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Induction and Diverse Assessment Indicators of Experimental Autoimmune Encephalomyelitis
Published on: September 9, 2022
Predictors for identifying autoimmune encephalitis in pediatric patients
Yanmeng Sun1,2, Shangmin Yang1,2, Mengyuan Wang1,2
1Department of Microbiology Laboratory, Children's Hospital Affiliated to Shandong University (Jinan Children's Hospital), Jinan, China.
Insights
This study identified key predictors for pediatric autoimmune encephalitis (AE) and developed a highly accurate diagnostic model. The model uses age, cerebrospinal fluid protein and chloride levels, and spontaneous remission to predict AE in children.
Area of Science:
- Neurology
- Pediatrics
- Immunology
Background:
- Autoimmune encephalitis (AE) is a serious neurological condition in children.
- Accurate and early diagnosis of pediatric AE is crucial for effective treatment.
- Predictive models can aid in identifying children at risk for AE.
Purpose of the Study:
- To identify independent predictors of autoimmune encephalitis (AE) in pediatric patients.
- To develop and validate a predictive model for diagnosing pediatric AE.
- To improve the diagnostic accuracy of AE in children.
Main Methods:
- Retrospective study of 88 pediatric patients (37 AE, 51 non-AE).
- Lasso regression, univariate, and multivariate logistic analyses were used to identify risk factors.
- A predictive model was developed and validated using ROC curves, calibration plots, and decision curve analysis.
Main Results:
- Four independent predictors for pediatric AE were identified: age, cerebrospinal fluid protein, cerebrospinal fluid chloride, and spontaneous remission.
- The predictive model demonstrated excellent diagnostic performance with an AUC of 0.976.
- The model showed strong discrimination and calibration, indicating high reliability.
Conclusions:
- A high-performance predictive model for pediatric autoimmune encephalitis was successfully established and validated.
- The model incorporates four clinically accessible parameters, facilitating practical application in diagnosis.
- This tool can significantly aid clinicians in the early and accurate diagnosis of AE in children.
Objectives:
This study aimed to identify the independent predictors and develop a predictive model for autoimmune encephalitis (AE) in pediatric populations.
Methods:
This retrospective study comprised 88 pediatric patients with encephalitis (37 AE cases and 51 non- AE cases) at Children's Hospital Affiliated to Shandong University between May 2020 and April 2025. Lasso regression analysis, univariate and multivariate logistic analysis was used to identify autoimmune encephalitis associated risk factors. The nomogram visualized the results. Receiver operating characteristic (ROC) curves, calibration plots, Brier scoring and decision curve analysis (DCA) were used to evaluate the diagnostic model.
Results:
16 clinical variables significantly differed between the autoimmune encephalitis and non-autoimmune encephalitis groups. Lasso regression analysis, univariate and multivariate logistic analysis identified four significant independent predictors: age (OR: 1.44; 95% CI: 1.09-1.91; P = 0.010), proteins in the cerebrospinal fluid/100(C.Protein.100) (OR: 0.80; 95% CI: 0.65-1.00; P = 0.049), chloride in the cerebrospinal fluid(C. Chloride) (OR: 1.38; 95% CI: 1.00-1.92; P = 0.050), and spontaneous remission (OR: 21.14; 95% CI: 3.17-141.17; P = 0.002) were risk factors for autoimmune encephalitis. The predictive model demonstrated excellent discrimination (AUC 0.976, 95% CI 0.947-1.000) and calibration (Hosmer-Lemeshow p = 0.886, R²=0.9796, Brier score 0.052).
Conclusions:
This study established and validated a high-performance predictive model incorporating four clinically accessible parameters for the diagnosis of pediatric autoimmune encephalitis.
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01:19Encephalitis l: Introduction
01:26Encephalitis ll: Pathophysiology