Volume 23 Issue 6
Jun.  2025
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Article Navigation >  Chinese Journal of General Practice  > 2025  >  23(6): 1049-1055
ZHOU Zhongyun, MA Wenlu, DU Xiaolin. Establishment and evaluation of a model for predicting the risk of hypertension among railroad workers[J]. Chinese Journal of General Practice, 2025, 23(6): 1049-1055. doi: 10.16766/j.cnki.issn.1674-4152.004063
Citation: ZHOU Zhongyun, MA Wenlu, DU Xiaolin. Establishment and evaluation of a model for predicting the risk of hypertension among railroad workers[J]. Chinese Journal of General Practice, 2025, 23(6): 1049-1055. doi: 10.16766/j.cnki.issn.1674-4152.004063
shu

Establishment and evaluation of a model for predicting the risk of hypertension among railroad workers

doi: 10.16766/j.cnki.issn.1674-4152.004063

  • Department of Cardiology, the Second Affiliated Hospital of Shandong University of TCM, Jinan, Shandong 250001, China

Funds:

 Z-2022051

  • Received Date: 2024-06-08
    Available Online: 2025-09-04
    Abstract
  •   Objective  To analyze the risk factors of hypertension among railroad workers and to establish an individualized risk prediction model for hypertension.  Methods  A total of 2 717 railroad workers who underwent the physical examination in the Second Affiliated Hospital of Shandong University of TCM from January 2022 to December 2022 were enrolled in this study, Data collection was done through face-to-face questionnaires, on-site physical examinations, and laboratory tests. According to the ratio of 7∶3, they were randomly divided into a modeling group and a validation group. After preliminary screening by univariate analysis and LASSO regression, the predictive variables were determined by multivariate analysis, and a nomogram model of hypertension was constructed. The predictive models were evaluated using the ROC curve, calibration curve, and decision curve analysis (DCA).  Results  A total of 2 717 railroad workers were enrolled in this study, and 951 railroad workers had hypertension, the incidence of hypertension was 35.0%, After a univariate and multivariate analysis, age, lack of exercise, loss of sleep, high salt diet, family history of hypertension, BMI, heart rate (HR), triglyceride (TG)were included in prediction model. The model showed a better performance, the area under the ROC curve was 0.844 (95% CI: 0.825-0.860), and the AUC for the validation group was 0.869 (95% CI: 0.845-0.896). The calibration curve showed a good agreement. DCA analysis showed that the modeling and validation groups showed large positive yields.  Conclusion  We developed a nomogram for predicting the risk of hypertension among railroad workers, which can be used as a tool to guide future patients.

     

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