Effect of Vessel Tortuosity on Stress Concentration at the Distal Stent–Vessel Interface: Possible Link With New Entry Formation Through Biomechanical Simulation

Abstract

A computational approach is used to investigate potential risk factors for distal stent graft-induced new entry (dSINE) in aortic dissection (AD) patients. Patient-specific simulations were performed based on computed tomography images acquired from six AD patients (three dSINE and three non-dSINE) to analyze the correlation between anatomical characteristics and stress/strain distributions. Sensitivity analysis was carried out using idealized models to independently assess the effect of stent graft length, stent tortuosity and wedge apposition angle at the landing zone on key biomechanical variables. Mismatch of biomechanical properties between the stented and nonstented regions led to high stress at the distal stent graft-vessel interface in all patients, as well as shear strain in the neighbouring region, which coincides with the location of tear formation. Stress was observed to increase with the increase of stent tortuosity (from 263kPa at a tortuosity angle of 50o to 313 kPa at 30o). It was further amplified by stent graft landing at the inflection point of a curve. Malapposition of the stent graft led to an asymmetrical segment within the aorta, therefore changing the location and magnitude of the maximum von Mises stress substantially (up to +25.9% with a +25o change in the distal wedge apposition angle). In conclusion, stent tortuosity and wedge apposition angle serve as important risk predictors for dSINE formation in AD patients.

Patient-Specific Virtual Stent-Graft Deployment for Type B Aortic Dissection: A Pilot Study of the Impact of Stent-Graft Length

“…In this pilot study, we employed a FEM-based simulation method for SG deployment in a patient-specific type B aortic dissection reconstructed from pre-TEVAR CTA scan. Previous FEM-based studies attempted to investigate the role of dissection geometry and wall stress in the occurrence of SINE ( Menichini et al, 2018 ; Tan et al, 2021 ), but these studies relied on post-TEVAR CTA or magnetic resonance scans to reconstruct patient-specific geometry and assumed the SG as an additional wall layer without explicit description of the SG design. Other studies have investigated the solid-solid interaction between SG and aorta in type B aortic dissection by adopting virtual SG deployment approach, but the pre-stress conditions were ignored ( Ma et al, 2018 ; Meng et al, 2020 ).…”

“…In the most recent study reported by Tan et al, the effect of SG length on wall stress distribution was examined as part of a sensitivity analysis using an idealised geometric model ( Tan et al, 2021 ). It was found that the distal SG landing position was an important factor in determining the magnitude of maximum wall stress, where landing in a straight portion of the aorta had the lowest maximum von Mises stress.…”

Numerical simulation of the distal stent graft‐induced new entry after TEVAR

“…In this study, an interesting finding was that STI in non-dSINE patient was higher than that in dSINE patient, contrary to previous clinical research results. 6 However, there is literature 27 indicating that the aortic arch not only exhibits curvature in the coronal plane but also has a certain angle in the sagittal plane. The complex structure of the aorta might lead to limitations in using only STI to predict the degree of stent twist accurately.…”

“…SINE is defined as the occurrence of new intimal entries at both ends of the stent graft caused by the stent itself, 5 including proximal stent graft-induced new entry (pSINE) and distal stent graft-induced new entry (dSINE). 6 Previous research has reported a significantly higher incidence of dSINE compared to pSINE (81.0%-88.1% vs. 11.9%-19.0%). 7,8 Some scholars have studied the reasons for the occurrence of dSINE after TEVAR based on aortic morphological parameters.…”

Patient‐specific computational modeling of endovascular aneurysm repair: State of the art and future directions

“…Furthermore, computational modeling can be used to tailor intervention strategies, including personalized SG designs, to individual patients, significantly reducing complications. While computational models of EVAR still need to evolve to achieve this promise, great progress has been achieved to date (some current examples include 171–174 ). This manuscript will describe some current efforts at modeling EVAR using physics‐based computational approaches and how these models are used to plan for EVAR interventions and predicting outcomes.…”