Data-Driven Uncertainty Quantification for Cardiac Electrophysiological Models: Impact of Physiological Variability on Action Potential and Spiral Wave Dynamics
Computational modeling of cardiac electrophysiology (EP) has recently transitioned from a scientific research tool to clinical applications. To ensure reliability of clinical or regulatory decisions made using cardiac EP models, it is vital to evaluate the uncertainty in model predictions. Model pre...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-11-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2020.585400/full |
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| author | Pras Pathmanathan Suran K. Galappaththige Jonathan M. Cordeiro Abouzar Kaboudian Flavio H. Fenton Richard A. Gray |
| author_facet | Pras Pathmanathan Suran K. Galappaththige Jonathan M. Cordeiro Abouzar Kaboudian Flavio H. Fenton Richard A. Gray |
| author_sort | Pras Pathmanathan |
| collection | DOAJ |
| description | Computational modeling of cardiac electrophysiology (EP) has recently transitioned from a scientific research tool to clinical applications. To ensure reliability of clinical or regulatory decisions made using cardiac EP models, it is vital to evaluate the uncertainty in model predictions. Model predictions are uncertain because there is typically substantial uncertainty in model input parameters, due to measurement error or natural variability. While there has been much recent uncertainty quantification (UQ) research for cardiac EP models, all previous work has been limited by either: (i) considering uncertainty in only a subset of the full set of parameters; and/or (ii) assigning arbitrary variation to parameters (e.g., ±10 or 50% around mean value) rather than basing the parameter uncertainty on experimental data. In our recent work we overcame the first limitation by performing UQ and sensitivity analysis using a novel canine action potential model, allowing all parameters to be uncertain, but with arbitrary variation. Here, we address the second limitation by extending our previous work to use data-driven estimates of parameter uncertainty. Overall, we estimated uncertainty due to population variability in all parameters in five currents active during repolarization: inward potassium rectifier, transient outward potassium, L-type calcium, rapidly and slowly activating delayed potassium rectifier; 25 parameters in total (all model parameters except fast sodium current parameters). A variety of methods was used to estimate the variability in these parameters. We then propagated the uncertainties through the model to determine their impact on predictions of action potential shape, action potential duration (APD) prolongation due to drug block, and spiral wave dynamics. Parameter uncertainty had a significant effect on model predictions, especially L-type calcium current parameters. Correlation between physiological parameters was determined to play a role in physiological realism of action potentials. Surprisingly, even model outputs that were relative differences, specifically drug-induced APD prolongation, were heavily impacted by the underlying uncertainty. This is the first data-driven end-to-end UQ analysis in cardiac EP accounting for uncertainty in the vast majority of parameters, including first in tissue, and demonstrates how future UQ could be used to ensure model-based decisions are robust to all underlying parameter uncertainties. |
| first_indexed | 2024-12-12T18:47:27Z |
| format | Article |
| id | doaj.art-8f33e51d36964cb197102e020c03ca3a |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-12-12T18:47:27Z |
| publishDate | 2020-11-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Physiology |
| spelling | doaj.art-8f33e51d36964cb197102e020c03ca3a2022-12-22T00:15:28ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2020-11-011110.3389/fphys.2020.585400585400Data-Driven Uncertainty Quantification for Cardiac Electrophysiological Models: Impact of Physiological Variability on Action Potential and Spiral Wave DynamicsPras Pathmanathan0Suran K. Galappaththige1Jonathan M. Cordeiro2Abouzar Kaboudian3Flavio H. Fenton4Richard A. Gray5U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, United StatesU.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, United StatesDepartment of Experimental Cardiology, Masonic Medical Research Institute, Utica, NY, United StatesSchool of Physics, Georgia Institute of Technology, Atlanta, GA, United StatesSchool of Physics, Georgia Institute of Technology, Atlanta, GA, United StatesU.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, United StatesComputational modeling of cardiac electrophysiology (EP) has recently transitioned from a scientific research tool to clinical applications. To ensure reliability of clinical or regulatory decisions made using cardiac EP models, it is vital to evaluate the uncertainty in model predictions. Model predictions are uncertain because there is typically substantial uncertainty in model input parameters, due to measurement error or natural variability. While there has been much recent uncertainty quantification (UQ) research for cardiac EP models, all previous work has been limited by either: (i) considering uncertainty in only a subset of the full set of parameters; and/or (ii) assigning arbitrary variation to parameters (e.g., ±10 or 50% around mean value) rather than basing the parameter uncertainty on experimental data. In our recent work we overcame the first limitation by performing UQ and sensitivity analysis using a novel canine action potential model, allowing all parameters to be uncertain, but with arbitrary variation. Here, we address the second limitation by extending our previous work to use data-driven estimates of parameter uncertainty. Overall, we estimated uncertainty due to population variability in all parameters in five currents active during repolarization: inward potassium rectifier, transient outward potassium, L-type calcium, rapidly and slowly activating delayed potassium rectifier; 25 parameters in total (all model parameters except fast sodium current parameters). A variety of methods was used to estimate the variability in these parameters. We then propagated the uncertainties through the model to determine their impact on predictions of action potential shape, action potential duration (APD) prolongation due to drug block, and spiral wave dynamics. Parameter uncertainty had a significant effect on model predictions, especially L-type calcium current parameters. Correlation between physiological parameters was determined to play a role in physiological realism of action potentials. Surprisingly, even model outputs that were relative differences, specifically drug-induced APD prolongation, were heavily impacted by the underlying uncertainty. This is the first data-driven end-to-end UQ analysis in cardiac EP accounting for uncertainty in the vast majority of parameters, including first in tissue, and demonstrates how future UQ could be used to ensure model-based decisions are robust to all underlying parameter uncertainties.https://www.frontiersin.org/articles/10.3389/fphys.2020.585400/fulluncertainty quantificationsensitivity analysisvariabilityelectrophysiologycorrelation |
| spellingShingle | Pras Pathmanathan Suran K. Galappaththige Jonathan M. Cordeiro Abouzar Kaboudian Flavio H. Fenton Richard A. Gray Data-Driven Uncertainty Quantification for Cardiac Electrophysiological Models: Impact of Physiological Variability on Action Potential and Spiral Wave Dynamics Frontiers in Physiology uncertainty quantification sensitivity analysis variability electrophysiology correlation |
| title | Data-Driven Uncertainty Quantification for Cardiac Electrophysiological Models: Impact of Physiological Variability on Action Potential and Spiral Wave Dynamics |
| title_full | Data-Driven Uncertainty Quantification for Cardiac Electrophysiological Models: Impact of Physiological Variability on Action Potential and Spiral Wave Dynamics |
| title_fullStr | Data-Driven Uncertainty Quantification for Cardiac Electrophysiological Models: Impact of Physiological Variability on Action Potential and Spiral Wave Dynamics |
| title_full_unstemmed | Data-Driven Uncertainty Quantification for Cardiac Electrophysiological Models: Impact of Physiological Variability on Action Potential and Spiral Wave Dynamics |
| title_short | Data-Driven Uncertainty Quantification for Cardiac Electrophysiological Models: Impact of Physiological Variability on Action Potential and Spiral Wave Dynamics |
| title_sort | data driven uncertainty quantification for cardiac electrophysiological models impact of physiological variability on action potential and spiral wave dynamics |
| topic | uncertainty quantification sensitivity analysis variability electrophysiology correlation |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2020.585400/full |
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