AUTHOR=Li Xiang , Pan XiDing , Jiang ChunLian , Wu MingRu , Liu YuKai , Wang FuSang , Zheng XiaoHan , Yang Jie , Sun Chao , Zhu YuBing , Zhou JunShan , Wang ShiHao , Zhao Zheng , Zou JianJun 

TITLE=Predicting 6-Month Unfavorable Outcome of Acute Ischemic Stroke Using Machine Learning

JOURNAL=Frontiers in Neurology

VOLUME=11

YEAR=2020

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2020.539509

DOI=10.3389/fneur.2020.539509

ISSN=1664-2295

ABSTRACT=<p><bold>Background and Purpose:</bold> Accurate prediction of functional outcome after stroke would provide evidence for reasonable post-stroke management. This study aimed to develop a machine learning-based prediction model for 6-month unfavorable functional outcome in Chinese acute ischemic stroke (AIS) patient.</p><p><bold>Methods:</bold> We collected AIS patients at National Advanced Stroke Center of Nanjing First Hospital (China) between September 2016 and March 2019. The unfavorable outcome was defined as modified Rankin Scale score (mRS) 3–6 at 6-month. We developed five machine-learning models (logistic regression, support vector machine, random forest classifier, extreme gradient boosting, and fully-connected deep neural network) and assessed the discriminative performance by the area under the receiver-operating characteristic curve. We also compared them to the Houston Intra-arterial Recanalization Therapy (HIAT) score, the Totaled Health Risks in Vascular Events (THRIVE) score, and the NADE nomogram.</p><p><bold>Results:</bold> A total of 1,735 patients were included into this study, and 541 (31.2%) of them had unfavorable outcomes. Incorporating age, National Institutes of Health Stroke Scale score at admission, premorbid mRS, fasting blood glucose, and creatinine, there were similar predictive performance between our machine-learning models, while they are significantly better than HIAT score, THRIVE score, and NADE nomogram.</p><p><bold>Conclusions:</bold> Compared with the HIAT score, the THRIVE score, and the NADE nomogram, the RFC model can improve the prediction of 6-month outcome in Chinese AIS patients.</p>