Prediction of Clinical Outcome at Discharge After Rupture of Anterior Communicating Artery Aneurysm Using the Random Forest Technique

Nengzhi Xia; Jie Chen; Chenyi Zhan; Xiufen Jia; Yilan Xiang; Yongchun Chen; Yuxia Duan; Li Lan; Boli Lin; Chao Chen; Bing Zhao; Xiaoyu Chen; Yunjun Yang; Jinjin Liu

doi:10.3389/fneur.2020.538052

Prediction of Clinical Outcome at Discharge After Rupture of Anterior Communicating Artery Aneurysm Using the Random Forest Technique

Front Neurol. 2020 Oct 29:11:538052. doi: 10.3389/fneur.2020.538052. eCollection 2020.

Authors

Nengzhi Xia¹, Jie Chen¹, Chenyi Zhan¹, Xiufen Jia¹, Yilan Xiang¹, Yongchun Chen¹, Yuxia Duan¹, Li Lan², Boli Lin¹, Chao Chen¹, Bing Zhao³, Xiaoyu Chen¹, Yunjun Yang¹, Jinjin Liu¹

Affiliations

¹ Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
² Department of Ultrasonography, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
³ Department of Neurosurgery, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.

Abstract

Background: Aneurysmal subarachnoid hemorrhage (SAH) is a devastating disease. Anterior communicating artery (ACoA) aneurysm is the most frequent location of intracranial aneurysms. The purpose of this study is to predict the clinical outcome at discharge after rupture of ACoA aneurysms using the 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 data set. An additional 202 patients from February 2016 to December 2017 were collected for externally validating the model. The prediction performance of the random forest model was compared with two radiologists. Results: Patients' age (odds ratio [OR] = 1.04), ventilated breathing status (OR = 4.23), World Federation of Neurosurgical Societies (WFNS) grade (OR = 2.13), and Fisher grade (OR = 1.50) are significantly associated with poor outcome. None of the investigated morphological parameters of ACoA aneurysm is an independent predictor of poor outcome. The developed random forest model achieves sensitivities of 78.3% for internal test and 73.8% for external test. The areas under receiver operating characteristic (ROC) curve of the random forest model were 0.90 for the internal test and 0.84 for the external test. Both sensitivities and areas under ROC curves of our model are superior to those of two raters in both internal and external tests. Conclusions: The random forest model presents good performance in predicting the outcome after rupture of ACoA aneurysms, which may aid in clinical decision making.

Keywords: aneurysms; outcome; prediction; random forest; subarachnoid hemorrhage.