Uncertainty quantification of a thrombosis model considering the clotting assay PFA‐100®

Abstract

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.

clotFoam: An open-source framework to simulate blood clot formation under arterial flow

“…Spatio-temporal continuum models that employ computational fluid dynamics (CFD) have gained widespread usage to study blood clotting in devices [ 10 ], in common microfluidic assays [ 11 , 12 ], in aneurysms [ 13 ] and bleeding [ 14 , 15 ], and to understand the effects of flow and transport on the clotting process overall [ 3 , 12 , 16 , 17 ]. A significant proportion of spatial-temporal models of clotting have been implemented using inhouse codes [ 3 , 15 , 16 ], commercial tools with high licensing costs [ 11 ], or are not publicly available [ 10 , 12 , 13 , 18 ], posing a challenge for researchers with limited resources. There is growing interest in open source and freely available software tools such as OpenFOAM [ 19 ].…”