Why the study?
As cardiac action potential models advance toward clinical decision-making, understanding the uncertainty hidden in parameter estimates from calibration to experimental data is critical.
Does reducing gating kinetic model complexity or calibrating to more complete experimental datasets reduce parameter uncertainty and unidentifiability in human atrial cell models?
Does reducing gating kinetic model complexity or calibrating to more complete experimental datasets reduce parameter uncertainty and unidentifiability in human atrial cell models?
Applying approximate Bayesian computation reveals substantial parameter unidentifiability in standard human atrial cell models and demonstrates that simpler standardized gating equations can improve fast sodium channel model fit, parameter identifiability, and computational speed.
Answers are grounded in this paper and cite their sources.
May improve INa model fit and identifiability in atrial simulations; hypothesis-generating, requires human validation before any modeling adoption.
Mathematical models of a cellular action potential (AP) in cardiac modelling have become increasingly complex, particularly in gating kinetics, which control the opening and closing of individual ion channel currents. As cardiac models advance towards use in personalized medicine to inform clinical decision-making, it is critical to understand the uncertainty hidden in parameter estimates from their calibration to experimental data. This study applies approximate Bayesian computation to re-calibrate the gating kinetics of four ion channels in two existing human atrial cell models to their original datasets, providing a measure of uncertainty and indication of potential issues with selecting a single unique value given the available experimental data. Two approaches are investigated to reduce the uncertainty present: re-calibrating the models to a more complete dataset and using a less complex formulation with fewer parameters to constrain. The re-calibrated models are inserted back into the full cell model to study the overall effect on the AP. The use of more complete datasets does not eliminate uncertainty present in parameter estimates. The less complex model, particularly for the fast sodium current, gave a better fit to experimental data alongside lower parameter uncertainty and improved computational speed. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.
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Houston et al. (2020) studied Human atrial cardiomyocyte action potential modelling. Standardized less-complex gating formulation (S model) and approximate Bayesian computation (ABC) re-calibration to unified datasets vs. Original Nygren (N) and Courtemanche (C) human atrial cell channel models and original calibration datasets was evaluated on Parameter uncertainty (relative standard deviation [RSD] of posterior distributions) and goodness-of-fit (converged ABC residual) for the fast sodium channel (INa) (p=0.02 vs N model; 0.04 vs C model).
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