Ping Zheng, Min Li, Qiuyun Lu, Xu Chen, Qingke Bai
Key Laboratory and Neurosurgery, Shanghai Pudong New Area People's Hospital, Shanghai, China.
Department of Neurology, Shanghai Pudong New Area People's Hospital, Shanghai, China.
Front Neurosci. 2022 Oct 19;16:1017883. doi: 10.3389/fnins.2022.1017883. eCollection 2022.
The prediction of neurological outcomes in ischemic stroke patients is very useful in treatment choices, as well as in post-stroke management. This study is to develop a convenient nomogram for the bedside evaluation of stroke patients with intravenous thrombolysis.
We reviewed all enrolled stroke patients with intravenous thrombolysis retrospectively. Favorable outcome was defined as modified Rankin Score (mRs) less than 2 at 90 days post thrombolysis. We compared the clinical characteristics between patients with favorable outcome and poor outcome. Then, we applied logistic regression models and compared their predictability.
A total of 918 patients were enrolled in this study, 448 patients from one hospital were included to develop a nomogram, whereas 470 patients from the other hospital were used for the external validation. Associated risk factors were identified by multivariate logistic regression. The nomogram was validated by the area under the receiver operating characteristic curve (AUC). A nomogram was developed with baseline NIHSS, blood sugar, blood cholesterol level, part-and full anterior circulation infarction (OCSP type). The AUC was 0.767 (95% CI 0.653-0.772) and 0.836 (95% CI 0.697-0.847) in the derivation and external validation cohorts, respectively. The calibration plot for the probability of severe neurological outcome showed an optimal agreement between the prediction by nomogram and actual observation in both derivation and validation cohorts.
A convenient outcome evaluation nomogram for patients with intravenous thrombolysis was developed, which could be used by physicians in making clinical decisions and predicting patients' prognosis.
预测缺血性中风患者的神经功能转归对治疗方案的选择以及中风后的管理非常有用。本研究旨在开发一种便捷的列线图，用于床边评估接受静脉溶栓治疗的中风患者。
我们回顾性分析了所有纳入的接受静脉溶栓治疗的中风患者。良好转归定义为溶栓后90天时改良Rankin量表（mRs）评分小于2分。我们比较了转归良好和转归不良患者的临床特征。然后，我们应用逻辑回归模型并比较它们的预测能力。
本研究共纳入918例患者，其中来自一家医院的448例患者用于构建列线图，而来自另一家医院的470例患者用于外部验证。通过多因素逻辑回归确定相关危险因素。列线图通过受试者操作特征曲线下面积（AUC）进行验证。利用基线美国国立卫生研究院卒中量表（NIHSS）、血糖、血胆固醇水平、部分前循环和完全前循环梗死（OCSP分型）构建了列线图。在推导队列和外部验证队列中，AUC分别为0.767（95%CI 0.653 - 0.772）和0.836（95%CI 0.697 - 0.847）。严重神经功能转归概率的校准图显示，在推导队列和验证队列中，列线图预测与实际观察之间具有最佳一致性。
开发了一种用于接受静脉溶栓治疗患者的便捷转归评估列线图，可供医生用于临床决策和预测患者预后。
