Giulia Luraghi

Transcatheter aortic valve implantation (TAVI)-related post-operative complications remain significant clinical challenges, and current in-silico simulations fall short in predicting them accurately, limiting their clinical applicability. This scoping review evaluates the state of the art in TAVI computational modeling, identifying methodological gaps and proposing directions for refinement to enhance translational impact. Following PRISMA-ScR guidelines, 40 studies were included, with data extracted and summarized by evaluated outcomes. A quality assessment was performed using a 14-item rubric. Most studies focused on predicting paravalvular leak (65%) and conduction disturbances (20%). This review reveals substantial heterogeneity in modeling approaches, with limited standardization and varying degrees of validation. To improve clinical relevance, future efforts should prioritize model standardization, rigorous validation following ASME V&V guidelines, increased automation, and improved interpretability for clinical users. By ensuring robustness, efficiency, and clinical accessibility, in-silico models could transform TAVI outcome prediction and support personalized treatment planning, ultimately enhancing care standards in structural heart interventions.

Mechanical thrombectomy (MT) is an emergency treatment for acute ischemic stroke (AIS) to remove a clot occluding a large cerebral vessel. Histological analysis on retrieved thrombi have shown that they are mainly composed of red blood cells (RBCs), platelets and fibrin, and the outcome of MT appears to be influenced by clot composition. Therefore, being able to predict clot composition from routine medical images used for AIS diagnosis could support the choice of interventional strategy. Along with that, finite element simulations of the MT procedure can help provide insights into the impact of the procedural choices, the vessels morphology and the clot characteristics on the MT outcome. To achieve this, a realistic representation of the involved structures is necessary. In this context, this work aimed to (i) develop a methodology for the analysis of routine radiological images aiming at inferring information about clot characteristics (position, length, and composition) and (ii) develop a semi-automatic pipeline to position the clot in the patient-specific reconstructed geometry to build a patient-specific model which could be the starting point for the in silico replica of the MT procedure. However, image analysis alone could not distinguish between white and mixed clots, while a distinction between red and non-red clots was possible. Consequently, histological analyses were used to assign the clot composition, and thus the mechanical properties, in the positioning simulation. The resulting patient-specific model showed a strong similarity with pre-interventional clinical images.

Thoracic endovascular aortic repair (TEVAR) procedure is sometimes discouraged from clinical guidelines in the presence of calcifications and thrombus along the sealing zones. This computational study aims to understand which is the effect of calcification on stent graft displacement after TEVAR procedure, simulated in a patient-specific anatomy with a penetrating aortic ulcer (PAU). A patient-specific anatomy without calcification is taken as reference, and four models with idealized calcifications positioned in different regions and with different material properties are analyzed. Opening area, von Mises stresses and contact pressures are evaluated to provide a reliable comparison between the calcified (Ca) and the non-calcified models (noCa), and among the calcified models themselves. Comparing qualitatively the Ca and noCa models, no particular changes in the stent graft apposition are observed. In addition, in the Ca models the opening area results lower with respect to the noCa models, but no significant differences are observed among the Ca models. Regarding the von Mises stresses, it seems that the calcifications act as load-bearing structures, absorbing the stresses and reducing them on the aorta. Decreasing the Young modulus of the calcifications, this effect is reduced. Higher contact pressures are observed when the highest Young’s modulus of calcification is adopted, with all Ca models having greater pressures than the noCa model. From this analysis, the stent graft seems to be positioned correctly inside the aorta, even in the presence of calcifications. In this setting, the calcifications seem to reduce the stresses on the aorta, thus reducing the likelihood of aneurysm rupture.

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 in silico approach. Methods and results: Five patients with BAV undergoing TAVI were enrolled. TAVs were virtually modelled within each BAV patient-specific anatomy, resulting in 10 virtual patients. Acurate Neo2 and Evolut R implantations were subsequently simulated across all cases. Post-implantation stresses exerted on both the stent and aortic root were measured, allowing a comparative analysis of the impact of the two valve morphologies. Comparing stent stresses between BAV and TAV configurations, the stress gap increased by 21.96 ± 5.35% (P = 0.01) in Acurate Neo2 cases (n = 6) compared with Evolut R cases (n = 4). The analysis of aortic root stresses showed no significant differences between BAV (n = 5) and TAV (n = 5) configurations, with a mean stress difference of 5.1 ± 8.17% (P > 0.05). Conclusion: Our patient-specific model shows that high radial force devices, such as Evolut R, demonstrate consistent expansion regardless of valve morphology, without increasing the risk of post-implantation conduction disturbances, hence resulting more suitable for BAV cases. Incorporating this methodology into pre-operative planning could support clinicians in selecting the most suitable device with a patient-specific approach.