Abstract The integration of biomechanical and morphological analyses holds tremendous potential for assessing the rupture risk of abdominal
Abstract The integration of biomechanical and morphological analyses holds tremendous potential for assessing the rupture risk of abdominal aortic aneurysms (AAA). We employed a one-way fluid-structure interaction (FSI) model to distinguish between ruptured AAA (RAAA) and asymptomatic intact AAA (IAAA), focusing on morphological and computational fluid dynamics (CFD) indices. Patient groups with ruptured RAAA and asymptomatic IAAA were matched by diameter, age, and sex. AAA morphology was analyzed via CT segmentation, and biomechanical indices—including wall shear stress (WSS), peak wall stress (PWS), maximum deformation (MD), and other indices—were determined using FSI analysis. Statistical comparisons were performed using paired t-tests or Wilcoxon rank sum tests. Multivariate and LASSO regression analyses identified predictive factors, and a nomogram was developed. Model accuracy was assessed using the area under the curve (AUC). In our study with 66 RAAA and 66 asymptomatic IAAA patients, the tortuosity of the RAAAs was 1.4 times that of the asymptomatic IAAAs (P = 0.0005). The PWS, MD and peak wall rupture index (PWRI) of the RAAAs was 1.18, 1.32 and 1.27 times that of the asymptomatic IAAAs (P = 0.0158, 0.0036, 0.0071). The MD position demonstrated high consistency with RAAA rupture locations (94.12%). Four variables were selected for a nomogram, predicting AAA rupture with an AUC of 0.7604 (95% CI 0.6653–0.8556) and an internal validation AUC of 0.8051 (95% CI 0.6400–0.9703). In this study, we demonstrated that the location of MD is valuable for predicting the rupture location of AAA. We constructed a nomogram incorporating four key predictors—aortic neck length (ANL), intraluminal thrombus volume relative to AAA volume (VILT/VAAA), tortuosity, and MD—that enhances the prediction of AAA rupture risk, offering a more personalized assessment beyond traditional diameter-based methods.
