[Predictive risk analysis for pneumoconiosis combined with tuberculosis].

Objective

To explore the risk factors of pneumoconiosis complicated with pulmonary tuberculosis, to construct a clinical prediction model for patients with pneumoconiosis complicated with pulmonary tuberculosis, and to provide a scientific basis for the prevention of pneumoconiosis complicated with pulmonary tuberculosis.

Methods

In January 2024, a total of 232 patients with pneumoconiosis (including coal workers' pneumoconiosis and silicosis) who were treated in the Department of Respiratory and Critical Care Medicine of the Third People's Hospital of Xinjiang Uygur Autonomous Region (Xinjiang Uygur Autonomous Region Occupational Disease Hospital) from January 2022 to January 2023 were randomly selected as the study subjects. Collectted basic patient information and diagnostic data. Multivariate logistic regression analysis was used to screen the risk factors related to pneumoconiosis complicated with pulmonary tuberculosis. According to the results of multivariate logistic regression analysis, a nomogram was established, and the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve and decision curve analysis (DCA) were used to evaluate the predictive ability.

Results

Among the 232 patients with pneumoconiosis, 73 were complicated with pulmonary tuberculosis, accounting for 31.47% (73/232). Multivariate logistic regression analysis determined that dust exposure time, type of work, smoking history, and lung function level were all risk factors for pneumoconiosis complicated with tuberculosis (OR=10.33, 95%CI=1.92~55.66, OR=5.43, 95% CI=1.91~15.44, OR=3.10, 95% CI=1.15~8.37, OR=4.00, 95% CI=1.62~9.87; P<0.05). The constructed nomogram model has good clinical applicability when the area under the receiver operating characteristic (ROC) curve is 0.77 [95% CI (0.69, 0.73) ], the calibration curve is close to the ideal diagonal, the absolute error between the simulation curve and the actual curve is 0.03, and the DCA decision curve shows that the probability threshold of the nomogram model is 1%-90%.

Conclusion

The risk of pneumoconiosis complicated with tuberculosis is high, and the risk factors of dust exposure time, smoking history, type of work and lung function level are high. This nomogram model can be used to predict the risk of pulmonary tuberculosis in patients with pneumoconiosis, which is helpful for early intervention.