Machine Learning Prediction of Progression in FEV1 in the COPDGene Study

Background: The heterogeneous nature of COPD complicates the identification of the predictors of disease progression and consequently the development of effective therapies. We aimed to improve the prediction of disease progression in COPD by using machine learning and incorporating a rich dataset of phenotypic features. Methods: We included 4,496 smokers with available data from their enrollment and 5-year follow-up visits in the Genetic Epidemiology of COPD (COPDGene) study. We constructed supervised random forest models to predict 5-year progression in FEV1 from 46 baseline demographic, clinical, physiologic, and imaging features. Using cross-validation, we randomly partitioned participants into training and testing samples. We also validated the results in the COPDGene 10-year follow-up visit. Results: Predicting the change in FEV1 over time is more challenging than simply predicting the future absolute FEV1 level. Nevertheless, the area under the ROC curves for the prediction of subjects in the top quartile of observed disease progression was 0.70 in the 10-year follow-up data. The model performance accuracy was best for GOLD1-2 subjects and it was harder to achieve accurate prediction in advanced stages of the disease. Predictive variables differed in their relative importance as well as for the predictions by GOLD grade. Conclusion: This state-of-the art approach along with deep phenotyping predicts FEV1 progression with reasonable accuracy. There is significant room for improvement in future models. This prediction model facilitates the identification of smokers at increased risk for rapid disease progression. Such findings may be useful in the selection of patient populations for targeted clinical trials.