Pathmanathan Pras, Cordeiro Jonathan M, Gray Richard A
Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, United States.
Masonic Medical Research Institute, Utica, NY, United States.
Front Physiol. 2019 Jun 26;10:721. doi: 10.3389/fphys.2019.00721. eCollection 2019.
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.
近期在医疗保健领域为确保基于计算模型的预测可靠性所做的努力，例如美国机械工程师协会（ASME）的V&V40标准，强调了在评估计算模型时不确定性量化（UQ）和敏感性分析（SA）的重要性。不确定性量化包括凭经验确定模型输入中的不确定性——通常源于自然变异性或测量误差——然后计算模型输出中的合成不确定性。敏感性分析涉及计算模型输出中的不确定性如何分配到输入不确定性中。严格全面的不确定性量化/敏感性分析为基于模型的决策对潜在不确定性具有稳健性提供了信心。然而，由于包括模型复杂性以及测量众多参数变异性的难度等众多因素，全面的不确定性量化/敏感性分析目前对于全心模型尚不可行。在此，我们朝着开发一个克服这些限制的框架迈出了重要一步。我们：（i）开发了一个中等复杂度的新型动作电位（AP）模型（六个电流、七个变量、36个参数）；（ii）规定了所有参数的输入变异性（非凭经验得出）；（iii）使用单个“超参数”来研究参数不确定性不断增加的水平；（iv）对一系列具有生理相关性的模型衍生量进行不确定性量化和敏感性分析；以及（v）提出定量和定性方法来分析在参数不确定性下出现的不同行为，包括“模型失效”。这是首次对心脏模型中每个离子电流的每个参数（包括电导、稳态参数和时间常数参数）的不确定性进行研究。这种方法使我们能够证明，对于该模型，模拟的动作电位对低水平的参数不确定性具有完全的稳健性——据我们所知，这是任何心脏模型首次被证明如此。在更大的参数不确定性下观察到了一系列动态变化（例如振荡动态）；分析表明五个参数在这些动态变化中具有高度影响力。总体而言，我们展示了对心脏细胞模型进行全面不确定性量化/敏感性分析的可行性，并展示了在进行心脏不确定性量化分析时如何评估稳健性以及克服模型失效。这里提出的方法代表了朝着开发基于模型的临床工具迈出的重要且关键的一步，这些工具对所有潜在不确定性都具有明显的稳健性，因此在安全关键决策中更可靠。
