Prediction of large vessel occlusion for ischaemic stroke by using the machine learning model random forests

作     者：Jianan Wang Jungen Zhang Xiaoxian Gong Wenhua Zhang Ying Zhou Min Lou 

作者机构：Department of NeurologyZhejiang University School of Medicine Second Affiliated Hospital Department of NeurologyHangzhouZhejiangChina 

出 版 物：《Stroke & Vascular Neurology》 (卒中与血管神经病学（英文）)

年 卷 期：2022年第7卷第2期

页      面：94-100页

核心收录：

学科分类：1002[医学-临床医学] 100204[医学-神经病学] 10[医学] 

基　　金：supported by the Science Technology Department of Zhejiang Province(2018C04011) the National Natural Science Foundation of China(81971101) the National Key Research and Development Program of China(2016YFC1301503) 

主　　题：boosting random narrow 

摘      要：Backgrounds The timely identification of large vessel occlusion(LVO)in the prehospital stage is extremely important given the disease morbidity and narrow time window for *** current evaluation strategies still remain *** goal of this study was to develop a machine learning(ML)model to predict LVO using prehospital accessible *** Consecutive acute ischaemic stroke patients who underwent CT or MR angiography and received reperfusion therapy within 8 hours from symptom onset in the Computer-based Online Database of Acute Stroke Patients for Stroke Management Quality Evaluation-II dataset from January 2016 to August 2021 were *** developed eight ML models to integrate National Institutes of Health Stroke Scale(NIHSS)items with demographics,medical history and vascular risk factors to identify LVO and validate its *** Finally,15365 patients were included in the training set and 4215 patients were included in the test *** the test set,random forests(RF),gradient boosting machine and extreme gradient boosting presented area under the curve(AUC)of 0.831(95%CI 0.819 to 0.843),which were higher than other models,and RF presented the highest specificity(0.827).In addition,the AUC of RF was higher than other scales,and the accuracy of the model was improved by 6.4%compared with *** also found the top five items of identifying LVO were total NIHSS score,gaze deviation,level of consciousness(LOC),LOC commands and motor left *** Our proposed model could be a useful screening tool to predict LVO based on the prehospital accessible medical data.