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

Thoracic endovascular aortic repair (TEVAR) has been accepted as a standard treatment option for complicated type B aortic dissection. Distal stent-graft-induced new entry (SINE) is recognised as one of the main post-TEVAR complications, which can lead to fatal prognosis. Previous retrospective cohort studies suggested that short stent-graft (SG) length (<165 mm) might correlate with increased risk of distal SINE. However, the influence of SG length on changes in local biomechanical conditions before and after TEVAR is unknown. In this paper, we aim to address this issue using a virtual SG deployment simulation model developed for application in type B aortic dissection. Our model incorporates detailed SG design and hyperelastic behaviour of the aortic wall. By making use of patient-specific geometry reconstructed from pre-TEVAR computed tomography angiography (CTA) scan, our model can predict post-TEVAR SG configuration and wall stress. Virtual SG deployment simulations were performed on a patient who underwent TEVAR with a short SG (158 mm in length), mimicking the actual clinical procedure. Further simulations were carried out on the same patient geometry but with different SG lengths (183 mm and 208 mm) in order to evaluate the effect of SG length on changes in local stress in the treated aorta. Comparisons of simulation results for different SG lengths showed the location of maximum stress varied with the SG length. With the short SG (deployed in the patient), the maximum von Mises stress of 238.9 kPa was found on the intimal flap at the distal landing zone where SINE was identified at 3-month follow-up. Increasing the SG length caused the maximum von Mises stress to move away from the distal landing zone where stress values were reduced by approximately 17% with the medium-length SG and by 60% with the long SG. This pilot study demonstrates the potential of using the virtual SG deployment model as a pre-surgical planning tool to help select the most appropriate SG length for individual patients.