或用作 AF 分类算法的训练数据。
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.