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已发表论文
慢性阻塞性肺疾病患者持续戒烟预测模型的构建与验证
Authors Tong H, Tian Z , Zhang N , Liu X, Zhu H , Jing L, Wang L
Received 20 July 2025
Accepted for publication 31 December 2025
Published 12 January 2026 Volume 2026:21 554995
DOI https://doi.org/10.2147/COPD.S554995
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Vanesa Bellou
Huimin Tong,1 Zheng Tian,2 Nan Zhang,3 Xinyi Liu,4 Hongyi Zhu,4 Liwei Jing,2 Lan Wang4
1Department of Respiratory and Critical Care Medicine, Tianjin Fourth Central Hospital, Tianjin, 300000, People’s Republic of China; 2School of Nursing, Capital Medical University, Beijing, 100069, People’s Republic of China; 3Department of Ophthalmology, Peking University People’s Hospital, Beijing, 100044, People’s Republic of China; 4School of Nursing, Tianjin Medical University, Tianjin, 300070, People’s Republic of China
Correspondence: Liwei Jing, Capital Medical University, 10 Xitoutiao, You’anmenwai, Fengtai District, Beijing, 100069, People’s Republic of China, Email lwjing2004@ccmu.edu.cn Lan Wang, Tianjin Medical University, 22 Qixiangtai Road, Heping District, Tianjin, 300070, People’s Republic of China, Email wangl0423@tmu.edu.cn
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
1Department of Respiratory and Critical Care Medicine, Tianjin Fourth Central Hospital, Tianjin, 300000, People’s Republic of China; 2School of Nursing, Capital Medical University, Beijing, 100069, People’s Republic of China; 3Department of Ophthalmology, Peking University People’s Hospital, Beijing, 100044, People’s Republic of China; 4School of Nursing, Tianjin Medical University, Tianjin, 300070, People’s Republic of China
Correspondence: Liwei Jing, Capital Medical University, 10 Xitoutiao, You’anmenwai, Fengtai District, Beijing, 100069, People’s Republic of China, Email lwjing2004@ccmu.edu.cn Lan Wang, Tianjin Medical University, 22 Qixiangtai Road, Heping District, Tianjin, 300070, People’s Republic of China, Email wangl0423@tmu.edu.cn
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