Xie Zhuxiao, Zhang Jingxiao, Liu Lei, Hu Enyu, Wang Jiawei
Department of Neurology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Front Neurol. 2025 Mar 18;16:1575835. doi: 10.3389/fneur.2025.1575835. eCollection 2025.
Severe autoimmune encephalitis (AE) can cause significant neurological deficits, status epilepticus, status dystonicus, and even death, which can be life-threatening to patients. Accurate risk stratification for severe AE progression is critical for optimizing therapeutic strategies. The comprehensive prediction models for severe AE based on routine clinical data and laboratory indicators remain lacking.
To develop and validate a prediction model for severe AE to optimize individualized treatment.
We collected clinical data and laboratory examination results from 207 patients with confirmed AE. The study population was divided into development and validation cohort. A prediction model for severe AE was constructed using a nomogram and was rigorously validated both internally and externally. Severe AE was defined as modified Rankin Scale (mRS) > 2 and Clinical Assessment Scale for Encephalitis (CASE) > 4.
The variables ultimately included in the nomogram for the severe AE predictive model were age, psychiatric and/or behavioral abnormalities, seizures, decreased level of consciousness, cognitive impairment, involuntary movements, autonomic dysfunction, and increased intrathecal IgG synthesis rate. It demonstrated excellent discriminative capacity and calibration through internal-external validation.
The prediction model has highly feasibility in clinical practice, and holds promise as an important tool for risk assessment and guiding individualized treatment in patients with AE.
重症自身免疫性脑炎（AE）可导致严重的神经功能缺损、癫痫持续状态、肌张力障碍持续状态，甚至死亡，对患者生命构成威胁。准确对重症AE进展进行风险分层对于优化治疗策略至关重要。基于常规临床数据和实验室指标的重症AE综合预测模型仍然缺乏。
建立并验证一种重症AE预测模型以优化个体化治疗。
我们收集了207例确诊AE患者的临床数据和实验室检查结果。研究人群分为开发队列和验证队列。使用列线图构建重症AE预测模型，并在内部和外部进行严格验证。重症AE定义为改良Rankin量表（mRS）>2且脑炎临床评估量表（CASE）>4。
重症AE预测模型列线图最终纳入的变量为年龄、精神和/或行为异常、癫痫发作、意识水平下降、认知障碍、不自主运动、自主神经功能障碍以及鞘内IgG合成率升高。通过内部-外部验证，其显示出良好的辨别能力和校准度。
该预测模型在临床实践中具有高度可行性，有望成为AE患者风险评估和指导个体化治疗的重要工具。
