Development of a nomogram-based risk prediction model for chronic obstructive pulmonary disease incidence in community-dwelling population aged 40 years and above in Shanghai
10.19428/j.cnki.sjpm.2025.250028
- VernacularTitle:上海市40岁及以上社区人群慢性阻塞性肺疾病发病列线图风险预测模型构建
- Author:
Yixuan ZHANG
1
;
Yiling WU
2
;
Jinxin ZANG
1
;
Xuyan SU
2
;
Xin YIN
1
;
Jing LI
3
;
Wei LUO
2
;
Minjun YU
4
;
Wei WANG
5
;
Qi ZHAO
1
;
Qin WANG
6
;
Genming ZHAO
1
;
Yonggen JIANG
2
;
Na WANG
1
Author Information
1. School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai 20032, China
2. Songjiang District Center for Disease Control and Prevention, Shanghai 201600, China
3. Songjiang District Zhongshan Street Community Health Service Center, Shanghai 201600, China
4. Songjiang District Maogang Town Community Health Service Center, Shanghai 201600, China
5. Songjiang District Xinqiao Town Community Health Service Center, Shanghai 201600, China
6. Songjiang District Sheshan Town Community Health Service Center, Shanghai 201600, China
- Publication Type:Journal Article
- Keywords:
chronic obstructive pulmonary disease;
prediction model;
nomogram;
cohort study;
community-based population
- From:
Shanghai Journal of Preventive Medicine
2025;37(8):669-675
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo develop a nomogram-based risk prediction model for chronic obstructive pulmonary disease (COPD) incidence among the community-dwelling population aged 40 years old and above, so as to provide targeted references for the screening and prevention of COPD. MethodsBased on a natural population cohort in suburban Shanghai, a total of 3 381 randomly selected participants aged ≥40 years underwent pulmonary function tests between July and October 2021. Cox stepwise regression analysis was used to develop overall and gender-specific risk prediction models, along with the construction of corresponding risk nomograms. Model predictive performance was evaluated using the C-indice, area under the curve (AUC) values, and Brier score. Stability was assessed through 10-fold cross-validation and sensitivity analysis. ResultsA total of 3 019 participants were included, with a median follow-up duration of 4.6 years. The COPD incidence density was 17.22 per 1 000 person-years, significantly higher in males (32.04/1 000 person-years) than that in females (7.38/1 000 person-years) (P<0.001). The overall risk prediction model included the variables such as gender, age, education level, BMI, smoking, passive smoking, and respiratory comorbidities. The male-specific model incorporated the variables such as age, BMI, respiratory comorbidities, and smoking, while the female-specific model included age, marital status, respiratory comorbidities, and pulmonary tuberculosis history. The C-indices for the overall, male-specific, and female-specific models were 0.829, 0.749, and 0.807, respectively. The 5-year AUC values were 0.785, 0.658, and 0.811, with Brier scores of 0.103, 0.176, and 0.059, respectively. Both 10-fold cross-validated C-indices and sensitivity analysis (excluding participants with a follow-up duration of <6 months) yielded C-indices were above 0.740. ConclusionThis study developed concise and practical overall and gender-specific COPD risk prediction models and corresponding nomograms. The models demonstrated robust performance in predicting COPD incidence, providing a valuable reference for identifying high-risk populations and formulating targeted screening and personalized management strategies.
