Risk Factors For Posterior Communicating Artery Aneurysms Rupture And Building Of The Predictive Regression Models

Part I:Analysis of Risk Fators for Posterior Communicating Artery Aneurysms Rupture and Primary Building of Regression ModelsObjective:Morphology and hemodynamics are believed to play an important role in rupture of intracranial aneurysms. The conflicting findings of previous morphological and hemodynamic studies on intracranial aneurysm rupture may be caused by the relatively small sizes and the different locations of the patient-specific aneurysm models. We aimed to determine the independent risk factors of aneurysm rupture by focusing on only posterior communicating artery (PCoA) aneurysms and build regression models for rupture risk assessment of PCoA aneurysms.Materials and Methods:In 129 PCoA aneurysms (85 ruptured,44 unruptured), clinical, morphological and hemodynamic characteristics were compared between the ruptured and unruptured groups. Multivariate logistic regression analysis was performed to determine the independent predictors for the rupture status of PCoA aneurysms and build logistic regression models for assessment of rupture risk.Results:Univariate analyses showed that the size of aneurysm dome, aspect ratio (AR), size ratio (SR), dome-to-neck ratio (DN), inflow angle (IA), normalized wall shear stress (NWSS) and percentage of low WSS area (LSA) were significant parameters (P<0.05). Multivariate logistic regression analysis demonstrated size and IA to be the independently significant factor in the morphology model (AUC,0.75; 95% CI,0.66 to 0.84), whereas NWSS was the only independently significant variables in the hemodynamics model (AUC,0.65; 95% CI,0.55 to 0.75). The combined model retained 2 variables, IA and LSA (AUC,0.76; 95% CI,0.67 to 0.85).Conclusions:Hemodynamics and morphology were important in discriminating rupture status of intracranial aneurysm. Among the 3 models-morphological (based on size and IA), hemodynamic (based on NWSS), and combined (based on IA and LSA), the combined one discriminate intracranial aneurysm rupture status with relatively higher AUC values.Part â…¡:Verification of the Logistic Regression Models for Rupture Risk Assessment of Posterior Communicating Artery AneurysmsObjective:We have previously developed three logistic regression models for discriminating intracranial aneurysm rupture status from 129 posterior communicating artery aneurysms based on hemodynamic-morphological parameters. In this part, we exploit their use as a tool for predicting the risk of rupture of a new cohort of PCoA aneurysms.Materials and Methods:We collected three-dimensional images of 28 consecutive aneurysms from January 2014 to June 2014, applied the 3 regression models and compared model performance at predicting rupture status against anecdotal metrics (aneurysm size>7mm, aspect ratio>1.6 and size ratio>2.0).Results:At a cutoff score of 50%, the sensitivity and specificity of the morphological model, hemodynamic model and combined model were (0.68,0.67), (0.68,0.78) and (0.79,0.78), respectively; at a cutoff score of 60%, the sensitivity and specificity were (0.68,0.67), (0.68,0.78), (0.74,0.78); at a cutoff score of 70%, the sensitivity and specificity were (0.63,0.78), (0.68,0.78), (0.68,0.78); at a cutoff score of 80%, the sensitivity and specificity were (0.52,1.00), (0.16,1.00), (0.52,0.78). Aneurysm size, aspect ratio and size ratio showed poor sensitivities (0.32 to 0.53).Conclusions:Previous regression models based on hemodynamic-morphological parameters are able to discriminate rupture in a new cohort in the same population. A higher probability of rupture is associated with larger inflow angle and higher percentage of low wall shear stress area. The models could potentially stratify rupture risk and assist in treatment decision-making for unruptured aneurysms.