Clinical characteristics of patients with autoimmune encephalitis and related factors of social frailty
10.3969/j.issn.1006-2483.2026.04.032
- VernacularTitle:自身免疫性脑炎患者临床特点及社会衰弱发生的相关因素
- Author:
Yue XU
1
;
Yang XIANG
1
;
Jing WANG
1
Author Information
1. Department of Neurology, Nanjing Brain Hospital, Nanjing , Jiangsu 210000, China
- Publication Type:Journal Article
- Keywords:
Autoimmune encephalitis;
Social frailty;
Predictors
- From:
Journal of Public Health and Preventive Medicine
2026;37(4):155-159
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the clinical characteristics of autoimmune encephalitis (AE) and the related factors of social frailty. Methods A total of 412 patients with AE admitted to the hospital between January 2020 and August 2025 were collected as research subjects. According to the occurrence status of social frailty, the above patients were classified into social frailty group (n=166) and control group (n=246). The clinical data of patients were analyzed, Multivariate logistic regression analysis was used to analyze the related factors of social frailty in patients with AE.. Results Among the 412 patients, 166 cases (40.29%) had social frailty and 246 cases (59.71%) did not have social frailty. The proportions of age≥40 years old, language disorder, epileptic seizure, acute onset and abnormal cranial magnetic resonance imaging (MRI) in the social frailty group were higher than those in the control group (Multivariate logistic regression analysis was used to analyze the related factors of social frailty in patients with AE.P<0.05). Age≥40 years old, language disorder, acute onset, Abnormal cranial magnetic resonance imaging is a related factor for the occurrence of social frailty in patients with AE (P<0.05). The area under the curve of the logistic regression model for prediction was 0.851, suggesting that the model had good predictive value. Conclusion Age, language disorder, epileptic seizure, acute onset and cranial MRI abnormality are important risk factors of social frailty in patients with AE. The prediction model based on these factors exhibits good clinical application value.
