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摘要： 目的 开发轻型急性缺血性卒中（acute ischemic stroke，AIS）患者院内神经功能恶化的预测模型，为AIS的院内分层管理提供科学依据。 方法 选取中国国家卒中登记Ⅲ（China national stoke registry Ⅲ，CNSR Ⅲ）中发病24 h内的轻型AIS（NIHSS评分≤5分）且未接受rt-PA静脉溶栓或血管内治疗的患者作为研究对象。开发队列为CNSR Ⅲ 2015—2016年入组的2256例患者，验证队列为CNSR Ⅲ 2017—2018年入组的1775例患者。应用LASSO回归筛选预测因子，并参考既往研究确定最终预测因子。院内神经功能恶化定义为出院时NIHSS评分较入院时NIHSS评分增加≥4分。基于logistic回归开发预测模型，分别使用C统计量和Brier得分对模型的区分度和校准度进行评价。 结果 本研究共纳入4031例患者，开发队列2256例患者中有58例（2.6%）发生院内神经功能恶化，验证队列1775例患者中有63例（3.5%）发生院内神经功能恶化。两个队列在人群特征上基本一致。预测模型最终基于年龄、性别、吸烟情况、收缩压、IL-6、hs-CRP、入院NIHSS评分、糖尿病及梗死模式共计9个预测因子开发。模型的C统计量在开发队列中为0.69（95%CI 0.62～0.76），在验证队列中为0.73（95%CI 0.67～0.80）；模型的Brier得分在开发队列中为0.025，在验证队列中为0.033。 结论 本研究基于常规住院数据建立了一个轻型AIS患者院内神经功能恶化预测模型，有较好的区分度和校准度，但是外推性需要外部数据进一步验证。 Abstract: Objective To develop a prediction model of in-hospital neurological deterioration for patients with minor acute ischemic stroke (AIS), and to provide scientific basis for stratified in-hospital management. Methods Patients with minor AIS (defined as NIHSS score≤5) enrolled in the China national stroke registry Ⅲ (CNSR Ⅲ) and arriving within 24 hours from onset while without taking rt-PA intravenous thrombolysis or endovascular treatment were selected as the study subjects. The derivation cohort was consisted of 2256 patients enrolled from 2015 to 2016, and the validation cohort was consisted of 1775 patients enrolled from 2017 to 2018. The predictors were finally determined by LASSO regression and reviewing of previous studies. In-hospital neurological deterioration was defined as 4 points or more increase in NIHSS score at discharge compared with the NIHSS score at admission. A logistic regression model was used to develop the prediction model. Discrimination and calibration were evaluated using C statistic and the Brier score, respectively. Results A total of 4031 patients were included in the study, with 58(2.6%) of 2256 patients from the derivation cohort and 63(3.5%) of 1775 patients from the validation cohort encountered in-hospital neurological deterioration. The population characteristics were similar between the two cohorts. The prediction model was developed based on 9 predictors, including age, gender, smoking, systolic blood pressure, IL-6, hs-CRP, NIHSS score on admission, diabetes mellitus and infarction pattern. The C statistic for the model was 0.69 (95%CI 0.62-0.76) in the derivation cohort and 0.73 (95%CI 0.67-0.80) in the validation cohort. The Brier score of the model was 0.025 in the derivation cohort and 0.033 in the validation cohort. Conclusions This study developed a prediction model for the risk of in-hospital neurological deterioration for patients with minor AIS based on routine hospitalization data, and the prediction model achieved acceptable levels of discrimination and calibration, yet the extrapolation needs to be further verified by external data. |
