A patient-specific CTA-CFD framework deciphers hemodynamic heterogeneity after fenestrated TEVAR: a pilot study.

Despite the widespread use of fenestrated thoracic endovascular aortic repair (TEVAR), clinical outcomes exhibit considerable heterogeneity whose underlying hemodynamic mechanisms remain poorly understood. This study aimed to establish a patient-specific computational framework integrating postoperative computed tomography angiography (CTA) and computational fluid dynamics (CFD) to quantitatively evaluate morpho-hemodynamic alterations after fenestrated TEVAR. Six aortic dissection patients undergoing TEVAR with left subclavian artery (LSA) fenestration (three in situ, three in vitro) were included. Patient-specific 3D aortic geometries were reconstructed from postoperative CTA. High-fidelity CFD simulations were performed to analyze flow distribution, velocity, time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI). Computational analysis revealed that the brachiocephalic trunk was the least affected vessel following TEVAR (median ARBT 0.80, IQR 0.74-0.89). Notable abnormalities included one case of severe LSA stenosis (ARLSA = 5.25, VLSA, systole = 1.15 m/s), and one instance of mild stent-induced proximal LCCA compromise (ARLCCA = 1.27, VLCCA, systole = 1.06 m/s), both demonstrating TAWSS elevation at affected segments. Additionally, one patient exhibited inadequate endovascular recovery in the descending aorta, which received only 42.83% of the total cardiac output. The remaining patients showed no significant hemodynamic abnormalities. This pilot study establishes a patient-specific computational framework that integrates CTA with CFD to decipher post-intervention morpho-hemodynamic alterations. By quantitatively linking stent-induced geometric changes to adverse hemodynamic phenotypes, the demonstrated methodology explores a mechanistic approach for understanding post-surgical outcome disparities, thereby establishing a computational tool for postoperative evaluation. The clinical utility and predictive value of this tool, however, await validation in larger, prospective cohorts.