Xvwen Zhai1, Min Feng2, Hui Guo3,4, Zhaojun Liang2, Yanlin Wang2, Yan Qin2, Yanyao Wu2, Xiangcong Zhao2, Chong Gao5, Jing Luo2. 1. Clinical Skills Teaching Simulation Hospital, Shanxi Medical University, Jinzhong, China. 2. Department of Rheumatology, The Second Hospital of Shanxi Medical University, Taiyuan, China. 3. Division of Nephrology, Department of Medicine, The Second Hospital of Shanxi Medical University, Taiyuan, China. 4. Division of Nephrology, Department of Medicine, The Shenzhen Baoan Shiyan People's Hospital, Shenzhen, China. 5. Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States.
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.
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: SLEpatients 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-infectedpatients, 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 SLEpatients 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.
Authors: Y Tang; C Liao; X Xu; H Song; S Shi; S Yang; F Zhao; W Xu; X Chen; J Mao; L Zhang; B Pan Journal: Clin Microbiol Infect Date: 2011-04-04 Impact factor: 8.067
Authors: Maria E Santolaya; Ana M Alvarez; Carmen L Aviles; Ana Becker; Alejandra King; Claudio Mosso; Miguel O'Ryan; Ernesto Paya; Carmen Salgado; Pamela Silva; Santiago Topelberg; Juan Tordecilla; Monica Varas; Milena Villarroel; Tamara Viviani; Marcela Zubieta Journal: Pediatr Infect Dis J Date: 2008-06 Impact factor: 2.129