ObjectivesTo identify hemodynamic risk factors for intracranial aneurysm rupture and establish a predictive model to aid evaluation. MethodsWe analyzed the hemodynamic parameters of 91 pairs of ruptured mirror aneurysms. A conditional univariate analysis was used for the continuous variables. A conditional multivariate logistic regression analysis was performed to identify the independent risk factors. Differences where p < 0.05 were statistically significant. A predictive model was established based on independent risk factors. Odds ratios (ORs) were used to score points. The validation cohort consisted of 189 aneurysms. Receiver operating characteristic curves were generated to determine the cutoff values and area under the curves (AUCs) of the predictive model and independent risk factors. ResultsThe conditional multivariate logistic analysis showed that the low shear area (LSA) (OR = 70.322, p = 0.044, CI = 1.112-4,445.256), mean combined hemodynamic parameter (CHP) (>0.087) (OR = 3.171, p = 0.034, CI = 1.089-9.236), and wall shear stress gradient (WSSG) ratio (>893.180) (OR = 5.740, p = 0.003, CI = 1.950-16.898) were independent risk factors. A prediction model was established: 23*LSA + 1*CHP mean (>0.087: yes = 1, no = 0) + 2 * WSSG ratio (>893.180: yes = 1, no = 0). The AUC values of the predictive model, LSA, mean CHP (>0.087), and WSSG ratio (>893.180) were 0.748, 0.700, 0.654, and 0.703, respectively. The predictive model and LSA cutoff values were 1.283 and 0.016, respectively. In the validation cohort, the predictive model, LSA, CHP (>0.087), and WSSG ratio (>893.180) were 0.736, 0.702, 0.689, and 0.706, respectively. ConclusionsLSA, CHP (>0.087), and WSSG ratio (>893.180) were independent risk factors for aneurysm rupture. Our predictive model could aid practical evaluation.