Morphological and Hemodynamic Characteristics Associated With the Rupture of Multiple Intracranial Aneurysms - Tang, Xiaoyu; Zhou, Longjiang; Wen, Lili; Wu, Qi; Leng, Xiaochang; Xiang, Jianping; Zhang, Xin (Frontiers in Neurology, 2022)
Background and Purpose The objective of this study was to identify the morphological and hemodynamic factors associated with the rupture of multiple intracranial aneurysms regardless of patient-related factors and establish a statistical model for aneurysm rupture risk assessment. Methods The digital subtraction angiography (DSA) data of 104 mirror intracranial aneurysms in 52 consecutive patients were retrospectively analyzed in this study. 21 morphological parameters and hemodynamic parameters were calculated by 3-dimensional reconstruction and computational fluid dynamics (CFD) simulation. Significant differences (p < 0.05) between the two groups were subsequently tested with the multivariate logistic regression to identify the independent risk factors. A prediction model was established based on the independent risk factors. The receiver operating characteristics (ROCs) were generated to estimate the prediction performance. A cohort of patients with multiple intracranial aneurysms admitted in our institute from January 2021 to October 2021 was introduced to verify the value of the model. Results Significant differences between the ruptured and unruptured aneurysms were found in 15 out of 19 parameters. Bleb formation, neck width, and size ratio were independent factors in the multivariate logistic regression. A prediction model based on the three independent risk factors was established: Odds = −1.495 – 0.707 × (Neckwidth) + 3.061 × (Blebformation) + 2.1 × (SR) (bleb formation: Yes = 1, No = 0). The area under the curve (AUC) value of the model was 0.901. In the validation cohort, the prediction model showed satisfying performance in assessing multiple aneurysm rupture risk with a sensitivity of 100% and specificity of 88.46%. Conclusion Bleb formation, neck width, and size ratio were independently associated with aneurysm rupture status. The prediction model may help in identifying the aneurysm with high rupture risk.