Predicting who has delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage using machine learning approach: a multicenter, retrospective cohort study

作者全名：Ge, Sihan; Chen, Junxin; Wang, Wei; Zhang, Li-bo; Teng, Yue; Yang, Cheng; Wang, Hao; Tao, Yihao; Chen, Zhi; Li, Ronghao; Niu, Yin; Zuo, Chenghai; Tan, Liang

作者地址：[Ge, Sihan] Northeastern Univ, Coll Med & Biol Informat Engn, Shenyang, Peoples R China; [Chen, Junxin] Dalian Univ Technol, Sch Software, Dalian, Peoples R China; [Wang, Wei] Shenzhen MSU BIT Univ, Artificial Intelligence Res Inst, Guangdong Hong Kong Macao Joint Lab Emot Intellige, Shenzhen, Peoples R China; [Zhang, Li-bo] Gen Hosp Northern Theater Chinese Peoples Liberat, Dept Radiol, Shenyang, Peoples R China; [Teng, Yue] Gen Hosp Northern Theater Chinese Peoples Liberat, Emergency Dept, Shenyang, Peoples R China; [Yang, Cheng; Chen, Zhi; Niu, Yin; Zuo, Chenghai] Army Med Univ Third Mil Med Univ, Southwest Hosp, Dept Neurosurg, Chongqing, Peoples R China; [Wang, Hao] Army Med Univ Third Mil Med Univ, Dept Neurosurg, Dept Pathol, Chongqing, Peoples R China; [Tao, Yihao] Chongqing Med Univ, Affiliated Hosp 2, Dept Neurosurg, Chongqing, Peoples R China; [Li, Ronghao] Army Med Univ, Dept Basic Med, Chongqing, Peoples R China; [Tan, Liang] Army Med Univ Third Mil Med Univ, Southwest Hosp, Dept Crit Care Med, Chongqing, Peoples R China; [Wang, Wei] Beijing Inst Technol, Sch Med Technol, Beijing, Peoples R China

通信作者：Niu, Y; Zuo, CH (通讯作者)，Army Med Univ Third Mil Med Univ, Southwest Hosp, Dept Neurosurg, Chongqing, Peoples R China.; Tan, L (通讯作者)，Army Med Univ Third Mil Med Univ, Southwest Hosp, Dept Crit Care Med, Chongqing, Peoples R China.

来源：BMC NEUROLOGY

ESI学科分类：NEUROSCIENCE & BEHAVIOR

WOS号：WOS:001233369300002

JCR分区：Q3

影响因子：2.2

年份：2024

卷号：24

期号：1

开始页：　

结束页：　

文献类型：Article

关键词：Aneurysmal subarachnoid hemorrhage; Delayed cerebral ischemia; Machine learning; Prediction; Random forest

摘要：Background Early prediction of delayed cerebral ischemia (DCI) is critical to improving the prognosis of aneurysmal subarachnoid hemorrhage (aSAH). Machine learning (ML) algorithms can learn from intricate information unbiasedly and facilitate the early identification of clinical outcomes. This study aimed to construct and compare the ability of different ML models to predict DCI after aSAH. Then, we identified and analyzed the essential risk of DCI occurrence by preoperative clinical scores and postoperative laboratory test results.Methods This was a multicenter, retrospective cohort study. A total of 1039 post-operation patients with aSAH were finally included from three hospitals in China. The training group contained 919 patients, and the test group comprised 120 patients. We used five popular machine-learning algorithms to construct the models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, and f1 score were used to evaluate and compare the five models. Finally, we performed a Shapley Additive exPlanations analysis for the model with the best performance and significance analysis for each feature.Results A total of 239 patients with aSAH (23.003%) developed DCI after the operation. Our results showed that in the test cohort, Random Forest (RF) had an AUC of 0.79, which was better than other models. The five most important features for predicting DCI in the RF model were the admitted modified Rankin Scale, D-Dimer, intracranial parenchymal hematoma, neutrophil/lymphocyte ratio, and Fisher score. Interestingly, clamping or embolization for the aneurysm treatment was the fourth button-down risk factor in the ML model.Conclusions In this multicenter study, we compared five ML methods, among which RF performed the best in DCI prediction. In addition, the essential risks were identified to help clinicians monitor the patients at high risk for DCI more precisely and facilitate timely intervention.

基金机构：Science-Health Joint Medical Scientific Research Project of Chongqing

基金资助正文：Not applicable.