A Simple Scoring Model for Prediction of Rupture Risk of Anterior Communicating Artery Aneurysms - Wang, Guang-xian; Wang, Shuang; Liu, Lan-lan; Gong, Ming-fu; Zhang, Dong; Yang, Chun-yang; Wen, Li (Frontiers in Neurology, 2019)
Background: The rupture risk of anterior communicating artery aneurysms (ACoAAs) has been known to be higher than those of the other locations. Thus, the aim of this study was to investigate the clinical and morphological characteristics associated with risk factors for the rupture of ACoAAs. Methods: A total of 361 consecutive patients with 361 ACoAAs between August 2011 and December 2017 were retrospectively reviewed. Patients and ACoAAs were divided into rupture and unrupture groups. In addition to clinical characteristics, ACoAAs characteristics were evaluated by CT angiography (CTA). Multiple logistic regression analysis was used to identify the independent risk factors associated with ACoAAs rupture. The assigning score of these variables depends on the β coefficient. Receiver operating characteristic (ROC) curve analysis was used to calculate the optimal thresholds. Results: Multiple logistic regression model revealed that A1 dominant (odds ratio (OR) 3.034), irregular shape (OR 3.358) and aspect ratio (AR, OR 3.163) positively increased the risk of rupture, while cerebral atherosclerosis (OR 0.080) and mean diameters (OR 0.474) were negatively correlated with rupture. Incorporating these 5 factors, ROC analysis revealed that the threshold value of the multi-factors was 1, the sensitivity was 88.3%, and the specificity was 66.0%. Conclusions: The scoring model is a sample method which based on A1 dominant, irregular shape, aspect ratio, cerebral atherosclerosis and mean diameters on CTA is of great value in the prediction of rupture risk of ACoAAs.