The impact of bicuspid valve morphology on the selection of transcatheter aortic valve implantation devices: an study.

Aims: Bicuspid aortic valve (BAV) represents a challenge for transcatheter aortic valve implantation (TAVI). Few data are reported about the procedural implications of BAV using different self-expandable devices. The aim of this study is to investigate how BAV and tricuspid aortic valve (TAV) morphologies influence device selection and their impact on the potential development of post-operative conduction disturbances, using a novel approach.

Impact of thrombus composition on virtual thrombectomy procedures using human clot analogues mechanical data.

Endovascular thrombectomy (EVT) aims at restoring blood flow in case of acute ischemic stroke by removing the thrombus occluding a large cerebral artery. During the procedure with stent-retriever, the thrombus is captured within the device, which is then retrieved, subjecting the thrombus to several forces, potentially leading to its fragmentation. In silico studies, along with mechanical characterisation of thrombi, can enhance our understanding of the EVT, helping the development of new devices and interventional strategies.

Plaque heterogeneity influences in-stent restenosis following drug-eluting stent implantation: Insights from patient-specific multiscale modelling.

In-stent restenosis represents a major cause of failure of percutaneous coronary intervention with drug-eluting stent implantation. Computational multiscale models have recently emerged as powerful tools for investigating the mechanobiological mechanisms underlying vascular adaptation processes during in-stent restenosis. However, to date, the interplay between intervention-induced inflammation, drug delivery and drug retention has been under-investigated.

Predicting vulnerable coronary arteries: A combined radiomics-biomechanics approach.

Background And Objective: Nowadays, vulnerable coronary plaque detection from coronary computed tomography angiography (CCTA) is suboptimal, although being crucial for preventing major adverse cardiac events. Moreover, despite the suggestion of various vulnerability biomarkers, encompassing image and biomechanical factors, accurate patient stratification remains elusive, and a comprehensive approach integrating multiple markers is lacking. To this aim, this study introduces an innovative approach for assessing vulnerable coronary arteries and patients by integrating radiomics and biomechanical markers through machine learning methods.

Validation evidence with experimental and clinical data to establish credibility of TAVI patient-specific simulations.

Purpose: The objective of this study is to validate a novel workflow for implementing patient-specific finite element (FE) simulations to virtually replicate the Transcatheter Aortic Valve Implantation (TAVI) procedure.

Methods: Seven patients undergoing TAVI were enrolled. Patient-specific anatomical models were reconstructed from pre-operative computed tomography (CT) scans and subsequentially discretized, considering the native aortic leaflets and calcifications.

Discussing on the Aortic Coverage in Type B Aortic Dissection Treatment: A Comprehensive Scoping Review.

Objective: The objective of this study is to investigate and address the question surrounding the determination of the optimal endograft length of coverage during TEVAR for type B aortic dissection (TBAD), with a particular emphasis on the distal landing zone (DLZ).

Data Sources: MEDLINE, Scopus, and Web of Science databases were used.

Methods: The PRISMA-ScR statement was followed.

Shear Stress Quantification in Tissue Engineering Bioreactor Heart Valves: A Computational Approach.

Tissue-engineered heart valves can grow, repair, and remodel after implantation, presenting a more favorable long-term solution compared to mechanical and porcine valves. Achieving functional engineered valve tissue requires the maturation of human cells seeded onto valve scaffolds under favorable growth conditions in bioreactors. The mechanical stress and strain on developing valve tissue caused by different pressure and flow conditions in bioreactors are currently unknown.

Utilizing numerical simulations to prevent stent graft kinking during thoracic endovascular aortic repair.

Numerical simulations of thoracic endovascular aortic repair (TEVAR) may be implemented in the preoperative workflow if credible and reliable. We present the application of a TEVAR simulation methodology to an 82-year-old woman with a penetrating atherosclerotic ulcer in the left hemiarch, that underwent a left common carotid artery to left subclavian artery bypass and consequent TEVAR in zone 2. During the intervention, kinking of the distal thoracic stent graft occurred and the simulation was able to reproduce this event.

Investigating the effect of drug release on in-stent restenosis: A hybrid continuum - agent-based modelling approach.

Background And Objective: In-stent restenosis (ISR) following percutaneous coronary intervention with drug-eluting stent (DES) implantation remains an unresolved issue, with ISR rates up to 10%. The use of antiproliferative drugs on DESs has significantly reduced ISR. However, a complete knowledge of the mechanobiological processes underlying ISR is still lacking.

Thoracic Stent Graft Numerical Models To Virtually Simulate Thoracic Endovascular Aortic Repair: A Scoping Review.

Objective: Pre-procedural planning of thoracic endovascular aortic repair (TEVAR) may implement computational adjuncts to predict technical and clinical outcomes. The aim of this scoping review was to explore the currently available TEVAR procedure and stent graft modelling options.

