ArticleInternational journal of chronic obstructive pulmonary disease2026
Construction and Validation of a Prediction Model for Sustained Smoking Cessation in Patients with Chronic Obstructive Pulmonary Disease.
Article in International journal of chronic obstructive pulmonary disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Clinical Characteristics and Factors Associated with One-Year Smoking Cessation Among Smokers with COPD: A Subgroup Analysis of a Multicentre Randomised Trial.International journal of chronic obstructive pulmonary disease · 2026Trial
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To identify factors associated with smoking relapse or non-attempt within one year in COPD patients and to develop a predictive model for early identification of high-risk individuals to guide targeted interventions. Methods: Based on the health ecology model, a questionnaire integrating factors affecting smoking cessation was developed. We enrolled 221 COPD patients from a tertiary hospital in Tianjin and categorized them into smoking cessation success or failure groups. Mann-Whitney Results: Among 221 patients, 92 successfully quit smoking and 129 failed. Multivariate analysis identified age (OR = 0.922, P < 0.001), GOLD grade (OR = 0.257, P < 0.001), and death anxiety score (OR = 0.930, P = 0.001) as protective factors against cessation failure, while depression score (OR = 1.107, P < 0.001) and quit-smoking partner complaints score (OR = 1.075, P < 0.001) were risk factors. The prediction model demonstrated good discrimination (C-index = 0.876) and calibration (Hosmer-Lemeshow test P = 0.350). DCA and CIC confirmed the model's clinical utility. Conclusion: Younger age, mild/moderate GOLD grade, higher depression score, lower death anxiety, and higher partner complaints increase the risk of smoking cessation failure in COPD patients. The developed model facilitates early identification of high-risk patients for targeted intervention to improve quit rates.
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