摘要
Abstract
Objective To establish a predictive model for death within 7 days in patients with acute ischemic stroke undergoing endovascular treatment based on the machine learning method,and to explore key predictive factors.Methods A total of 293 patients with acute ischemic stroke who received endovascular treatment in the Department of Neurology,Shanxi People's Hospital,from January 2021 to June 2023 were enrolled.A total of 33 preoperative variables were collected,including demographics,disease history,and auxiliary examination results.The patients were divided into a training set and a validation set,and the random forest algorithm and the Extreme Gradient Boosting(XGBoost)algorithm were used to establish predictive models.The performance of the models was assessed based on accuracy,sensitivity,specificity,and the area under the ROC curve(AUC).Results In the training set,the random forest model showed the best performance in predicting 7-day mortality,with an AUC of 0.986,a sensitivity of 95.8%,and a specificity of 91.1%,with a better performance than the XGBoost model and the Logistic regression model.In the training set,the XGBoost model had better AUC and specificity than the random forest model in predicting 7-day mortality(AUC:0.908 vs 0.860;specificity:98.0%vs 97.9%),but with a poorer sensitivity than the random forest model(26.7%vs 66.7%).The key predictive factors in the random forest model and XGBoost model included National Institutes of Health Stroke Scale score,Glasgow coma score,and Alberta Stroke Program Early CT Score.Conclusions Machine learning-based models can effectively predict death within 7 days in patients with acute ischemic stroke,which provides a valuable tool for clinical decision-making.关键词
缺血性卒中/血管内治疗/病死率/机器学习/预测Key words
ischemic stroke/endovascular treatment/mortality/machine learning/prediction分类
医药卫生
