Development of Prediction Models for New Integrated Models and a Bioscore System to Identify Bacterial Infections in Systemic Lupus Erythematosus - Zhai, Xvwen; Feng, Min; Guo, Hui; Liang, Zhaojun; Wang, Yanlin; Qin, Yan; Wu, Yanyao; Zhao, Xiangcong; Gao, Chong; Luo, Jing (Frontiers in Cellular and Infection Microbiology, 2021)
Abstract Objectives: Distinguishing flares from bacterial infections in systemic lupus erythematosus (SLE) patients remains a challenge. This study aimed to build a model, using multiple blood cells and plasma indicators, to improve the identification of bacterial infections in SLE. Design: Building PLS-DA/OPLS-DA models and a bioscore system to distinguish bacterial infections from lupus flares in SLE. Setting: Department of Rheumatology of the Second Hospital of Shanxi Medical University. Participants: SLE patients with flares (n=142) or bacterial infections (n=106) were recruited in this retrospective study. Outcome: The peripheral blood of these patients was collected by the experimenter to measure the levels of routine examination indicators, immune cells, and cytokines. PLS-DA/OPLS-DA models and a bioscore system were established. Results: Both PLS-DA (R2Y=0.953, Q2=0.931) and OPLS-DA (R2Y=0.953, Q2=0.942) models could clearly identify bacterial infections in SLE. The white blood cell (WBC), neutrophile granulocyte (NEUT), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), IL-10, interferon-γ (IFN-γ) and tumor necrosis factor α (TNF-α) levels were significantly higher in bacteria-infected patients, while regulatory T (Treg) cells obviously decreased. A multivariate analysis using the above 10 dichotomized indicators, based on the cut-off value of their respective ROC curve, was established to screen out the independent predictors and calculate their weights to build a bioscore system, which exhibited a strong diagnosis ability (AUC=0.842, 95%CI 0.794-0.891). The bioscore system showed that 0% and 100% of SLE patients with scores of 0 and 8-10, respectively, were infected with bacteria. The higher the score, the greater the likelihood of bacterial infections in SLE. Conclusions: The PLS-DA/OPLS-DA models, including the above biomarkers, showed a strong predictive ability for bacterial infections in SLE. Combining WBC, NEUT, CRP, PCT, IL-6 and IFN-γ in a bioscore system may result in faster prediction of bacterial infections in SLE and may guide toward a more appropriate, timely treatment for SLE.