Background: Information on the clinical characteristics and severity of autoimmune encephalitis with antibodies against the N-methyl-d-aspartate receptor (NMDAR) in children is attracting more and more attention in the field of pediatric research. Methods: In this retrospective cohort study, all cases (n = 67) were enrolled from a tertiary children's hospital, from 2017 to 2020. We compared severe cases that received intensive care unit (ICU) care with nonsevere cases that did not receive ICU care and used machine learning algorithm to predict the severity of children, as well as using immunologic and viral nucleic acid tests to identify possible pathogenic triggers. Results: Mean age of children was 8.29 (standard deviation 4.09) years, and 41 (61.19%) were girls. Eleven (16.42%) were admitted to the ICU, and 56 (83.58%) were admitted to neurology ward. Ten individual parameters were statistically significant differences between severe cases and nonsevere cases (P < .05), including headache, abnormal mental behavior or cognitive impairment, seizures, concomitant tumors, sputum/blood pathogens, blood globulin, blood urea nitrogen, blood immunoglobulin G, blood immunoglobulin M, and number of polynucleated cells in cerebrospinal fluid. Random forest regression model presented that the overall prediction power of severity reached 0.806, among which the number of polynucleated cells in cerebrospinal fluid contributed the most. Potential pathogenic causes exhibited that the proportion of mycoplasma was the highest, followed by Epstein-Barr virus. Conclusion: Our findings provided evidence for early identification of autoimmune encephalitis in children, especially in severe cases.

背景：关于儿​​童N-甲基-d-天冬氨酸受体 (NMDAR)抗体的自身免疫性脑炎的临床特征和严重程度的信息越来越受到儿科研究领域的关注。方法：在这项回顾性队列研究中，所有病例 (n = 67) 均来自 2017 年至 2020 年的一家三级儿童医院。我们比较了接受重症监护病房 (ICU) 护理的重症病例与未接受 ICU 护理的非重症病例并使用机器学习算法来预测儿童的严重程度，并使用免疫学和病毒核酸测试来识别可能的致病诱因。结果：儿童的平均年龄为 8.29（标准差 4.09）岁，其中 41 名（61.19%）为女孩。ICU病房11人（16.42%），神经内科病房56人（83.58%）。10个单项参数在重症病例与非重症病例之间差异有统计学意义（P < .05)，包括头痛、精神行为异常或认知障碍、癫痫发作、伴随肿瘤、痰液/血液病原体、血球蛋白、血尿素氮、血免疫球蛋白 G、血免疫球蛋白 M 和脑脊液中多核细胞的数量。随机森林回归模型显示，严重程度的总体预测能力达到0.806，其中脑脊液中多核细胞的数量贡献最大。潜在致病原因显示支原体的比例最高，其次是爱泼斯坦-巴尔病毒。结论：我们的研究结果为早期识别儿童自身免疫性脑炎提供了证据，特别是在严重病例中。