Prediction of COPD risk accounting for time-varying smoking exposures

• Published in: PloS one Vol. 16; no. 3; p. e0248535
• Main Authors: Chang, Joanne T, Meza, Rafael, Levy, David T, Arenberg, Douglas, Jeon, Jihyoun
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 10.03.2021; Public Library of Science (PLoS)
• Subjects: Adult; Age; Aged; Air pollution; Asthma; Biology and Life Sciences; Cancer; Chemical pollution; Chronic illnesses; Chronic obstructive pulmonary disease; Disease; Editing; Epidemiology; Exposure; Female; Follow-Up Studies; Fumes; Funding; Health aspects; Health hazards; Health risks; Humans; Lung diseases; Lung diseases, Obstructive; Male; Males; Medical personnel; Medicine; Medicine and Health Sciences; Methodology; Middle Aged; Models, Biological; Obstructive lung disease; Outdoor air quality; People and Places; Physical Sciences; Physiological aspects; Predictive Value of Tests; Public health; Pulmonary Disease, Chronic Obstructive - epidemiology; Pulmonary Disease, Chronic Obstructive - genetics; Questionnaires; Regression analysis; Regression coefficients; Research and Analysis Methods; Reviews; Risk analysis; Risk Factors; Smoking; Smoking - adverse effects; Smoking - epidemiology; Social Sciences; Statistical models; Time dependence; Trypsin; Visualization; United States; Ann Arbor Michigan; United States > US; Michigan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0248535

Chronic Obstructive Pulmonary Disease (COPD) is the fourth leading cause of death in the United States. Studies have primarily assessed the relationship between smoking on COPD risk focusing on summary measures, like smoking status. Develop a COPD risk prediction model incorporating individual time-varying smoking exposures. The Nurses' Health Study (N = 86,711) and the Health Professionals Follow-up Study (N = 39,817) data was used to develop a COPD risk prediction model. Data was randomly split in 50-50 samples for model building and validation. Cox regression with time-varying covariates was used to assess the association between smoking duration, intensity and year-since-quit and self-reported COPD diagnosis incidence. We evaluated the model calibration as well as discriminatory accuracy via the Area Under the receiver operating characteristic Curve (AUC). We computed 6-year risk of COPD incidence given various individual smoking scenarios. Smoking duration, year-since-quit (if former smokers), sex, and interaction of sex and smoking duration are significantly associated with the incidence of diagnosed COPD. The model that incorporated time-varying smoking variables yielded higher AUCs compared to models using only pack-years. The AUCs for the model were 0.80 (95% CI: 0.74-0.86) and 0.73 (95% CI: 0.70-0.77) for males and females, respectively. Utilizing detailed smoking pattern information, the model predicts COPD risk with better accuracy than models based on only smoking summary measures. It might serve as a tool for early detection programs by identifying individuals at high-risk for COPD.