Prediction of Clinical Outcome at Discharge After Rupture of Anterior Communicating Artery Aneurysm Using the Random Forest Technique - Xia, Nengzhi; Chen, Jie; Zhan, Chenyi; Jia, Xiufen; Xiang, Yilan; Chen, Yongchun; Duan, Yuxia; Lan, Li; Lin, Boli; Chen, Chao; Zhao, Bing; Chen, Xiaoyu; et al. (Frontiers in Neurology, 2020)
Background Aneurysmal subarachnoid haemorrhage (SAH) is a devastating disease. Anterior communicating artery (ACoA) aneurysm is the most frequent location of intracranial aneurysms. The purpose of this study was to predict the clinical outcome at discharge after rupture of ACoA aneurysm using random forest machine learning technique. Methods A total of 607 patients with ruptured ACoA aneurysms were included in this study between December 2007 and January 2016. In addition to basic clinical variables, 12 aneurysm morphologic parameters were evaluated. A multivariate logistic regression analysis was performed to determine the independent predictors of poor outcome. Of the 607 patients, 485 patients were randomly selected for training and the remaining for internal testing. The random forest model was developed using the training dataset. Additional 202 patients from February 2016 to December 2017 were collected for externally validating the random forest model. Results Patients' age (odds ratio [OR] =1.04), ventilated breathing status (OR=4.23), Federation of Neurological Surgeons (WFNS) grade (OR=2.13), and Fisher grade (OR=1.50) were significantly associated with poor outcome. None of the investigated morphological parameters of ACoA aneurysm were independent predictors of poor outcome. The developed random forest model achieved overall accuracies of 82.0% for internal test and 81.2% for external test. The areas under receiver operating characteristic (ROC) curve of the random forest model were 0.90 for internal test and 0.84 for external test. Conclusions The random forest model presents good performance in predicting the outcome after rupture of ACoA aneurysms, which may aid in clinical decision-making.