Abstract:

Objective: To investigate risk factors and develop a prediction model for chronic obstructive pulmonary disease (COPD) combined with pulmonary tuberculosis (PTB) using propensity score analysis. Methods: A total of 150 patients with COPD and PTB admitted to the Baoding People’s Hospital from July 2020 to July 2023 were selected as observation group, and 100 patients with only COPD admitted to the hospital during the same period were selected as control group. Relevant factors that might affect COPD combined with PTB were collected, and propensity score matching method was used to match gender, age, and place of residence of the patients, with a matching ratio of 1∶2. Univariable and multivariable logistic regression analysis was used to screen out independent risk factors affecting COPD combing PTB, and risk weights were obtained according to regression coefficients of risk factors to construct a prediction model, and then a ROC curve was drawn, and the value of the prediction model was evaluated by area under the curve (AUC). Results: Multivariable logistic regression analysis revealed that a history of smoking (OR=2.038, 95%CI: 1.119-3.713), lack of BCG vaccination (OR=1.714, 95%CI: 1.283-2.291), tuberculosis history (OR=2.795, 95%CI: 1.723-4.536), use of inhaled glucocorticoids (OR=2.083, 95%CI: 1.367-3.175), dust exposure (OR=2.109, 95%CI: 1.333-3.336), and poor nutritional status (OR=2.815, 95%CI: 1.755-4.515) were independent risk factors for COPD combined with PTB. AUC of ROC curve for the prediction model was 0.811 (95%CI: 0.762-0.894), with a sensitivity and specificity of 80.67% and 73.59%, respectively. Conclusion: Propensity score matching analysis indicates that a history of smoking, lack of BCG vaccination, tuberculosis history, use of inhaled glucocorticoids, dust exposure, and poor nutritional status are independent risk factors for COPD combined with PTB. The prediction model constructed based on these risk factors demonstrates a good prediction value for COPD combined with PTB.

Key words: Lung diseases, obstructive, Tuberculosis, pulmonary, Risk factors, Forecasting, Models, structural