Nikishova Anna, Veen Lourens, Zun Pavel, Hoekstra Alfons G
Computational Science Lab, Institute for Informatics, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands.
Netherlands eScience Center, Amsterdam, The Netherlands.
Cardiovasc Eng Technol. 2018 Dec;9(4):761-774. doi: 10.1007/s13239-018-00372-4. Epub 2018 Aug 22.
Coronary artery stenosis, or abnormal narrowing, is a widespread and potentially fatal cardiac disease. After treatment by balloon angioplasty and stenting, restenosis may occur inside the stent due to excessive neointima formation. Simulations of in-stent restenosis can provide new insight into this process. However, uncertainties due to variability in patient-specific parameters must be taken into account.
We performed an uncertainty quantification (UQ) study on a complex two-dimensional in-stent restenosis model. We used a quasi-Monte Carlo method for UQ of the neointimal area, and the Sobol sensitivity analysis (SA) to estimate the proportions of aleatory and epistemic uncertainties and to determine the most important input parameters.
We observe approximately 30% uncertainty in the mean neointimal area as simulated by the model. Depending on whether a fast initial endothelium recovery occurs, the proportion of the model variance due to natural variability ranges from 15 to 35%. The endothelium regeneration time is identified as the most influential model parameter.
The model output contains a moderate quantity of uncertainty, and the model precision can be increased by obtaining a more certain value on the endothelium regeneration time. We conclude that the quasi-Monte Carlo UQ and the Sobol SA are reliable methods for estimating uncertainties in the response of complicated multiscale cardiovascular models.
冠状动脉狭窄，即异常变窄，是一种常见且可能致命的心脏疾病。在接受球囊血管成形术和支架置入治疗后，由于新生内膜过度形成，支架内可能会发生再狭窄。支架内再狭窄的模拟可以为这一过程提供新的见解。然而，必须考虑到患者特定参数变异性所带来的不确定性。
我们对一个复杂的二维支架内再狭窄模型进行了不确定性量化（UQ）研究。我们使用准蒙特卡罗方法对新生内膜面积进行不确定性量化，并使用索博尔灵敏度分析（SA）来估计偶然不确定性和认知不确定性的比例，并确定最重要的输入参数。
我们观察到模型模拟的平均新生内膜面积存在约30%的不确定性。根据初始内皮是否快速恢复，由自然变异性导致的模型方差比例在15%至35%之间。内皮再生时间被确定为最具影响力的模型参数。
模型输出包含一定量的不确定性，通过获得更确定的内皮再生时间值可以提高模型精度。我们得出结论，准蒙特卡罗不确定性量化和索博尔灵敏度分析是估计复杂多尺度心血管模型响应中不确定性的可靠方法。
