基于环境暴露因素的系统性红斑狼疮发病风险预测模型的建立。
Development of a predictive model for systemic lupus erythematosus incidence risk based on environmental exposure factors.
机构信息
Department of Epidemiology and Biostatistics, Nanjing Medical University, Nanjing, China.
Hospital for Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China.
出版信息
Lupus Sci Med. 2024 Nov 20;11(2):e001311. doi: 10.1136/lupus-2024-001311.
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.
目的
系统性红斑狼疮（SLE）是一种自身免疫性疾病，其特征是免疫耐受丧失，影响多个器官，并显著损害患者的健康和生活质量。虽然遗传因素是 SLE 发病的重要因素，但外部环境影响也很重要。目前，很少有预测 SLE 的模型考虑职业和生活环境暴露的影响。因此，我们收集了 SLE 患者的基本信息、职业背景和生活环境暴露数据，构建了一个预测模型，以便更容易进行干预。
方法
我们采用病例对照设计，比较了 316 名确诊为 SLE 的患者和 851 名健康志愿者，收集了他们的基本信息、职业暴露史和环境暴露数据。采用 70/30 的随机分组方法将受试者分为训练组和验证组。使用三种特征选择方法，我们使用多元逻辑回归构建了四个预测模型。通过接收者操作特征曲线、校准和决策曲线评估模型性能和临床实用性。留一法交叉验证进一步验证了模型。使用最优模型创建了一个动态列线图，直观地表示了 SLE 发病的预测相对风险。
结果
ForestMDG 模型具有很强的预测能力，在训练集的曲线下面积为 0.903（95%置信区间 0.880 至 0.925），在验证集的曲线下面积为 0.851（95%置信区间 0.809 至 0.894），这表明模型性能评估结果良好。校准和决策曲线表明结果准确，具有实际的临床价值。留一法交叉验证证实，ForestMDG 模型的准确性最高（0.8338）。最后，我们开发了一个实用的动态列线图，可通过以下链接访问：https://yingzhang99321.shinyapps.io/dynnomapp/。
结论
我们创建了一个基于职业和生活环境暴露的预测 SLE 发病相对风险的用户友好型动态列线图。
临床试验注册号
ChiCTR2000038187。
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