Severity of Hospitalized Children with Anti-NMDAR Autoimmune Encephalitis.
Mingxing FanWenjie SunDanrong ChenTianyu DongWu YanMingzhi ZhangHaibo YangJun LiXu WangPublished in: Journal of child neurology (2022)
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.
Keyphrases
- intensive care unit
- young adults
- machine learning
- epstein barr virus
- cerebrospinal fluid
- healthcare
- cognitive impairment
- multiple sclerosis
- induced apoptosis
- mental health
- mechanical ventilation
- early onset
- sars cov
- cystic fibrosis
- climate change
- drug induced
- artificial intelligence
- cell death
- mycobacterium tuberculosis
- big data
- pain management
- signaling pathway
- deep learning
- diffuse large b cell lymphoma
- multidrug resistant
- health information
- binding protein
- gram negative
- temporal lobe epilepsy