Department of Nursing, The First Affiliated Hospital of Shenzhen University/Shenzhen Second People's Hospital, 518035 Shenzhen, China.
School of Nursing, Guangzhou Medical University, Guangzhou 510030, China.
J Stroke Cerebrovasc Dis. 2021 Mar;30(3):105459. doi: 10.1016/j.jstrokecerebrovasdis.2020.105459. Epub 2020 Dec 30.
This study aimed to develop a predictive model of early neurological deterioration (END) in patients with acute ischemic stroke (AIS).
The present retrospective cohort study considered patients with AIS who were admitted to a tertiary hospital in Shenzhen, China between January 2014 and December 2018. An increase of 2 points or more on the National Institute of Health Stroke Scale (NIHSS) within 7 days indicated END. We selected baseline clinical, laboratory, and neuroimaging variables to construct predictive models through multivariate logistic regression. The receiver operating characteristic curve and calibration plots were calculated.
A total of 391 patients with AIS were enrolled in the study. END was observed in 64 (16.4%) cases. A prediction model developed from the initial NIHSS score, middle cerebral artery stenosis, and carotid stenosis of≥ 50% showed good discriminative ability: area under the receiver operating characteristic curve, 0.870 (95%CI, 0.813-0.911); threshold, -1.570; specificity, 84.40%; sensitivity, 75.00%; positive predictive value, 48.48%; and a negative predictive value, 94.52%.
Our predictive model developed from the initial NIHSS score, middle cerebral artery stenosis, and carotid stenosis of ≥ 50% could identify patients with AIS who were at risk of developing END. The model requires validation by larger studies performed at other institutions.
本研究旨在建立急性缺血性脑卒中（AIS）患者早期神经功能恶化（END）的预测模型。
本回顾性队列研究纳入了 2014 年 1 月至 2018 年 12 月期间在中国深圳一家三级医院就诊的 AIS 患者。NIHSS 评分在 7 天内增加 2 分或以上提示 END。我们选择基线临床、实验室和神经影像学变量，通过多变量逻辑回归构建预测模型。计算受试者工作特征曲线和校准图。
共纳入 391 例 AIS 患者，其中 64 例（16.4%）发生 END。由初始 NIHSS 评分、大脑中动脉狭窄和颈动脉狭窄≥50%构建的预测模型具有良好的判别能力：受试者工作特征曲线下面积为 0.870（95%CI，0.813-0.911）；截断值为-1.570；特异性为 84.40%；灵敏度为 75.00%；阳性预测值为 48.48%；阴性预测值为 94.52%。
我们基于初始 NIHSS 评分、大脑中动脉狭窄和颈动脉狭窄≥50%构建的预测模型可以识别出发生 END 的 AIS 患者。该模型需要其他机构进行更大规模的研究验证。
