Statistical Modeling of Disease Progression for Chronic Obstructive Pulmonary Disease Using Data from the ECLIPSE Study
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abstract
Background. To develop statistical models predicting disease progression and outcomes in chronic obstructive pulmonary disease (COPD), using data from ECLIPSE, a large, observational study of current and former smokers with COPD. Methods. Based on a conceptual model of COPD disease progression and data from 2164 patients, associations were made between baseline characteristics, COPD disease progression attributes (exacerbations, lung function, exercise capacity, and symptoms), health-related quality of life (HRQoL), and survival. Linear and nonlinear functional forms of random intercept models were used to characterize these relationships. Endogeneity was addressed by time-lagging variables in the regression models. Results. At the 5% significance level, an exacerbation history in the year before baseline was associated with increased risk of future exacerbations (moderate: +125.8%; severe: +89.2%) and decline in lung function (forced expiratory volume in 1 second [FEV1]) (–94.20 mL per year). Each 1% increase in FEV1 % predicted was associated with decreased risk of exacerbations (moderate: –1.1%; severe: –3.0%) and increased 6-minute walk test distance (6MWD) (+1.5 m). Increases in baseline exercise capacity (6MWD, per meter) were associated with slightly increased risk of moderate exacerbations (+0.04%) and increased FEV1 (+0.62 mL). Symptoms (dyspnea, cough, and/or sputum) were associated with an increased risk of moderate exacerbations (+13.4% to +31.1%), and baseline dyspnea (modified Medical Research Council score ≥2 v. <2) was associated with lower FEV1 (–112.3 mL). Conclusions. A series of linked statistical regression equations have been developed to express associations between indicators of COPD disease severity and HRQoL and survival. These can be used to represent disease progression, for example, in new economic models of COPD.
