Predicting the Onset of Chronic Obstructive Pulmonary Disease in the English Longitudinal Study of Ageing

Chronic obstructive pulmonary disease (COPD) is a chronic lung disease estimated to be responsible of about 5% of all deaths worldwide. The identification of subjects at risk of developing COPD is important to reduce its global burden, as early interventions on modifiable risk factors (e.g. smoking) can delay or even prevent the decline of lung function. A few models to predict risk of COPD onset in the general population were developed, which included a small set of risk factors. The aim of this work is to develop a new predictive model of COPD onset, testing the predictive ability of a variety of variables, including socio-economic and lifestyle factors, wellbeing status, respiratory symptoms, medical history, lung function measurements and blood test biomarkers. The model was developed by applying logistic regression to a training set (n=2897) extracted from the English Longitudinal Study of Ageing. Most important variables for COPD prediction were selected by least absolute shrinkage and selection operator regularization. The analysis showed that variables not considered by the literature models, such as physical activity, depression, marital status, self-reported health, fibrinogen, C-reactive protein and cholesterol can be important predictors of COPD onset. The derived model presented good discrimination and calibration performance on an independent test set (n=724), with area under the receiver-operating characteristic curve equal to 0.81 and expected-to-observed event ratio equal to 0.93. Future works include an external validation of the model, the use of different modelling techniques (e.g. survival models) and the application of variable ranking methods.