Potential pre-COPD indicators in association with COPD development and COPD prediction models in Chinese: a prospective cohort study

Background Lung injury might take place before chronic obstructive pulmonary disease (COPD) occurs. A clearer definition of "pre-COPD" based on the effects of potential indicators on increasing risk of COPD development and a prediction model involving them are lacking. Methods A total of 3526 Chinese residents without COPD aged 40 years or older derived from the national cross-sectional survey of COPD surveillance in 2014-2015 were followed up for a mean of 3.59 years. We examined the associations of chronic bronchitis, preserved ratio impaired spirometry (PRISm), low peak expiratory flow (PEF), spirometric small airway dysfunction (sSAD), low maximal mid-expiratory flow (MMEF), low forced expiratory flow 50% of pulmonary volume (FEF50), and low FEF75 with subsequent COPD and constructed a prediction model with LASSO-Cox regression. Findings 235 subjects in the cohort developed COPD during the follow-up. Subjects with PRISm, low PEF, sSAD, low MMEF, low FEF50, and low FEF75 had an increased risk of developing COPD (adjusted hazard ratio [HR] ranging from 1.57 to 3.01). Only chronic bronchitis (HR 2.84 [95% CI 1.38-5.84] and 2.94 [1.43-6.04]) and sSAD/low MMEF (HR 2.74 [2.07-3.61] and 2.38 [1.65-3.43]) showed effects independent of the other indicators and their concurrence had the strongest effect (HR 5.89 and 4.80). The prediction model including age, sex, low MMEF, low FEF50, and indoor exposure to biomass had good performance both internally and temporally. The corrected C-index was 0.77 (0.72-0.81) for discrimination in internal validation. For temporal validation, the area under the receiver operating characteristic curve was 0.73 (0.63-0.83). Good calibration was indicated in plot for internal validation and by Hosmer-Lemeshow test for temporal validation. Interpretation Individuals with concurrent chronic bronchitis and sSAD/low MMEF indicating pre-COPD optimally require more high attention from physicians. Our prediction model could serve as a multi-dimension tool to predict COPD comprehensively.