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Abstract
Recent efforts to ensure the reliability of computational model-based predictions in healthcare, such as the ASME V&V40 Standard, emphasize the importance of uncertainty quantification (UQ) and sensitivity analysis (SA) when evaluating computational models. UQ involves empirically determining the uncertainty in model inputs—typically resulting from natural variability or measurement error—and then calculating the resultant uncertainty in model outputs. SA involves calculating how uncertainty in model outputs can be apportioned to input uncertainty. Rigorous comprehensive UQ/SA provides confidence that model-based decisions are robust to underlying uncertainties. However, comprehensive UQ/SA is not currently feasible for whole heart models, due to numerous factors including model complexity and difficulty in measuring variability in the many parameters. Here, we present a significant step to developing a framework to overcome these limitations. We: (i) developed a novel action potential (AP) model of moderate complexity (six currents, seven variables, 36 parameters); (ii) prescribed input variability for all parameters (not empirically derived); (iii) used a single “hyper-parameter” to study increasing levels of parameter uncertainty; (iv) performed UQ and SA for a range of model-derived quantities with physiological relevance; and (v) present quantitative and qualitative ways to analyze different behaviors that occur under parameter uncertainty, including “model failure”. This is the first time uncertainty in every parameter (including conductances, steady-state parameters, and time constant parameters) of every ionic current in a cardiac model has been studied. This approach allowed us to demonstrate that, for this model, the simulated AP is fully robust to low levels of parameter uncertainty — to our knowledge the first time this has been shown of any cardiac model. A range of dynamics was observed at larger parameter uncertainty (e.g., oscillatory dynamics); analysis revealed that five parameters were highly influential in these dynamics. Overall, we demonstrate feasibility of performing comprehensive UQ/SA for cardiac cell models and demonstrate how to assess robustness and overcome model failure when performing cardiac UQ analyses. The approach presented here represents an important and significant step toward the development of model-based clinical tools which are demonstrably robust to all underlying uncertainties and therefore more reliable in safety-critical decision-making.
Highlights
Computational modeling and simulation (M&S) is a powerful tool for medical product design optimization, safety evaluation, clinical trial reduction, and enabling precision medicine (Viceconti et al, 2016; Faris and Shuren, 2017; Morrison et al, 2018)
We can observe that the model reproduces canine action potential shape including spike-and-dome-morphology, and current traces take physiologically realistic shapes and magnitudes
We have presented a novel cardiac cell model which has relatively few (36) parameters, which enabled us to perform Uncertainty quantification (UQ) and global sensitivity analysis (GSA) accounting for uncertainty in all conductance and kinetic parameters, albeit with prescribed input distributions
Summary
Computational modeling and simulation (M&S) is a powerful tool for medical product design optimization, safety evaluation, clinical trial reduction, and enabling precision medicine (Viceconti et al, 2016; Faris and Shuren, 2017; Morrison et al, 2018). The Comprehensive in vitro Proarrhythmia Assay (CiPA) program proposes to replace the long QT study based paradigm for assessing cardiotoxicity of novel compounds with a series of in vitro and in silico tests, one of which uses simulation of drug effects on the action potential using a cardiac cellular model (Li et al, 2018; Strauss et al, 2018) Another notable recent breakthrough is research demonstrating the clinical predictive capability of personalized whole-heart models in patient stratification (Arevalo et al, 2016) and other clinical cardiology applications (Ashikaga et al, 2013). There has been growing interest and research into cardiac model credibility (Niederer et al, 2011; Pathmanathan and Gray, 2013, 2018; Krishnamoorthi et al, 2014; Mirams et al, 2016)
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