Stroke Prognostic Scores and Data-Driven Prediction of Clinical Outcomes After Acute Ischemic Stroke
Stroke Prognostic Scores and Data-Driven Prediction of Clinical Outcomes After Acute Ischemic Stroke
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DOI：
10.1161/strokeaha.119.027300
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发表时间：
2020-05-01
期刊：
影响因子：
8.3
通讯作者：
Kamouchi, Masahiro
中科院分区：
文献类型：
--
作者：
Matsumoto, Koutarou;Nohara, Yasunobu;Kamouchi, Masahiro
背景和目的--几种预测中风预后的评分已经被用来预测中风后的临床结果。这项研究旨在通过参考真实环境中急性缺血性中风患者先前的预后评分，开发和验证新的数据驱动的临床预后预测模型。方法-我们使用了2012年1月至2017年8月在日本一家卒中中心住院的4237名急性缺血性中风患者的回顾数据。我们首次在我们的队列中验证了基于点数的卒中预后评分(入院前并发症、意识水平、年龄和神经功能缺失[PLAN]评分、缺血性卒中预测风险评分[IScore]和急性卒中登记和洛桑[Astral]评分分析；休斯顿动脉内再通治疗[HIAT]评分、血管事件中的总健康风险[Thrive]评分，以及接受再灌注治疗的患者使用年龄和国立卫生研究院卒中分级-100[SPAN-100]进行卒中预测)。然后，我们使用所有可用的数据通过线性回归或决策树集成(随机森林和梯度提升决策树)建立预测模型，并在重复随机拆分后评估它们在接收器操作特征曲线下的临床结果面积。结果-患者的平均年龄(SD)为74.7(12.9)岁，其中58.3%是男性。预后评分的受试者操作特征曲线下面积(95%Cis)分别为：PLAN评分0.92分(0.90~0.93)，IScore评分0.86(0.85~0.87)，Astral评分0.85~0.86，HIAT评分0.69(0.62~0.75)，Thrive评分0.70(0.64~0.76)，SPAN-100评分0.70(0.63~0.76)。计划评分0.87(0.85~0.90)，IScore评分0.88(0.86~0.91)，Astral评分0.88(0.85~0.91)。数据驱动预测模型的内部验证显示，受试者操作特征曲线下的面积在0.88到0.94之间，对于功能预后差，在0.84到0.88之间，对于住院死亡率。决策树的集成模型在预测不良的功能结果方面优于线性回归模型，但在预测住院死亡率方面表现不佳。结论：卒中预后评分在预测中风后的临床结果方面表现良好。数据驱动模型可能是在现实世界中预测中风后临床结果的另一种工具。
Background and Purpose-Several stroke prognostic scores have been developed to predict clinical outcomes after stroke. This study aimed to develop and validate novel data-driven predictive models for clinical outcomes by referring to previous prognostic scores in patients with acute ischemic stroke in a real-world setting.Methods-We used retrospective data of 4237 patients with acute ischemic stroke who were hospitalized in a single stroke center in Japan between January 2012 and August 2017. We first validated point-based stroke prognostic scores (preadmission comorbidities, level of consciousness, age, and neurological deficit [PLAN] score, ischemic stroke predictive risk score [IScore], and acute stroke registry and analysis of Lausanne [ASTRAL] score in all patients; Houston intraarterial recanalization therapy [HIAT] score, totaled health risks in vascular events [THRIVE] score, and stroke prognostication using age and National Institutes of Health Stroke Scale-100 [SPAN-100] in patients who received reperfusion therapy) in our cohort. We then developed predictive models using all available data by linear regression or decision tree ensembles (random forest and gradient boosting decision tree) and evaluated their area under the receiver operating characteristic curve for clinical outcomes after repeated random splits.Results-The mean (SD) age of the patients was 74.7 (12.9) years and 58.3% were men. Area under the receiver operating characteristic curves (95% CIs) of prognostic scores in our cohort were 0.92 PLAN score (0.90-0.93), 0.86 for IScore (0.85-0.87), 0.85 for ASTRAL score (0.83-0.86), 0.69 for HIAT score (0.62-0.75), 0.70 for THRIVE score (0.64-0.76), and 0.70 for SPAN-100 (0.63-0.76) for poor functional outcomes, and 0.87 for PLAN score (0.85-0.90), 0.88 for IScore (0.86-0.91), and 0.88 ASTRAL score (0.85-0.91) for in-hospital mortality. Internal validation of data-driven prediction models showed that their area under the receiver operating characteristic curves ranged between 0.88 and 0.94 for poor functional outcomes and between 0.84 and 0.88 for in-hospital mortality. Ensemble models of a decision tree tended to outperform linear regression models in predicting poor functional outcomes but not in predicting in-hospital mortality.Conclusions-Stroke prognostic scores perform well in predicting clinical outcomes after stroke. Data-driven models may be an alternative tool for predicting poststroke clinical outcomes in a real-world setting.