A predictive risk model for outcomes of ischemic stroke
A predictive risk model for outcomes of ischemic stroke
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DOI：
10.1161/01.str.31.2.448
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发表时间：
2000-02-01
期刊：
影响因子：
8.3
通讯作者：
Haley, EC
中科院分区：
文献类型：
--
作者：
Johnston, KC;Connors, AF;Haley, EC
背景和目的：脑卒中患者预后的巨大可变性使得人们对确定预后预测因素产生了兴趣。在多变量风险调整模型中，临床和影像学变量的结合作为脑卒中预后的预测因素可能比单独使用更有效。本研究的目的是确定缺血性脑卒中患者梗死面积、6个临床变量和3个月预后之间的多变量关系。方法：该研究纳入了256名来自甲磺酸替拉扎德治疗急性卒中随机试验（RANTTAS）的符合条件的患者。6个临床变量和1周梗死面积是预先指定的预测变量，美国国立卫生研究院卒中量表、Barthel指数和格拉斯哥结局量表是预测结果。使用多变量逻辑回归技术来开发模型方程，并使用自举技术进行内部验证。用接收算子特征（ROC）曲线评估模型的预测性能，并用校准曲线进行校准。结果：预测模型的ROC曲线下面积在0.79 ~ 0.88之间，具有较为理想的校正曲线。ROC曲线下的面积在统计学上更大(P
Background and Purpose-The great variability of outcome seen in stroke patients has led to an interest in identifying predictors of outcome. The combination of clinical and imaging variables as predictors of stroke outcome in a multivariable risk adjustment model may be more powerful than either alone. The purpose of this study was to determine the multivariable relationship between infarct volume, 6 clinical variables, and 3-month outcomes in ischemic stroke patients.Methods-Included in the study were 256 eligible patients from the Randomized Trial of Tirilazad Mesylate in Acute Stroke (RANTTAS). Six clinical variables and I-week infarct volume were the prespecified predictor variables, The National Institutes of Health Stroke Scale, Barthel Index, and Glasgow Outcome Scale were the outcomes. Multivariable logistic regression techniques were used to develop the model equations, and bootstrap techniques were used for internal validation. Predictive performance of the models was assessed for discrimination with receiver operator characteristic (ROC) curves and for calibration with calibration curves.Results-The predictive models had areas under the ROC curve of 0.79 to 0.88 and demonstrated nearly ideal calibration curves. The areas under the ROC curves were statistically greater (P