Personalized risk prediction of symptomatic intracerebral hemorrhage after stroke thrombolysis using a machine-learning model
AuthorWang, F; Huang, Y; Xia, Y; Zhang, W; Fang, K; Zhou, X; Yu, X; Cheng, X; Li, G; Wang, X; ...
Source TitleTherapeutic Advances in Neurological Disorders
PublisherSAGE PUBLICATIONS LTD
University of Melbourne Author/sCampbell, Bruce
Document TypeJournal Article
CitationsWang, F., Huang, Y., Xia, Y., Zhang, W., Fang, K., Zhou, X., Yu, X., Cheng, X., Li, G., Wang, X., Luo, G., Wu, D., Liu, X., Campbell, B. C. V., Dong, Q. & Zhao, Y. (2020). Personalized risk prediction of symptomatic intracerebral hemorrhage after stroke thrombolysis using a machine-learning model. THERAPEUTIC ADVANCES IN NEUROLOGICAL DISORDERS, 13, https://doi.org/10.1177/1756286420902358.
Access StatusOpen Access
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.
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