ArticleFrontiers in cellular and infection microbiology2021
Development of Prediction Models for New Integrated Models and a Bioscore System to Identify Bacterial Infections in Systemic Lupus Erythematosus.
Article in Frontiers in cellular and infection microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 7 citations in OpenAlex.
- Development and temporal validation of a clinical prediction model to identify bacterial infection in hospitalised patients with systemic lupus erythematosus.Lupus science & medicine · 2026Article
- Comprehensive analysis of predictive models for disease manifestations and case fatality in systemic lupus erythematosus.NPJ digital medicine · 2026Review
- Machine learning-based prediction of 30-day unplanned readmission risk in day surgery lung cancer patients after lobectomy or sublobectomy: a real-world study.Frontiers in medicine · 2026Article
- Discriminating Disease Flare From Infection in Febrile Patients With Systemic Lupus Erythematosus in a Safety-Net Hospital System: A Multicenter Study.ACR open rheumatology · 2025Article
- Bacterial infection in patients with juvenile systemic lupus erythematosus and fever.Pediatric rheumatology online journal · 2025Article
- Procalcitonin for the early discrimination of fever etiology in patients with systemic autoimmune diseases attending the emergency department.Internal and emergency medicine · 2023Article
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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