Key result
Data-driven uncertainty quantification revealed that parameter uncertainty, especially in L-type calcium current parameters, significantly impacts cardiac electrophysiological model predictions.
Why the study?
Previous uncertainty quantification for cardiac electrophysiology models was limited by considering only parameter subsets or assigning arbitrary variation rather than data-driven estimates of parameter uncertainty.
Data-driven uncertainty quantification reveals that physiological variability in model parameters significantly impacts cardiac electrophysiology model predictions, highlighting the need for robust uncertainty evaluation in clinical applications.
Requires uncertainty quantification in cardiac models; leaves open translation to human electrophysiological predictions.
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.
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Pathmanathan et al. (2020) studied Cardiac electrophysiology modeling. Data-driven uncertainty quantification vs. Arbitrary parameter variation was evaluated on Impact on predictions of action potential shape, action potential duration prolongation, and spiral wave dynamics. Data-driven uncertainty quantification revealed that parameter uncertainty, especially in L-type calcium current parameters, significantly impacts cardiac electrophysiological model predictions.
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