Development of Prediction Models for New Integrated Models and a Bioscore System to Identify Bacterial Infections in Systemic Lupus Erythematosus
ObjectivesDistinguishing 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.DesignBuilding PLS-DA/OPLS-D...
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| Format: | Article |
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Frontiers Media S.A.
2021-03-01
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| Series: | Frontiers in Cellular and Infection Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcimb.2021.620372/full |
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| author | Xvwen Zhai Min Feng Hui Guo Hui Guo Zhaojun Liang Yanlin Wang Yan Qin Yanyao Wu Xiangcong Zhao Chong Gao Jing Luo |
| author_facet | Xvwen Zhai Min Feng Hui Guo Hui Guo Zhaojun Liang Yanlin Wang Yan Qin Yanyao Wu Xiangcong Zhao Chong Gao Jing Luo |
| author_sort | Xvwen Zhai |
| collection | DOAJ |
| description | ObjectivesDistinguishing 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.DesignBuilding PLS-DA/OPLS-DA models and a bioscore system to distinguish bacterial infections from lupus flares in SLE.SettingDepartment of Rheumatology of the Second Hospital of Shanxi Medical University.ParticipantsSLE patients with flares (n = 142) or bacterial infections (n = 106) were recruited in this retrospective study.OutcomeThe 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.ResultsBoth 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.ConclusionsThe 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. |
| first_indexed | 2024-12-14T12:31:22Z |
| format | Article |
| id | doaj.art-fb9d2cc917f54596bf5e84033dfe7ef6 |
| institution | Directory Open Access Journal |
| issn | 2235-2988 |
| language | English |
| last_indexed | 2024-12-14T12:31:22Z |
| publishDate | 2021-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cellular and Infection Microbiology |
| spelling | doaj.art-fb9d2cc917f54596bf5e84033dfe7ef62022-12-21T23:01:10ZengFrontiers Media S.A.Frontiers in Cellular and Infection Microbiology2235-29882021-03-011110.3389/fcimb.2021.620372620372Development of Prediction Models for New Integrated Models and a Bioscore System to Identify Bacterial Infections in Systemic Lupus ErythematosusXvwen Zhai0Min Feng1Hui Guo2Hui Guo3Zhaojun Liang4Yanlin Wang5Yan Qin6Yanyao Wu7Xiangcong Zhao8Chong Gao9Jing Luo10Clinical Skills Teaching Simulation Hospital, Shanxi Medical University, Jinzhong, ChinaDepartment of Rheumatology, The Second Hospital of Shanxi Medical University, Taiyuan, ChinaDivision of Nephrology, Department of Medicine, The Second Hospital of Shanxi Medical University, Taiyuan, ChinaDivision of Nephrology, Department of Medicine, The Shenzhen Baoan Shiyan People’s Hospital, Shenzhen, ChinaDepartment of Rheumatology, The Second Hospital of Shanxi Medical University, Taiyuan, ChinaDepartment of Rheumatology, The Second Hospital of Shanxi Medical University, Taiyuan, ChinaDepartment of Rheumatology, The Second Hospital of Shanxi Medical University, Taiyuan, ChinaDepartment of Rheumatology, The Second Hospital of Shanxi Medical University, Taiyuan, ChinaDepartment of Rheumatology, The Second Hospital of Shanxi Medical University, Taiyuan, ChinaDepartment of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, United StatesDepartment of Rheumatology, The Second Hospital of Shanxi Medical University, Taiyuan, ChinaObjectivesDistinguishing 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.DesignBuilding PLS-DA/OPLS-DA models and a bioscore system to distinguish bacterial infections from lupus flares in SLE.SettingDepartment of Rheumatology of the Second Hospital of Shanxi Medical University.ParticipantsSLE patients with flares (n = 142) or bacterial infections (n = 106) were recruited in this retrospective study.OutcomeThe 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.ResultsBoth 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.ConclusionsThe 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.https://www.frontiersin.org/articles/10.3389/fcimb.2021.620372/fullsystemic lupus erythematosusbacterial infectionlupus flarereceiver operating characteristicbioscore |
| spellingShingle | Xvwen Zhai Min Feng Hui Guo Hui Guo Zhaojun Liang Yanlin Wang Yan Qin Yanyao Wu Xiangcong Zhao Chong Gao Jing Luo Development of Prediction Models for New Integrated Models and a Bioscore System to Identify Bacterial Infections in Systemic Lupus Erythematosus Frontiers in Cellular and Infection Microbiology systemic lupus erythematosus bacterial infection lupus flare receiver operating characteristic bioscore |
| title | Development of Prediction Models for New Integrated Models and a Bioscore System to Identify Bacterial Infections in Systemic Lupus Erythematosus |
| title_full | Development of Prediction Models for New Integrated Models and a Bioscore System to Identify Bacterial Infections in Systemic Lupus Erythematosus |
| title_fullStr | Development of Prediction Models for New Integrated Models and a Bioscore System to Identify Bacterial Infections in Systemic Lupus Erythematosus |
| title_full_unstemmed | Development of Prediction Models for New Integrated Models and a Bioscore System to Identify Bacterial Infections in Systemic Lupus Erythematosus |
| title_short | Development of Prediction Models for New Integrated Models and a Bioscore System to Identify Bacterial Infections in Systemic Lupus Erythematosus |
| title_sort | development of prediction models for new integrated models and a bioscore system to identify bacterial infections in systemic lupus erythematosus |
| topic | systemic lupus erythematosus bacterial infection lupus flare receiver operating characteristic bioscore |
| url | https://www.frontiersin.org/articles/10.3389/fcimb.2021.620372/full |
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