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Uncertainty and variability in computational and mathematical models of cardiac physiology
KEY POINTS: 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 formulat...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5134370/ https://www.ncbi.nlm.nih.gov/pubmed/26990229 http://dx.doi.org/10.1113/JP271671 |
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author | Mirams, Gary R. Pathmanathan, Pras Gray, Richard A. Challenor, Peter Clayton, Richard H. |
author_facet | Mirams, Gary R. Pathmanathan, Pras Gray, Richard A. Challenor, Peter Clayton, Richard H. |
author_sort | Mirams, Gary R. |
collection | PubMed |
description | KEY POINTS: 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. ABSTRACT: 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. [Image: see text] |
format | Online Article Text |
id | pubmed-5134370 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-51343702016-12-15 Uncertainty and variability in computational and mathematical models of cardiac physiology Mirams, Gary R. Pathmanathan, Pras Gray, Richard A. Challenor, Peter Clayton, Richard H. J Physiol Special section reviews: The Cardiac Physiome Project KEY POINTS: 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. ABSTRACT: 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. [Image: see text] John Wiley and Sons Inc. 2016-06-09 2016-12-01 /pmc/articles/PMC5134370/ /pubmed/26990229 http://dx.doi.org/10.1113/JP271671 Text en © 2016 The Authors. The Journal of Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Special section reviews: The Cardiac Physiome Project Mirams, Gary R. Pathmanathan, Pras Gray, Richard A. Challenor, Peter Clayton, Richard H. Uncertainty and variability in computational and mathematical models of cardiac physiology |
title | Uncertainty and variability in computational and mathematical models of cardiac physiology |
title_full | Uncertainty and variability in computational and mathematical models of cardiac physiology |
title_fullStr | Uncertainty and variability in computational and mathematical models of cardiac physiology |
title_full_unstemmed | Uncertainty and variability in computational and mathematical models of cardiac physiology |
title_short | Uncertainty and variability in computational and mathematical models of cardiac physiology |
title_sort | uncertainty and variability in computational and mathematical models of cardiac physiology |
topic | Special section reviews: The Cardiac Physiome Project |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5134370/ https://www.ncbi.nlm.nih.gov/pubmed/26990229 http://dx.doi.org/10.1113/JP271671 |
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