Development and validation of a model to predict the risk of exacerbations in chronic obstructive pulmonary disease

Prediction models for exacerbations in patients with chronic obstructive pulmonary disease (COPD) are scarce. Our aim was to develop and validate a new model to predict exacerbations in patients with COPD.

The derivation cohort consisted of patients aged 65 years or over, with a COPD diagnosis, who were followed up over 24 months. The external validation cohort consisted of another cohort of COPD patients, aged 50 years or over. Exacerbations of COPD were defined as symptomatic deterioration requiring pulsed oral steroid use or hospitalization. Logistic regression analysis including backward selection and shrinkage were used to develop the final model and to adjust for overfitting. The adjusted regression coefficients were applied in the validation cohort to assess calibration of the predictions and calculate changes in discrimination applying C-statistics.

The derivation and validation cohort consisted of 240 and 793 patients with COPD, of whom 29% and 28%, respectively, experienced an exacerbation during follow-up. The final model included four easily assessable variables: exacerbations in the previous year, pack years of smoking, level of obstruction, and history of vascular disease, with a C-statistic of 0.75 (95% confidence interval [CI]: 0.69-0.82). Predictions were well calibrated in the validation cohort, with a small loss in discrimination potential (C-statistic 0.66 [95% CI 0.61-0.71]).

Our newly developed prediction model can help clinicians to predict the risk of future exacerbations in individual patients with COPD, including those with mild disease.