ArticleInternational journal of chronic obstructive pulmonary disease2024
The Incidence, Risk Factors, and Predictive Model of Obstructive Disease in Post-Tuberculosis Patients.
Article in International journal of chronic obstructive pulmonary disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Comorbidity of chronic obstructive pulmonary disease and pulmonary tuberculosis: a bibliometric analysis (2011-2025) with narrative review.Journal of thoracic disease · 2026Article
- Risk Factors and Prediction of Chronic Obstructive Pulmonary Disease After Pulmonary Tuberculosis: A Prospective Cohort Study.International journal of chronic obstructive pulmonary disease · 2026Article
- Participation of miRNA-23a-3p in Pulmonary Tuberculosis Through Macrophages via the JAK-STAT Pathway.International journal of chronic obstructive pulmonary disease · 2026Article
- Efficacy and prognostic analysis of bronchoscopic intervention in elderly patients with tracheobronchial tuberculosis.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Objective: To assess the incidence and risk factors of tuberculosis-associated obstructive pulmonary disease (TOPD) in individuals with treatment-naive pulmonary tuberculosis (PTB) and develop a predictive model to enhance its management. Methods: The incidence of TOPD among patients with treatment-naive PTB in Xinjiang, China, was followed up for one year. Patient characteristics, such as demographics, medical histories, laboratory test results, lung radiological evidence, and pulmonary function, were collected upon hospital admission and throughout follow-up visits. Risk factors associated with TOPD were evaluated by multivariate logistic regression analysis, and then a predictive model was established using LASSO regression. Results: Of the 159 included patients, 69 (43.4%) developed TOPD during the follow-up period. Multivariate regression analysis identified age, body mass index, ESR, and symptom duration as significant risk factors. Subsequently, a model formula was derived from these factors to predict TOPD. Utilizing a cut-off value of 0.435, the model demonstrated a sensitivity of 89% and a specificity of 83%. Conclusion: In Xinjiang, the prevalence of TOPD appears notably high among treatment-naive PTB patients. Our findings, such as the risk factors and predictive model, may facilitate the early detection and improved interventions for TOPD among patients with PTB, potentially leading to better patient outcomes.
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