Chiu I-Min, Zeng Wun-Huei, Cheng Chi-Yung, Chen Shih-Hsuan, Lin Chun-Hung Richard
Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 83301, Taiwan.
Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 804201, Taiwan.
Diagnostics (Basel). 2021 Jan 6;11(1):80. doi: 10.3390/diagnostics11010080.
Prediction of functional outcome in ischemic stroke patients is useful for clinical decisions. Previous studies mostly elaborate on the prediction of favorable outcomes. Miserable outcomes, which are usually defined as modified Rankin Scale (mRS) 5-6, should be considered as well before further invasive intervention. By using a machine learning algorithm, we aimed to develop a multiclass classification model for outcome prediction in acute ischemic stroke patients requiring reperfusion therapy. This was a retrospective study performed at a stroke medical center in Taiwan. Patients with acute ischemic stroke who visited between January 2016 and December 2019 and who were candidates for reperfusion therapy were included. Clinical outcomes were classified as favorable outcome, intermediate outcome, and miserable outcome. We developed four different multiclass machine learning models (Logistic Regression, Supportive Vector Machine, Random Forest, and Extreme Gradient Boosting) to predict clinical outcomes and compared their performance to the DRAGON score. A sample of 590 patients was included in this study. Of them, 180 (30.5%) had favorable outcomes and 152 (25.8%) had miserable outcomes. All selected machine learning models outperformed the DRAGON score on accuracy of outcome prediction (Logistic Regression: 0.70, Supportive Vector Machine: 0.67, Random Forest: 0.69, and Extreme Gradient Boosting: 0.67, vs. DRAGON: 0.51, < 0.001). Among all selected models, Logistic Regression also had a better performance than the DRAGON score on positive predictive value, sensitivity, and specificity. Compared with the DRAGON score, the multiclass machine learning approach showed better performance on the prediction of the 3-month functional outcome of acute ischemic stroke patients requiring reperfusion therapy.
预测缺血性中风患者的功能预后有助于临床决策。以往的研究大多详细阐述了对良好预后的预测。在进行进一步的侵入性干预之前，也应考虑通常被定义为改良Rankin量表（mRS）5 - 6级的悲惨预后。通过使用机器学习算法，我们旨在开发一种多类分类模型，用于预测需要再灌注治疗的急性缺血性中风患者的预后。这是一项在台湾一家中风医疗中心进行的回顾性研究。纳入了2016年1月至2019年12月期间就诊且符合再灌注治疗条件的急性缺血性中风患者。临床结局分为良好结局、中等结局和悲惨结局。我们开发了四种不同的多类机器学习模型（逻辑回归、支持向量机、随机森林和极端梯度提升）来预测临床结局，并将它们的性能与DRAGON评分进行比较。本研究纳入了590例患者样本。其中，180例（30.5%）有良好结局，152例（25.8%）有悲惨结局。所有选定的机器学习模型在结局预测准确性方面均优于DRAGON评分（逻辑回归：0.70，支持向量机：0.67，随机森林：0.69，极端梯度提升：0.67，vs. DRAGON：0.51，<0.001）。在所有选定模型中，逻辑回归在阳性预测值、敏感性和特异性方面也比DRAGON评分表现更好。与DRAGON评分相比，多类机器学习方法在预测需要再灌注治疗的急性缺血性中风患者3个月功能结局方面表现更好。
