Construction and Validation of a Prediction Model for Sustained Smoking Cessation in Patients with Chronic Obstructive Pulmonary Disease

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 U -tests, χ2 -tests, and logistic regression were used to identify predictors. A nomogram prediction model was developed using significant factors. Model performance was evaluated via calibration plot, Hosmer-Lemeshow test, concordance index (C-index), decision curve analysis (DCA), and clinical impact curve (CIC). 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. Keywords: chronic obstructive pulmonary disease, smoking cessation, prediction model, nomogram