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

BACKGROUND AND

OBJECTIVES

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.

METHODS

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).

RESULTS

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.

CONCLUSION

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.