Linking aneurysmal geometry and hemodynamics using computational fluid dynamics

The development of Abdominal Aortic Aneurysms (AAA) is related to complex flow patterns and wall-shear–driven mechanobiological stimuli, yet the quantitative relationship between aneurysmal geometry and hemodynamics remains poorly defined. In this study, we conduct a comprehensive hemodynamic analysis of 74 patient-specific abdominal aortas. A multiscale framework coupling zero-dimensional to one-dimensional (0D–1D) systemic circulation models with three-dimensional (3D) stabilized finite-element simulations is used to generate physiologically consistent boundary conditions and high-fidelity flow fields. From each model, we extract the Time Averaged Wall Shear Stress (TAWSS), Oscillatory Shear Index, Relative Residence Time (RRT), and Local Normalized Helicity indicators alongside an extended set of geometric descriptors characterizing diameter, curvature, and torsion. Our results reveal distinct and statistically significant geometry–hemodynamics relationships across the cohort, separately for the aneurysmal sac and the iliac regions. Large aneurysmal sacs exhibit elevated RRT values, enlarged recirculating zones, and reduced TAWSS. Surprisingly, the iliac arteries emerge as dominant contributors to disturbed hemodynamics, showing stronger geometric–hemodynamic correlations than the infrarenal aorta. These observations highlight previously underappreciated downstream effects of AAA morphology. This study provides a comprehensive view of how aneurysm shape influences blood-flow behavior, supported by one of the largest systematically analyzed Computational Fluid Dynamics datasets of AAAs to date. Our results show that specific geometric features reliably shape shear-stress patterns, suggesting that these geometry-driven flow signatures could serve as valuable biomarkers for patient-specific risk assessment. Together, these insights highlight the potential of incorporating detailed geometric descriptors into future models that aim to predict AAA growth and rupture.