title: Uncertainty and variability in computational and mathematical models of cardiac physiology.
abstract: 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.
authors.affiliation: Computational Biology, Department of Computer Science, University of Oxford, Oxford, OX1 3QD, UK.
authors.forename: Gary R
authors.identifier: 0000-0002-4569-4312
authors.initials: GR
authors.lastname: Mirams
authors.affiliation: US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA.
authors.forename: Pras
authors.initials: P
authors.lastname: Pathmanathan
authors.affiliation: US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA.
authors.forename: Richard A
authors.identifier: 0000-0003-2798-6378
authors.initials: RA
authors.lastname: Gray
authors.affiliation: College of Engineering, Mathematics and Physical Science, University of Exeter, Exeter, EX4 4QF, UK.
authors.forename: Peter
authors.initials: P
authors.lastname: Challenor
authors.affiliation: Insigneo institute for in-silico medicine and Department of Computer Science, University of Sheffield, Regent Court, Sheffield, S1 4DP, UK.
authors.forename: Richard H
authors.identifier: 0000-0002-8438-7518
authors.initials: RH
authors.lastname: Clayton
mesh_terms: D006321:Heart / Q000502:physiology*; D006801:Humans; D008955:Models, Cardiovascular; D035501:Uncertainty