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Updated: Jul 8, 2026
Induction and Clinical Scoring of Chronic-Relapsing Experimental Autoimmune Encephalomyelitis
Published on: July 4, 2007
A predictive nomogram for relapse risk in children with autoimmune encephalitis
1Department of Neurology, Hangzhou Children's Hospital, No. 195 Wenhui Road, Gongshu District, Hangzhou, Zhejiang, 310005, P.R. China. Yaowenting1993@protonmail.com.
Insights
A new nomogram accurately predicts relapse in Chinese children with autoimmune encephalitis (AE), aiding in personalized risk assessment and potentially improving long-term neurodevelopmental outcomes.
Area of Science:
- Pediatric Neurology
- Immunology
- Clinical Prediction Modeling
Background:
- Autoimmune encephalitis (AE) is a significant cause of acute encephalopathy in children, with relapses leading to adverse neurodevelopmental outcomes.
- Predicting relapse risk in pediatric AE is crucial for timely intervention, but validated tools for the Chinese population are lacking.
Purpose of the Study:
- To develop and validate a nomogram for predicting the risk of relapse in children diagnosed with autoimmune encephalitis in China.
Main Methods:
- A retrospective cohort study of 127 children (aged 1-16 years) diagnosed with AE between 2017 and 2023.
- Logistic regression identified independent predictors of relapse.
- A nomogram was constructed and validated for discrimination, calibration, and stability.
Main Results:
- The overall relapse rate was 26.8%.
- Independent risk factors for relapse included older age (≥8 years), limb weakness, delayed immunotherapy, and positive N-methyl-D-aspartate receptor (NMDAR) antibodies.
- Rituximab and prolonged IVIG treatment (≥6 months) were protective factors. The nomogram demonstrated excellent predictive accuracy (C-statistic 0.936).
Conclusions:
- The developed nomogram is accurate and clinically useful for predicting individualized relapse risk in Chinese children with AE.
- This tool can aid clinicians in stratifying risk and tailoring management strategies for pediatric AE patients.
Objectives:
Autoimmune encephalitis (AE) is a major cause of acute encephalopathy in children, and relapse is strongly linked to long-term neurodevelopmental sequelae. However, studies on relapse risk prediction in the Chinese pediatric population remain limited, and validated tools for individualized risk stratification are lacking. This study aimed to develop and validate a nomogram for predicting relapse risk in children with AE.
Methods:
This retrospective cohort study included 127 children aged 1-16 years diagnosed with AE at a tertiary pediatric center in China between 2017 and 2023. Independent predictors of relapse were identified using univariable and multivariable logistic regression. A nomogram was constructed and comprehensively validated for discrimination, calibration, and stability.
Results:
The overall relapse rate was 26.8% (34/127). Independent risk factors for relapse included age ≥ 8 years at onset (OR 3.19, 95% CI 1.37-7.42, P 0.007), limb weakness (OR 2.36, 95% CI 1.03-5.42, P 0.041), delayed immunotherapy (OR 2.77, 95% CI 1.23-6.24, P 0.013), and positive N-methyl-D-aspartate receptor (NMDAR) antibody (OR 2.91, 95% CI 1.29-6.56, P 0.010). Rituximab treatment (OR 0.43, 95%CI 0.19-0.95, P 0.036) and IVIG treatment ≥ 6 months (OR 0.35, 95%CI 0.13-0.92, P 0.033) were confirmed as independent protective factors. The nomogram showed excellent discriminative ability (C-statistic 0.936, R² 0.683), good calibration (Brier score 0.081, calibration intercept < 0.001, slope 1.000), and robust stability on internal validation with 1000 bootstrap resamples.
Conclusion:
This nomogram demonstrates favorable accuracy and clinical utility for individualized relapse risk prediction in Chinese children with AE.
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