Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.
Laboratoire Interdisciplinaire des Sciences du Numérique, CNRS, Université Paris-Saclay, Orsay, France.
Int J Numer Method Biomed Eng. 2022 May;38(5):e3595. doi: 10.1002/cnm.3595. Epub 2022 Apr 5.
Mathematical models of thrombosis are currently used to study clinical scenarios of pathological thrombus formation. As these models become more complex to predict thrombus formation dynamics high computational cost must be alleviated and inherent uncertainties must be assessed. Evaluating model uncertainties allows to increase the confidence in model predictions and identify avenues of improvement for both thrombosis modeling and anti-platelet therapies. In this work, an uncertainty quantification analysis of a multi-constituent thrombosis model is performed considering a common assay for platelet function (PFA-100®). The analysis is facilitated thanks to time-evolving polynomial chaos expansions used as a parametric surrogate for the full thrombosis model considering two quantities of interest; namely, thrombus volume and occlusion percentage. The surrogate is thoroughly validated and provides a straightforward access to a global sensitivity analysis via computation of Sobol' coefficients. Six out of 15 parameters linked to thrombus consitution, vWF activity, and platelet adhesion dynamics were found to be most influential in the simulation variability considering only individual effects; while parameter interactions are highlighted when considering the total Sobol' indices. The influential parameters are related to thrombus constitution, vWF activity, and platelet to platelet adhesion dynamics. The surrogate model allowed to predict realistic PFA-100® closure times of 300,000 virtual cases that followed the trends observed in clinical data. The current methodology could be used including common anti-platelet therapies to identify scenarios that preserve the hematological balance.
目前，数学血栓模型被用于研究病理性血栓形成的临床情况。随着这些模型变得更加复杂，以预测血栓形成动力学，必须减轻高计算成本并评估内在不确定性。评估模型不确定性可以提高对模型预测的信心，并为血栓形成建模和抗血小板治疗确定改进途径。在这项工作中，考虑到血小板功能的常用测定法（PFA-100®），对多成分血栓形成模型进行了不确定性量化分析。得益于时间演变多项式混沌扩展的使用，该分析作为完整血栓形成模型的参数替代物，考虑了两个感兴趣的数量;即血栓体积和闭塞百分比。该替代物经过彻底验证，并通过计算 Sobol'系数提供了一种直接进行全局敏感性分析的方法。在仅考虑个体效应的情况下，有 15 个与血栓构成、vWF 活性和血小板黏附动力学相关的参数中有 6 个被发现对模拟变异性影响最大;而在考虑总 Sobol'指数时，则突出了参数相互作用。有影响力的参数与血栓构成、vWF 活性和血小板与血小板黏附动力学有关。该替代模型允许预测 300,000 个虚拟病例的真实 PFA-100®闭合时间，这些病例遵循临床数据中观察到的趋势。目前的方法可以用于包括常见抗血小板治疗在内的方法，以确定保持血液学平衡的情况。
