Computational modelling of magnesium stent mechanical performance in a remodelling artery: Effects of multiple remodelling stimuli

Abstract

Significant research has been conducted in the area of coronary stents/scaffolds made from resorbable metallic and polymeric biomaterials. These next‐generation bioabsorbable stents have the potential to completely revolutionise the treatment of coronary artery disease. The primary advantage of resorbable devices over permanent stents is their temporary presence which, from a theoretical point of view, means only a healed coronary artery will be left behind following degradation of the stent potentially eliminating long‐term clinical problems associated with permanent stents. The healing of the artery following coronary stent/scaffold implantation is crucial for the long‐term safety of these devices. Computational modelling can be used to evaluate the performance of complex stent devices in silico and assist in the design and development and understanding of the next‐generation resorbable stents. What is lacking in computational modelling literature is the representation of the active response of the arterial tissue in the weeks and months following stent implantation, ie, neointimal remodelling, in particular for the case of biodegradable stents. In this paper, a computational modelling framework is developed, which accounts for two major physiological stimuli responsible for neointimal remodelling and combined with a magnesium corrosion model that is capable of simulating localised pitting (realistic) stent corrosion. The framework is used to simulate different neointimal growth patterns and to explore the effects the neointimal remodelling has on the mechanical performance (scaffolding support) of the bioabsorbable magnesium stent.

Impact of Degradation and Material Crystallinity on the Mechanical Performance of a Bioresorbable Polymeric Stent

“…In-vivo animal and clinical studies designed to evaluate tissue response alongside BPS performance would aid enhancement of BPS degradation models. In computational studies of bioresorbable magnesium stents (BRMS), NIR significantly impacts on the mechanics of the stent, along with the predicted in-vivo degradation rates [48,52,65].…”

“…The present work allows for the investigation of vessel stresses and the predictions are in agreement with that reported in Qiu et al [32], which investigated the mechanical interactions between degrading BPS in a combined artery and plaque model. Vessel stresses during vessel implantation are linked to arterial remodelling [61,77] and inclusion of such processes in a computational framework (e.g., [65]), would allow for prediction of clinical outcomes. Endothelialisation and neointimal growth can be combined with the lumen loss predicted here to form an overall measure of vessel patency, which is often reported as a primary endpoint in clinical studies of BPS in-vivo performance [63].…”

Multiscale Modeling of Vascular Remodeling Induced by Wall Shear Stress

“…When WSS is close to 0 Pa, its damage index is the largest, which is 1. When WSS is between 0 and 1, its damage index is linearly distributed (Boland et al, 2019).…”

An intricate interplay between stent drug dose and release rate dictates arterial restenosis

“…These models tend to be agent-based, where a set of rules detail the growth of excess tissue [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. Alternatively, a handful of continuum models also exist that utilise partial differential equations (PDEs), motivated through consideration of the underlying physics and biology [37][38][39]. Moreover, there are constrained mixture models that investigate long-term restenosis evolution, simulating the significance of initial vascular injury and subsequent tissue growth within a constitutive framework describing arterial kinematics [40][41][42][43].…”