Validation of Predictive Models for Autoimmune Encephalitis-Related Antibodies to Cell-Surface Proteins Expressed in Neurons: A Retrospective Study Based in a Hospital

Objective: Autoimmune encephalitis (AE) is a severe but treatable autoimmune disorder that is diagnosed by antibody (Ab) testing. However, it is unrealistic to obtain an early diagnosis in some areas since the Ab status cannot be immediately determined due to time and technology restrictions. In our study, we aimed to validate the Antibody Prevalence in Epilepsy and Encephalopathy (APE(2)) score among patients diagnosed with possible AE as a predictive model to screen AE patients with antibodies to cell-surface proteins expressed in neurons. Methods: A total of 180 inpatients were recruited, and antibodies were detected through serological and/or cerebrospinal fluid (CSF) evaluations. The APE(2) score was used to validate the predictive models of AE with autoantibodies. Results: The mean APE(2) score in the Ab-positive cases was 7.25, whereas the mean APE(2) score in the Ab-negative cases was 3.18 (P < 0.001). The APE(2) score had a receiver operating characteristic (ROC) area under the curve of 0.924 [P < 0.0001, 95% confidence interval (CI) = 0.875–0.973]. With a cutoff score of 5, the APE(2) score had the best psychometric properties, with a sensitivity of 0.875 and a specificity of 0.791. Conclusion: The APE(2) score is a predictive model for AE with autoantibodies to cell-surface proteins expressed in neurons and was validated and shown to have high sensitivity and specificity in our study. We suggest that such a model should be used in patients with a suspected diagnosis of AE, which could increase the detection rate of Abs, reduce testing costs, and help patients to benefit from treatment quickly.