Abstract
Background:
Early diagnosis and treatment for encephalitis are crucial for improving patient outcomes and reducing the economic burden, especially given the overlapping symptoms and low specificity of auxiliary diagnostic tests between viral encephalitis (VE) and autoimmune encephalitis (AE). Since these two conditions require different treatment approaches, an early differential diagnosis between AE and VE is a critical challenge.
Methods:
This study enrolled a cohort of 75 patients (38 with VE and 37 with AE) between September 2022 and July 2024. The demographic data, clinical characteristics, and laboratory test results were collected. The expression levels of co-stimulatory molecules were detected by flow cytometry and enzyme-linked immunosorbent assay within 7 days for viral encephalitis and 90 days for autoimmune encephalitis in the early phase of the disease. Differential analysis, logistic regression analysis, and least absolute shrinkage and selection operator regression were employed for model construction. Finally, a nomogram and a receiver operating characteristic (ROC) curve were developed to visualize the model and evaluate its predictive accuracy.
Results:
Upon analyzing the collected data, a model for the early differential diagnosis between AE and VE was eventually established. This comprehensive model incorporated 10 variables: serum creatinine and chloride levels, the percentage of peripheral blood CD4+ICOS+ and CD19+PD-L1+, plasma soluble inducible costimulatory ligand (sICOSL), cerebrospinal fluid (CSF) glucose content, and the presence of fever, nausea, vomiting, headaches, and cognitive impairment. Patients with creatinine <60.75 (μmol/L), chloride <106.25 (mmol/L), CD4+ICOS+ ≥11.2%, CD19+PD-L1+ ≥12.35%, plasma sICOSL≥286.37 ng/mL, CSF sugar content ≥3.775 (mmol/L), and those with cognitive impairment are more likely to be diagnosed with AE. The area under the curve (AUC)-ROC of our model was 0.942 [95% confidence interval (CI): 0.887-0.997], with a sensitivity of 0.844 and a specificity of 0.971, indicating strong diagnostic performance.
Conclusion:
This diagnostic model offers a convenient tool for distinguishing AE from VE in the early phase, facilitating early diagnosis and treatment, improving patient prognosis, and reducing financial burdens.