Computational simulation of platelet interactions in the initiation of stent thrombosis due to stent malapposition

Abstract

Coronary stenting is one of the most commonly used approaches to open coronary arteries blocked due to atherosclerosis. Stent malapposition can induce thrombosis but the microscopic process is poorly understood. The objective of this study was to determine the platelet-level process by which different extents of stent malapposition affect the initiation of stent thrombosis. We utilized a discrete element model to computationally simulate the transport, adhesion, and activation of thousands of individual platelets and red blood cells during thrombus initiation in stented coronary arteries. Simulated arteries contained a malapposed stent with a specified gap distance (0, 10, 25, 50, or 200 μm) between the struts and endothelium. Platelet-level details of thrombus formation near the proximal-most strut were measured during the simulations. The relationship between gap distance and amount of thrombus in the artery varied depending on different conditions (e.g., amount of dysfunctional endothelium, shear-induced activation of platelets, and thrombogenicity of the strut). Without considering shear-induced platelet activation, the largest gap distance (200 μm) produced no recirculation and less thrombus than the smallest two gap distances (0 and 10 μm) that created recirculation downstream of the strut. However, with the occurrence of shear-induced platelet activation, the largest gap distance produced more thrombus than the two smallest gap distances, but less thrombus than an intermediate gap distance (25 μm). A large gap distance was not necessarily the most thrombogenic, in contrast to implications of some computational fluid dynamics studies. The severity of stent malapposition affected initial stent thrombosis differently depending on various factors related to fluid recirculation, platelet trajectories, shear stress, and endothelial condition.

LVAD Outflow Graft Angle and Thrombosis Risk

“…Shear-induced-platelet-activation 12,36–40 has been the focus of many studies in evaluating thrombogenesis of medical devices 23,42 . In this study, we have quantified levels of shear stress histories on individual platelets to evaluate the relative thrombogenicity of LVAD outflow graft configurations.…”

“…While many factors influence platelet activation, one of the most widely accepted theories is shear-induced-platelet-activation (SIPA) 12,36–40 . Lagrangian tracking allows for determination of accumulated shear stress on each platelet, as a function of time in the flow, to evaluate the level of SIPA associated with each LVAD outflow graft angle studied: $$\mathrm{bold-italicS}\mathrm{bold-italicH}={\int }_{t_0}^t\tau false(\mathrm{bold-italicX}false(t\prime false),t\prime false)dt\prime$$ Where τ is the instantaneous shear stress magnitude at time t ′ and X ( t ′) is the platelet’s location at that time.…”

Intermittent Aortic Valve Opening and Risk of Thrombosis in Ventricular Assist Device Patients

“…Hemodynamic shear stress exacerbates thrombogenicity via shear-induced-platelet-activation (SIPA) and platelet microparticle formation 16,18–20 . Intermittent AV opening has a profound effect on platelet SH: levels of shear stress history, quantified by tracking individual platelets, are markedly higher with a closed AV and, more importantly, the probability of platelet exposure to outlier levels of elevated SH are reduced by up to 90% with intermittent AV opening.…”

Modeling thrombus formation and growth

“…Approaches that model blood as a continuum include solving advection diffusion reaction (ADR) equations (Bouchnita et al, 2017; Fogelson, ; Goodman et al, ; Hosseinzadegan and Tafti, ; Sorensen et al, ), the Leveque model (Bark and Ku, ), the Richardson's theory (Alenitsyn et al, ), Bark's correlation (Bark and Ku, ; Mehrabadi et al, ), diffusion with free‐escape boundary (DFEB) method (Mehrabadi et al, ), and finite time Lyapunov exponent (FTLE) measure (Shadden and Hendabadi, ). Discrete and multiscale models include force coupling (Pivkin et al, ), coarse‐grained theory (Narsimhan et al, ), discrete element method (Chesnutt and Han, , ), DPD and hybrid DPD‐PDE (Filipovic et al, ; Tosenberger et al, ), FSI (Vahidi and Fatouraee, ), cellular Potts (Xu et al, , ), Lattice Boltzmann method (LBM) and hybrid Monte Carlo‐Lattice Boltzmann (Crowl and Fogelson, ; Flamm et al, ), immersed boundary method (IBM) and LBM‐IBM (Crowl and Fogelson, ; Fogelson and Guy, ; Fogelson et al, ), LDH‐based models (Sheriff et al, ; Soares et al, ), moving particle semi‐implicit (MPS) (Kamada et al, , ), RBC membrane model (Reasor Jr et al, ), and Voigt model (Mori et al, ). Most of these models are validated using in vitro data, whereas many in vivo observations are not reflected under in vitro conditions.…”