中国实用神经疾病杂志2024，Vol.27Issue(7)：802-808,7.DOI:10.12083/SYSJ.230641
基于eStroke国家溶取栓影像平台的随机森林模型预测醒后卒中预后的价值
Prognostic value of random forest model based on eStroke National Thrombolysis Imaging Platform for post-awakening stroke
摘要
Abstract
Objective To investigate the prognostic value of random forest model based on clinical data and eStroke National Thrombolysis Imaging Platform in predicting post-awakening stroke(WUS),in order to provide reference for early prognosis prediction and intervention planning.Methods A total of 285 patients with WUS in the Seventh Affiliated Hospital of Guangxi Medical University from January 2020 to January 2023 were selected as subjects.According to the modified Rankin scale(mRS)score 90 days after thromposectomy,the patients were classified into good prognosis group and poor prognosis group.The clinical data of the two groups and the quantitative data(volume of ischemic penumbra and volume of infarction core area)automatically fed back by eStroke National Thrombolysis Imaging Platform were collected.The random forest model and Lasso-Logistic regression model were constructed.The risk of poor prognosis in WUS patients predicted by random forest model was evaluated.Results There were significant differences in Hcy,WBC,DSA-CS score,rLMC score,NIHSS score,thrombolysis times,puncture to recirculation time,intracranial stenosis degree,atrial fibrillation,smoking history,intravenous thrombolysis,ischemic penumbral zone volume and infarct core area volume between the two groups at admission(P<0.05).Ischemic semidark zone volume,infarct core volume,Hcy level on admission,NIHSS score on admission,DSA-CS score on admission,degree of intracranial stenosis,intravenous thrombolysis,and atrial fibrillation as factors influencing poor prognosis in patients with WUS(P<0.05).There was no significant difference in AUC between Logistic regression model and random forest model in predicting the risk of adverse prognosis in WUS patients(0.891,95%CI:0.875-0.902 vs 0.900,95%CI:0.894-0.923).Conclusion The random forest model based on clinical data and eStroke National Thrombolysis Imaging Platform can be used to predict the early prognosis of WUS patients,and provide reference for clinical follow-up treatment,so as to improve the prognosis.关键词
醒后卒中/随机森林模型/eStroke国家溶取栓影像平台/临床资料/预后/预测价值Key words
Wake-up stroke/Random forest model/eStroke National Thrombolysis Imaging Platform/Clinical data/Prognosis/Predictive value分类
医药卫生引用本文复制引用
梁炳松,李育英,张岐平,陈英道..基于eStroke国家溶取栓影像平台的随机森林模型预测醒后卒中预后的价值[J].中国实用神经疾病杂志,2024,27(7):802-808,7.基金项目
广西壮族自治区卫生健康委员会项目(编号:Z20211202) （编号:Z20211202）
