Key result
Patient-specific computational modeling and machine learning characterize adverse wall shear stress imposed by coronary stenting.
Why the study?
Drug-eluting stent success is limited by restenosis and late stent thrombosis, with mechanical stimuli like computationally simulated wall shear stress implicated in these outcomes.
This review highlights advancements in computational modeling and machine learning to assess patient-specific wall shear stress alterations following coronary stenting, which may help understand restenosis and stent thrombosis.
May inform personalized stenting strategies; leaves open prospective outcome validation before clinical adoption.
The success of drug-eluting stents (DES) is limited by restenosis and, to a lesser extent, late stent thrombosis. Mechanical stimuli have been implicated in these outcomes, with indices of wall shear stress (WSS) determined from computational simulations being reported most frequently. The current work summarizes state-of-the-art computational approaches applicable to patient-specific models aimed at further understanding changes in WSS indexes imposed by stent implantation. We begin with a review of best practices involved in the process and then summarize the literature related to stent-induced WSS alterations. Image-based reconstruction methods are also discussed, along with the latest generation boundary conditions that replicate cardiac physiology and downstream vasculature in the setting of coronary artery disease. The influence of existing material property data on WSS results obtained with geometries reconstructed from finite element modeling and fluid structure interaction (FSI) simulations is reviewed, along with the novel approaches being used to provide coronary artery plaque data that are currently missing from the literature. We also consider the use of machine learning tools that have the potential for impact when assessing the role of adverse stent-induced WSS in suboptimal clinical outcomes. We conclude by focusing on challenging cases that involve DES implantation, which may benefit from recent advancements in patient-specific computational modeling.
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LaDisa et al. (2022) conducted a review in Coronary artery disease. Computational modeling of wall shear stress was evaluated. Patient-specific computational modeling and machine learning tools offer advanced approaches to characterize adverse wall shear stress imposed by coronary artery stenting.
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