机构：[1]Department of Neurology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China[2]College of Electronics and Information Engineering, Tongji University, Shanghai, China[3]IBM, Shanghai, China[4]Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China[5]Department of Neurology, Shanghai Tenth People’s Hospital, Tongji University, Shanghai, China[6]Department of Neurology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China[7]Department of Neurology, East Hospital, Tongji University, Shanghai, China[8]Department of Neurology, Shanghai TongRen Hospital, Tongji University, Shanghai, China[9]Department of Neurology, Jinshan Branch of Shanghai Sixth People’s Hospital, Shanghai, China[10]Department of Neurology, Shanghai Fifth People’s Hospital, Fudan University, Shanghai, China[11]Departments of Medicine and Neurology, Melbourne Brain Centre at the Royal Melbourne Hospital, University of Melbourne, Parkville, Australia[12]Department of Neurology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, No. 600, Yishan Road, Xuhui District, Shanghai, 200233, China[13]Department of Neurology, Huashan Hospital, Fudan University, No.12, Wulumuqi Zhong Road, Jingan District, Shanghai, 200040, China
Background: Personalized prediction of the risk of symptomatic intracerebral hemorrhage (sICH) after stroke thrombolysis is clinically useful. Machine-learning-based modeling may provide the personalized prediction of the risk of sICH after stroke thrombolysis. Methods: We identified 2578 thrombolysis-treated ischemic stroke patients between January 2013 and December 2016 from a multicenter database, where 70% were used to train models and the remaining 30% were used as the nominal test sets. Another 136 consecutive tissue plasminogen-activated-treated patients between January 2017 and December 2017 from our institute were enrolled as the independent test sets for clinical usability evaluation. Five machine-learning models were developed to predict the risk of sICH after stroke thrombolysis, and the receiving operating characteristic (ROC) was used to compare the prediction performance. Results: In total, 2237 cases were included in our study, of which 102 had sICH transformation (4.56%). Finally, the three-layer neuro network was selected with the best performance on nominal test sets (AUC = 0.82). The probability of the model score was further categorized into three risk ranks (18.97%, 5.63%, and 0.81%) according to the risk distribution. Implementing our system in clinical practice was associated with reduced computed tomography (CT)-to-treatment time (CTT; 41 min versus 52 min, p < 0.001). All sICH patients were correctly predicted to be within the high-sICH risk rank. Conclusions: The machine-learning-based modeling is feasible for providing personalized risk prediction of sICH after stroke thrombolysis, and is able to reduce the CTT. More data are needed to further optimize the model and improve the accuracy of prediction.
基金：
Interdisciplinary Program of Shanghai Jiao Tong University [YG2017MS14]; Science and Technology Commission of Shanghai MunicipalityScience & Technology Commission of Shanghai Municipality (STCSM) [19411968500]
第一作者机构：[1]Department of Neurology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
通讯作者：
通讯机构：[12]Department of Neurology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, No. 600, Yishan Road, Xuhui District, Shanghai, 200233, China[13]Department of Neurology, Huashan Hospital, Fudan University, No.12, Wulumuqi Zhong Road, Jingan District, Shanghai, 200040, China[*1]Department of Neurology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, No. 600, Yishan Road, Xuhui District, Shanghai, 200233, China[*2]Department of Neurology, Huashan Hospital, Fudan University, No.12, Wulumuqi Zhong Road, Jingan District, Shanghai, 200040, China
推荐引用方式(GB/T 7714)：
Feng Wang,Yuanhanqing Huang,Yong Xia,et al.Personalized risk prediction of symptomatic intracerebral hemorrhage after stroke thrombolysis using a machine-learning model[J].THERAPEUTIC ADVANCES IN NEUROLOGICAL DISORDERS.2020,13:doi:10.1177/1756286420902358.
APA：
Feng Wang,Yuanhanqing Huang,Yong Xia,Wei Zhang,Kun Fang...&Yuwu Zhao.(2020).Personalized risk prediction of symptomatic intracerebral hemorrhage after stroke thrombolysis using a machine-learning model.THERAPEUTIC ADVANCES IN NEUROLOGICAL DISORDERS,13,
MLA：
Feng Wang,et al."Personalized risk prediction of symptomatic intracerebral hemorrhage after stroke thrombolysis using a machine-learning model".THERAPEUTIC ADVANCES IN NEUROLOGICAL DISORDERS 13.(2020)
