Sun Yueqian, Ren Guoping, Ren Jiechuan, Shan Wei, Han Xiong, Lian Yajun, Wang Tiancheng, Wang Qun
Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
China National Clinical Research Center for Neurological Diseases, Beijing, China.
Front Neurol. 2021 Apr 8;12:612569. doi: 10.3389/fneur.2021.612569. eCollection 2021.
The aim of this retrospective study was to derive and validate a reliable nomogram for predicting prognosis of autoimmune encephalitis (AE). A multi-center retrospective study was conducted in four hospitals in China, using a random split-sample method to allocate 173 patients into either a training ( = 126) or validation ( = 47) dataset. Demographic, radiographic and therapeutic presentation, combined with clinical features were collected. A modified Rankin Scale (mRS) at discharge was the principal outcome variable. A backward-stepwise approach based on the Akaike information criterion was used to test predictors and construct the final, parsimonious model. Multivariable analysis was conducted using logistic regression to develop a prognosis model and validate a nomogram using an independent dataset. The performance of the model was assessed using receiver operating characteristic curves and a Hosmer-Lemeshow test. The final nomogram model considered age, viral prodrome, consciousness impairment, memory dysfunction and autonomic dysfunction as predictors. Model validations displayed a good level of discrimination in the validation set: area under the Receiver operator characteristic curve = 0.72 (95% Confidence Interval: 0.56-0.88), Hosmer-Lemeshow analysis suggesting good calibration (chi-square: 10.33; = 0.41). The proposed nomogram demonstrated considerable potential for clinical utility in prediction of prognosis in autoimmune encephalitis.
本回顾性研究的目的是推导并验证一种用于预测自身免疫性脑炎（AE）预后的可靠列线图。在中国的四家医院进行了一项多中心回顾性研究，采用随机分割样本法将173例患者分为训练数据集（n = 126）或验证数据集（n = 47）。收集了人口统计学、影像学和治疗表现以及临床特征。出院时的改良Rankin量表（mRS）是主要结局变量。采用基于赤池信息准则的向后逐步法来检验预测因素并构建最终的简约模型。使用逻辑回归进行多变量分析以建立预后模型，并使用独立数据集验证列线图。使用受试者工作特征曲线和Hosmer-Lemeshow检验评估模型的性能。最终的列线图模型将年龄、病毒前驱症状、意识障碍、记忆功能障碍和自主神经功能障碍视为预测因素。模型验证在验证集中显示出良好的区分度：受试者工作特征曲线下面积 = 0.72（95%置信区间：0.56 - 0.88），Hosmer-Lemeshow分析表明校准良好（卡方值：10.33；P = 0.41）。所提出的列线图在预测自身免疫性脑炎预后方面显示出相当大的临床应用潜力。
