Predicting 1-year in-stent restenosis in superficial femoral arteries through multiscale computational modelling

In-stent restenosis in superficial femoral arteries (SFAs) is a complex, multi-factorial and multiscale vascular adaptation process whose thorough understanding is still lacking. Multiscale computational agent-based modelling has recently emerged as a promising approach to decipher mechanobiological mechanisms driving the arterial response to the endovascular intervention. However, the long-term arterial response has never been investigated with this approach, although being of fundamental relevance. In this context, this study investigates the 1-year post-operative arterial wall remodelling in three patient-specific stented SFA lesions through a fully coupled multiscale agent-based modelling framework. The framework integrates the effects of local haemodynamics and monocyte gene expression data on cellular dynamics through a bi-directional coupling of computational fluid dynamics simulations with an agent-based model of cellular activities. The framework was calibrated on the follow-up data at 1 month and 6 months of one stented SFA lesion and then applied to the other two lesions. The calibrated framework successfully captured (i) the high lumen area reduction occurring within the first post-operative month and (ii) the stabilization of the median lumen area from 1-month to 1-year follow-ups in all the stented lesions, demonstrating the potentialities of the proposed approach for investigating patient-specific short- and long-term responses to endovascular interventions.

Automated summary

This study investigates the long-term arterial wall remodelling one year post-operation in stented superficial femoral arteries (SFAs) using multiscale computational agent-based modelling. The model successfully captures the initial reduction in lumen area and subsequent stabilization of lumen area, demonstrating the potential of this approach for studying patient-specific responses to endovascular interventions.