Data Sources: PubMed (MEDLINE), Scopus, and Web of Science were systematically searched (English language, up to 9 December 2022) for studies presenting a virtual thoracic stent graft model or TEVAR simulation.

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.

Applicability assessment for in-silico patient-specific TEVAR procedures.

Thoracic Endovascular Aortic Repair (TEVAR) is a minimally invasive technique to treat thoracic aorta pathologies and consists of placing a self-expandable stent-graft into the pathological region to restore the vessel lumen and recreate a more physiological condition. Exhaustive computational models, namely the finite element analysis, can be implemented to reproduce the clinical procedure. In this context, numerical models, if used for clinical applications, must be reliable and the simulation credibility should be proved to predict clinical procedure outcomes or to build in-silico clinical trials.

In silico thrombectomy trials for acute ischemic stroke.

Background And Objective: In silico trials aim to speed up the introduction of new devices in clinical practice by testing device design and performance in different patient scenarios and improving patient stratification for optimizing clinical trials. In this paper, we demonstrate an in silico trial framework for thrombectomy treatment of acute ischemic stroke and apply this framework to compare treatment outcomes in different subpopulations and with different thrombectomy stent-retriever devices. We employ a novel surrogate thrombectomy model to evaluate the thrombectomy success in the in silico trial.

A low dimensional surrogate model for a fast estimation of strain in the thrombus during a thrombectomy procedure.

Background: Intra-arterial thrombectomy is the main treatment for acute ischemic stroke due to large vessel occlusions and can consist in mechanically removing the thrombus with a stent-retriever. A cause of failure of the procedure is the fragmentation of the thrombus and formation of micro-emboli, difficult to remove. This work proposes a methodology for the creation of a low-dimensional surrogate model of the mechanical thrombectomy procedure, trained on realizations from high-fidelity simulations, able to estimate the evolution of the maximum first principal strain in the thrombus.

Combined stent-retriever and aspiration intra-arterial thrombectomy performance for fragmentable blood clots: A proof-of-concept computational study.

Mechanical thrombectomy (MT) treatment of acute ischemic stroke (AIS) patients typically involves use of stent retrievers or aspiration catheters alone or in combination. For in silico trials of AIS patients, it is crucial to incorporate the possibility of thrombus fragmentation during the intervention. This study focuses on two aspects of the thrombectomy simulation: i) Thrombus fragmentation on the basis of a failure model calibrated with experimental tests on clot analogs; ii) the combined stent-retriever and aspiration catheter MT procedure is modeled by adding both the proximal balloon guide catheter and the distal access catheter.

Self-expandable stent for thrombus removal modeling: Solid or beam finite elements?

Background: The performance of self-expandable stents is being increasingly studied by means of finite-element analysis. As for peripheral stents, transcatheter valves and stent-grafts, there are numerous computational studies for setting up a proper model, this information is missing for stent-retrievers used in the procedure of thrombus removal in cerebral arteries. It is well known that the selection of the appropriate finite-element dimensions (topology) and formulations (typology) is a fundamental step to set up accurate and reliable computational simulations.

Validation and Verification of High-Fidelity Simulations of Thoracic Stent-Graft Implantation.

Thoracic Endovascular Aortic Repair (TEVAR) is the preferred treatment option for thoracic aortic pathologies and consists of inserting a self-expandable stent-graft into the pathological region to restore the lumen. Computational models play a significant role in procedural planning and must be reliable. For this reason, in this work, high-fidelity Finite Element (FE) simulations are developed to model thoracic stent-grafts.

Multiscale agent-based modeling of restenosis after percutaneous transluminal angioplasty: Effects of tissue damage and hemodynamics on cellular activity.

Background: Restenosis following percutaneous transluminal angioplasty (PTA) in femoral arteries is a major cause of failure of the revascularization procedure. The arterial wall response to PTA is driven by multifactorial, multiscale processes, whose complete understanding is lacking. Multiscale agent-based modeling frameworks, simulating the network of mechanobiological events at cell-tissue scale, can contribute to decipher the pathological pathways of restenosis.

Patient-specific multi-scale design optimization of transcatheter aortic valve stents.

Background And Objective: Transcatheter aortic valve implantation (TAVI) has become the standard treatment for a wide range of patients with aortic stenosis. Although some of the TAVI post-operative complications are addressed in newer designs, other complications and lack of long-term and durability data on the performance of these prostheses are limiting this procedure from becoming the standard for heart valve replacements. The design optimization of these devices with the finite element and optimization techniques can help increase their performance quality and reduce the risk of malfunctioning.

A predictive multiscale model of in-stent restenosis in femoral arteries: linking haemodynamics and gene expression with an agent-based model of cellular dynamics.

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.