| 要約: | BackgroundThe systemic inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR) and platelet-to-lymphocyte ratio (PLR) are inflammatory markers in peripheral blood, which have been proven to be associated with some central nervous system diseases. We aimed to evaluate the association of SII, NLR MLR and PLR with the severity of autoimmune encephalitis (AE) and to compare the predictive value of those biomarkers in the early identification of ICU admission.MethodsThis retrospective study was conducted in three medical centers in China. We included 176 patients diagnosed with AE and 200 age and gender-matched healthy controls and correlated their demographic and clinical data. The SII, NLR, MLR and PLR levels were calculated from the blood routine tests. The severity of the patients was evaluated by the Clinical Assessment Scale for Autoimmune Encephalitis (CASE) and the modified Rankin Scale (mRS) at admission, and the patients were divided into two groups according to the ICU admission.ResultsThe SII, NLR, MLR and PLR were significantly higher in AE patients than that in HCs (<0.001 for all). The SII and NLR were positively correlated with the CASE score (r = 0.243, p = 0.001; r = 0.237, p = 0.002) and the mRS score (r = 0.185, p = 0.014; r = 0.185, p = 0.014) in AE patients. The MLR and PLR were only positively correlated with the CASE score (r = 0.242, p = 0.001; r = 0.158, p = 0.036). The SII and NLR of the ICU group were significantly higher than that of the non-ICU group. The result of receiver operating characteristic (ROC) analysis showed that NLR was the best predictor of ICU admission for AE patients (AUC = 0.701). NLR and MLR had similar predictive ability (AUC = 0.654; AUC = 0.608) and were superior to PLR. The optimal NLR cut-off value for the incidence of ICU was 3.906.ConclusionIncreased SII, NLR, MLR and PLR at admission are positively correlated with the CASE score of AE patients. Among the four indexes, the NLR is the best predictor of ICU admission, which may be helpful for clinicians to monitor disease progression and identify potentially severe patients of AE.
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