Longitudinal associations between hemodynamic surrogate measures and morphological progression in abdominal aortic aneurysm

Scritto il 22/05/2026
da MyeungHye Suh

Biomech Model Mechanobiol. 2026 May 22;25(3):48. doi: 10.1007/s10237-026-02079-1.

ABSTRACT

Abdominal aortic aneurysm (AAA) is often asymptomatic, making early detection challenging. As the aneurysm expands, it alters the hemodynamic forces exerted on the arterial wall, which in turn influence disease progression. This study investigated longitudinal association between geometric structure and hemodynamics, along with changes in maximum diameter, volume, total surface area, and aneurysm sac surface area, using a longitudinal dataset of 36 computed tomography angiography (CTA) scans collected from eight patients over multiple time points. Hemodynamic parameters-including time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), relative residence time (RRT), endothelial cell activation potential (ECAP), velocity shear rate, and pressure-were derived via computational fluid dynamics (CFD) simulations. These parameters were analyzed alongside geometric indices such as the asymmetry index, saccular index, tortuosity index, and deformation diameter ratio. To account for the longitudinal nature of the data and inter-patient heterogeneity, a linear mixed model (LMM) was employed for the statistical analysis. Results revealed that across the AAA region, TAWSS and decreased significantly during aneurysm expansion, while ECAP and pressure showed strong positive associations with marginal values exceeding 0.9 (e.g., ECAP = 0.977; Pressure = 0.979). Geometric factors like the saccular index and deformation diameter ratio also strongly associated with aneurysm growth ( > 0.94), whereas the asymmetry index showed no significant relationship. These findings suggest that longitudinal associations exist between aneurysm morphology and CFD-derived hemodynamic surrogate measures, providing complementary descriptive information alongside conventional morphology-based assessment.

PMID:42171778 | DOI:10.1007/s10237-026-02079-1