In-stent restenosis (ISR) is a maladaptive inflammatory-driven response of femoral arteries to percutaneous transluminal angioplasty and stent deployment, leading to lumen re-narrowing as consequence of excessive cellular proliferative and synthetic activities. A thorough understanding of the underlying mechanobiological factors contributing to ISR is still lacking. Computational multiscale models integrating both continuous- and agent-based approaches have been identified as promising tools to capture key aspects of the complex network of events encompassing molecular, cellular and tissue response to the intervention. In this regard, this work presents a multiscale framework integrating the effects of local haemodynamics and monocyte gene expression data on cellular dynamics to simulate ISR mechanobiological processes in a patient-specific model of stented superficial femoral artery. The framework is based on the coupling of computational fluid dynamics simulations (haemodynamics module) with an agent-based model (ABM) of cellular activities (tissue remodelling module). Sensitivity analysis and surrogate modelling combined with genetic algorithm optimization were adopted to explore the model behaviour and calibrate the ABM parameters. The proposed framework successfully described the patient lumen area reduction from baseline to one-month follow-up, demonstrating the potential capabilities of this approach in predicting the short-term arterial response to the endovascular procedure.

In-stent restenosis (ISR) represents a major drawback of stented superficial femoral arteries (SFAs). Motivated by the high incidence and limited knowledge of ISR onset and development in human SFAs, this study aims to (i) analyze the lumen remodeling trajectory over 1-year follow-up period in human stented SFAs and (ii) investigate the impact of altered hemodynamics on ISR initiation and progression. Ten SFA lesions were reconstructed at four follow-ups from computed tomography to quantify the lumen area change occurring within 1-year post-intervention. Patient-specific computational fluid dynamics simulations were performed at each follow-up to relate wall shear stress (WSS) based descriptors with lumen remodeling. The largest lumen remodeling was found in the first post-operative month, with slight regional-specific differences (larger inward remodeling in the fringe segments, p < 0.05). Focal re-narrowing frequently occurred after 6 months. Slight differences in the lumen area change emerged between long and short stents, and between segments upstream and downstream from stent overlapping portions, at specific time intervals. Abnormal patterns of multidirectional WSS were associated with lumen remodeling within 1-year post-intervention. This longitudinal study gave important insights into the dynamics of ISR and the impact of hemodynamics on ISR progression in human SFAs.

Human pathophysiology is occasionally too complex for unaided hypothetical-deductive reasoning and the isolated application of additive or linear statistical methods. Clustering algorithms use input data patterns and distributions to form groups of similar patients or diseases that share distinct properties. Although clinicians frequently perform tasks that may be enhanced by clustering, few receive formal training and clinician-centered literature in clustering is sparse. To add value to clinical care and research, optimal clustering practices require a thorough understanding of how to process and optimize data, select features, weigh strengths and weaknesses of different clustering methods, select the optimal clustering method, and apply clustering methods to solve problems. These concepts and our suggestions for implementing them are described in this narrative review of published literature. All clustering methods share the weakness of finding potential clusters even when natural clusters do not exist, underscoring the importance of applying data-driven techniques as well as clinical and statistical expertise to clustering analyses. When applied properly, patient and disease phenotype clustering can reveal obscured associations that can help clinicians understand disease pathophysiology, predict treatment response, and identify patients for clinical trial enrollment.

In-stent restenosis (ISR) is the major drawback of superficial femoral artery (SFA) stenting. Abnormal hemodynamics after stent implantation seems to promote the development of ISR. Accordingly, this study aims to investigate the impact of local hemodynamics on lumen remodeling in human stented SFA lesions. Ten SFA models were reconstructed at 1-week and 1-year follow-up from computed tomography images. Patient-specific computational fluid dynamics simulations were performed to relate the local hemodynamics at 1-week, expressed in terms of time-averaged wall shear stress (TAWSS), oscillatory shear index and relative residence time, with the lumen remodeling at 1-year, quantified as the change of lumen area between 1-week and 1-year. The TAWSS was negatively associated with the lumen area change (ρ = − 0.75, p = 0.013). The surface area exposed to low TAWSS was positively correlated with the lumen area change (ρ = 0.69, p = 0.026). No significant correlations were present between the other hemodynamic descriptors and lumen area change. The low TAWSS was the best predictive marker of lumen remodeling (positive predictive value of 44.8%). Moreover, stent length and overlapping were predictor of ISR at follow-up. Despite the limited number of analyzed lesions, the overall findings suggest an association between abnormal patterns of WSS after stenting and lumen remodeling.

Background Despite being the definitive treatment for lower extremity peripheral arterial disease, vein bypass grafts fail in half of all cases. Early repair mechanisms following implantation, governed largely by the immune environment, contribute significantly to long-term outcomes. The current study investigates the early response patterns of circulating monocytes as a determinant of graft outcome. Methods and Results In 48 patients undergoing infrainguinal vein bypass grafting, the transcriptomes of circulating monocytes were analyzed pre-operatively and at 1, 7 and 28 days post-operation. Dynamic clustering algorithms identified 50 independent gene response patterns. Three clusters (64 genes) were differentially expressed, with a hyperacute response pattern defining those patients with failed versus patent grafts twelve months post-operation. A second independent data set, comprised of 96 patients subjected to major trauma, confirmed the value of these 64 genes in predicting an uncomplicated versus complicated recovery. Causal network analysis identified eight upstream elements that regulate these mediator genes, and Bayesian analysis with a priori knowledge of the biological interactions were integrated to create a functional network describing the relationships among the regulatory elements and downstream mediator genes. Linear models predicted the removal of either Signal Transducer and Activator of Transcription 3 (STAT3) or Myeloid Differentiation Primary Response gene 88 (MYD88) to shift mediator gene expression levels towards those seen in successful grafts. Conclusions A novel combination of dynamic gene clustering, linear models and Bayesian network analysis has identified a core set of regulatory genes whose manipulations could migrate vein grafts towards a more favorable remodeling phenotype.