A three-dimensional multiscale model for the prediction of thrombus growth under flow with single-platelet resolution

Abstract

Modeling thrombus growth in pathological flows allows evaluation of risk under patient-specific pharmacological, hematological, and hemodynamical conditions. We have developed a 3D multiscale framework for the prediction of thrombus growth under flow on a spatially resolved surface presenting collagen and tissue factor (TF). The multiscale framework is composed of four coupled modules: a Neural Network (NN) that accounts for platelet signaling, a Lattice Kinetic Monte Carlo (LKMC) simulation for tracking platelet positions, a Finite Volume Method (FVM) simulator for solving convection-diffusion-reaction equations describing agonist release and transport, and a Lattice Boltzmann (LB) flow solver for computing the blood flow field over the growing thrombus. A reduced model of the coagulation cascade was embedded into the framework to account for TF-driven thrombin production. The 3D model was first tested against in vitro microfluidics experiments of whole blood perfusion with various antiplatelet agents targeting COX-1, P2Y1, or the IP receptor. The model was able to accurately capture the evolution and morphology of the growing thrombus. Certain problems of 2D models for thrombus growth (artifactual dendritic growth) were naturally avoided with realistic trajectories of platelets in 3D flow. The generalizability of the 3D multiscale solver enabled simulations of important clinical situations, such as cylindrical blood vessels and acute flow narrowing (stenosis). Enhanced platelet-platelet bonding at pathologically high shear rates (e.g., von Willebrand factor unfolding) was required for accurately describing thrombus growth in stenotic flows. Overall, the approach allows consideration of patient-specific platelet signaling and vascular geometry for the prediction of thrombotic episodes.

Effects of cardiac function alterations on the risk of postoperative thrombotic complications in patients receiving endovascular aortic repair

“…By modeling the process of hemodynamics and mass transport in the coagulation cascade, the patient-specific patterns of formation and distribution of intraluminal/intra-prosthetic thrombus formation can be simulated or predicted with acceptable accuracy ( Menichini and Xu, 2016 ; Sun et al, 2021 ). Based on different thrombus initialization mechanisms, numerous computational models for thrombus formation have been developed in literature ( Fogelson, 1992 ; Wu et al, 2020 ; Zheng et al, 2020 ; Bouchnita et al, 2021 ; Grande Gutiérrez et al, 2021 ; Méndez Rojano et al, 2022 ; Shankar et al, 2022 ). However, the explicit effects of cardiac functions on thrombus formation were not yet well addressed.…”

Development of a parallel multiscale 3D model for thrombus growth under flow

“…To overcome this limitation, we developed a fully threedimensional multiscale model for platelet aggregation under flow and validated model predictions against experimental observations in prior work [9]. The model consists of four modules: a neural network (NN) model for platelet calcium signaling and activation, a lattice kinetic Monte Carlo (LKMC) module to track platelet motion and deposition on a growing clot mass under flow, a finite volume method (FVM) solver for computing agonist species concentration fields (ADP, thromboxane A 2 ) described by a convection-diffusionreaction equation, and a lattice Boltzmann (LB) method solver for tracking and updating the fluid velocity field as the clot grows (see Figure 1).…”

A fibrin enhanced thrombosis model for medical devices operating at low shear regimes or large surface areas

“…In contrast to device-related thrombosis modeling, a vast amount of work has been performed to understand fibrin formation dynamics due to a blood vessel injury. The modeling approaches range from kinetic pathway simulations of fibrin gel formation with ordinary differential equations [30][31][32] to complex gelation models that include blood flow interactions [33][34][35][36], as highlighted in a recent review by Nelson et al [37].…”