Journal of Neurology ( IF 6 ) Pub Date : 2020-05-26 , DOI: 10.1007/s00415-020-09914-0 E Muiño 1, 2, 3 , A Bustamante 4 , A Rodriguez-Campello 5 , C Gallego-Fabrega 1, 2 , A Ois 5 , C Carrera 2, 4 , N Cullell 1, 2 , N Torres-Aguila 1, 2 , J Cárcel-Márquez 1, 2 , M Rubiera 6 , C A Molina 6 , E Cuadrado-Godia 5 , E Giralt-Steinhauer 5 , J Jiménez-Conde 5 , J Montaner 4, 7, 8 , I Fernández-Cadenas 2 , J Roquer 5
Background
Most of the models to predict prognosis after an ischemic stroke include complex mathematical equations or too many variables, making them difficult to use in the daily clinic. We want to predict disability 3 months after an ischemic stroke in an independent patient not receiving recanalization treatment within the first 24 h, using a minimum set of variables and an easy tool to facilitate its implementation. As a secondary aim, we calculated the capacity of the score to predict an excellent/devastating outcome and mortality.
Methods
Eight hundred and forty-four patients were evaluated. A multivariable ordinal logistic regression was used to obtain the score. The Modified Rankin Scale (mRS) was used to estimate disability at the third month. The results were replicated in another independent cohort (378 patients). The “polr” function of R was used to perform the regression, stratifying the sample into seven groups with different cutoffs (from mRS 0 to 6).
Results
The Parsifal score was generated with: age, previous mRS, initial NIHSS, glycemia on admission, and dyslipidemia. This score predicts disability with an accuracy of 80–76% (discovery–replication cohorts). It has an AUC of 0.86 in the discovery and replication cohort. The specificity was 90–80% (discovery–replication cohorts); while, the sensitivity was 64–74% (discovery–replication cohorts). The prediction of an excellent or devastating outcome, as well as mortality, obtained good discrimination with AUC > 0.80.
Conclusions
The Parsifal Score is a model that predicts disability at the third month, with only five variables, with good discrimination and calibration, and being replicated in an independent cohort.
中文翻译:
使用免费的网络工具来预测缺血性中风后的残疾的简约评分:简约评分。
背景
预测缺血性中风后预后的大多数模型都包含复杂的数学方程式或太多变量,这使得它们很难在日常临床中使用。我们希望使用最小的变量集和简便的工具来预测缺血性卒中后3个月内在开始的24小时内未接受再通治疗的独立患者的残疾情况。作为第二个目标,我们计算了得分的能力,以预测优异/毁灭性的结果和死亡率。
方法
对844例患者进行了评估。使用多变量序数逻辑回归来获得分数。改良的兰金量表(mRS)用于估计第三个月的残疾。将结果复制到另一个独立的队列中(378例患者)。R的“ polr”函数用于执行回归,将样本分为具有不同截止值(从mRS 0到6)的七个组。
结果
Parsifal评分的产生依据是:年龄,以前的mRS,初始NIHSS,入院时的血糖和血脂异常。该分数预测残疾的准确性为80-76%(发现-复制队列)。发现和复制队列中的AUC为0.86。特异性为90-80%(发现-复制队列);而敏感性为64-74%(发现-复制队列)。对优或毁灭性结果以及死亡率的预测得到了很好的区分,AUC> 0.80。
结论
Parsifal分数是一个模型,该模型可预测第三个月的残障情况,只有五个变量,具有良好的辨别力和校准能力,并且可以在独立的队列中复制。