Development of a predictive model for systemic lupus erythematosus incidence risk based on environmental exposure factors
Objective Systemic lupus erythematosus (SLE) is an autoimmune disease characterised by a loss of immune tolerance, affecting multiple organs and significantly impairing patients’ health and quality of life. While hereditary elements are essential in the onset of SLE, external environmental influence...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2024-11-01
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| Series: | Lupus Science and Medicine |
| Online Access: | https://lupus.bmj.com/content/11/2/e001311.full |
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| author | Qianjin Lu Ying Zhang Cheng Zhao Yu Lei Hui Jin Qilin Li |
| author_facet | Qianjin Lu Ying Zhang Cheng Zhao Yu Lei Hui Jin Qilin Li |
| author_sort | Qianjin Lu |
| collection | DOAJ |
| description | Objective Systemic lupus erythematosus (SLE) is an autoimmune disease characterised by a loss of immune tolerance, affecting multiple organs and significantly impairing patients’ health and quality of life. While hereditary elements are essential in the onset of SLE, external environmental influences are also significant. Currently, there are few predictive models for SLE that takes into account the impact of occupational and living environmental exposures. Therefore, we collected basic information, occupational background and living environmental exposure data from patients with SLE to construct a predictive model that facilitates easier intervention.Methods We conducted a study comparing 316 individuals diagnosed with SLE and 851 healthy volunteers in a case–control design, collecting their basic information, occupational exposure history and environmental exposure data. Subjects were randomly allocated into training and validation groups using a 70/30 split. Using three-feature selection methods, we constructed four predictive models with multivariate logistic regression. Model performance and clinical utility were evaluated via receiver operating characteristic, calibration and decision curves. Leave-one-out cross-validation further validated the models. The best model was used to create a dynamic nomogram, visually representing the predicted relative risk of SLE onset.Results The ForestMDG model demonstrated strong predictive ability, with an area under the curve of 0.903 (95% CI 0.880 to 0.925) in the training set and 0.851 (95% CI 0.809 to 0.894) in the validation set, as indicated by model performance evaluation. Calibration and decision curves demonstrated accurate results along with practical clinical value. Leave-one-out cross-validation confirmed that the ForestMDG model had the best accuracy (0.8338). Finally, we developed a dynamic nomogram for practical use, which is accessible via the following link: https://yingzhang99321.shinyapps.io/dynnomapp/.Conclusion We created a user-friendly dynamic nomogram for predicting the relative risk of SLE onset based on occupational and living environmental exposures.Trial registration number ChiCTR2000038187. |
| format | Article |
| id | doaj-art-bc80dda207f24ba8a2ff651f2664fb8b |
| institution | DOAJ |
| issn | 2053-8790 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | Lupus Science and Medicine |
| spelling | doaj-art-bc80dda207f24ba8a2ff651f2664fb8b2025-08-20T02:49:02ZengBMJ Publishing GroupLupus Science and Medicine2053-87902024-11-0111210.1136/lupus-2024-001311Development of a predictive model for systemic lupus erythematosus incidence risk based on environmental exposure factorsQianjin Lu0Ying Zhang1Cheng Zhao2Yu Lei3Hui Jin4Qilin Li5Hospital for Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, ChinaDepartment of Epidemiology and Biostatistics, Nanjing Medical University, Nanjing, ChinaHunan Key Laboratory of Medical Epigenomics, Department of Dermatology, The Second Xiangya Hospital of Central South University, Changsha, ChinaHospital for Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, ChinaHospital for Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, ChinaHospital for Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, ChinaObjective Systemic lupus erythematosus (SLE) is an autoimmune disease characterised by a loss of immune tolerance, affecting multiple organs and significantly impairing patients’ health and quality of life. While hereditary elements are essential in the onset of SLE, external environmental influences are also significant. Currently, there are few predictive models for SLE that takes into account the impact of occupational and living environmental exposures. Therefore, we collected basic information, occupational background and living environmental exposure data from patients with SLE to construct a predictive model that facilitates easier intervention.Methods We conducted a study comparing 316 individuals diagnosed with SLE and 851 healthy volunteers in a case–control design, collecting their basic information, occupational exposure history and environmental exposure data. Subjects were randomly allocated into training and validation groups using a 70/30 split. Using three-feature selection methods, we constructed four predictive models with multivariate logistic regression. Model performance and clinical utility were evaluated via receiver operating characteristic, calibration and decision curves. Leave-one-out cross-validation further validated the models. The best model was used to create a dynamic nomogram, visually representing the predicted relative risk of SLE onset.Results The ForestMDG model demonstrated strong predictive ability, with an area under the curve of 0.903 (95% CI 0.880 to 0.925) in the training set and 0.851 (95% CI 0.809 to 0.894) in the validation set, as indicated by model performance evaluation. Calibration and decision curves demonstrated accurate results along with practical clinical value. Leave-one-out cross-validation confirmed that the ForestMDG model had the best accuracy (0.8338). Finally, we developed a dynamic nomogram for practical use, which is accessible via the following link: https://yingzhang99321.shinyapps.io/dynnomapp/.Conclusion We created a user-friendly dynamic nomogram for predicting the relative risk of SLE onset based on occupational and living environmental exposures.Trial registration number ChiCTR2000038187.https://lupus.bmj.com/content/11/2/e001311.full |
| spellingShingle | Qianjin Lu Ying Zhang Cheng Zhao Yu Lei Hui Jin Qilin Li Development of a predictive model for systemic lupus erythematosus incidence risk based on environmental exposure factors Lupus Science and Medicine |
| title | Development of a predictive model for systemic lupus erythematosus incidence risk based on environmental exposure factors |
| title_full | Development of a predictive model for systemic lupus erythematosus incidence risk based on environmental exposure factors |
| title_fullStr | Development of a predictive model for systemic lupus erythematosus incidence risk based on environmental exposure factors |
| title_full_unstemmed | Development of a predictive model for systemic lupus erythematosus incidence risk based on environmental exposure factors |
| title_short | Development of a predictive model for systemic lupus erythematosus incidence risk based on environmental exposure factors |
| title_sort | development of a predictive model for systemic lupus erythematosus incidence risk based on environmental exposure factors |
| url | https://lupus.bmj.com/content/11/2/e001311.full |
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