Uncertainty and variability in computational and mathematical models of cardiac physiology.
Uncertainty and variability in computational and mathematical models of cardiac physiology.
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DOI：
10.1113/jp271671
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发表时间：
2016-12-01
期刊：
影响因子：
--
通讯作者：
Clayton RH
中科院分区：
文献类型：
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作者：
Mirams GR;Pathmanathan P;Gray RA;Challenor P;Clayton RH
心脏生理学的数学和计算模型自其成立以来一直是心脏电生理学的组成部分，并且统称为心脏生理组。我们确定和分类的可变性和不确定性的模型制定，参数和其他输入，从实验数据的自然变化和缺乏知识的众多来源。不确定性对Cardiac Physiome模型输出的影响尚不清楚，这限制了其作为临床工具的实用性。我们认为，纳入可变性和不确定性应该是心脏生理组的未来的高度优先事项。我们建议调查采用其他科学和工程领域开发的方法，同时认识到心脏生理学的独特挑战;很可能需要与数学和统计学界合作的新方法。心脏生理组的努力是数学和计算建模的最成熟和最成功的应用之一，用于描述和促进对生理学的理解。经过50年的发展，生理心脏模型有望通过临床应用实现转化研究的前景，如药物开发和患者特异性方法以及消融、心脏硬化和收缩调节治疗。对于作为安全关键应用中决策过程的重要组成部分的模型，将需要对模型可信度进行严格评估。本白色文件通过识别和分类模型中的变异性和不确定性的来源以及它们对心脏模型的应用和开发的影响来描述该过程的一个方面。我们强调需要理解和量化模型输入的可变性和不确定性的来源，以及模型结构和复杂性的影响及其对预测模型输出的后果。我们建议，心脏生理组的未来应该包括一个概率的方法来量化模型输入和输出的可变性和不确定性的关系。
Mathematical and computational models of cardiac physiology have been an integral component of cardiac electrophysiology since its inception, and are collectively known as the Cardiac Physiome. We identify and classify the numerous sources of variability and uncertainty in model formulation, parameters and other inputs that arise from both natural variation in experimental data and lack of knowledge. The impact of uncertainty on the outputs of Cardiac Physiome models is not well understood, and this limits their utility as clinical tools. We argue that incorporating variability and uncertainty should be a high priority for the future of the Cardiac Physiome. We suggest investigating the adoption of approaches developed in other areas of science and engineering while recognising unique challenges for the Cardiac Physiome; it is likely that novel methods will be necessary that require engagement with the mathematics and statistics community. The Cardiac Physiome effort is one of the most mature and successful applications of mathematical and computational modelling for describing and advancing the understanding of physiology. After five decades of development, physiological cardiac models are poised to realise the promise of translational research via clinical applications such as drug development and patient‐specific approaches as well as ablation, cardiac resynchronisation and contractility modulation therapies. For models to be included as a vital component of the decision process in safety‐critical applications, rigorous assessment of model credibility will be required. This White Paper describes one aspect of this process by identifying and classifying sources of variability and uncertainty in models as well as their implications for the application and development of cardiac models. We stress the need to understand and quantify the sources of variability and uncertainty in model inputs, and the impact of model structure and complexity and their consequences for predictive model outputs. We propose that the future of the Cardiac Physiome should include a probabilistic approach to quantify the relationship of variability and uncertainty of model inputs and outputs.