Integrating Clinical and Air Quality Data to Improve Prediction of COPD Exacerbations

Ratcliff et al.

Several studies have found associations between air pollution and respiratory disease outcomes. However, there is minimal prognostic research explore whether integrating air quality into clinical prediction models can improve accuracy and utility. In this study, we built models using both logistical regression and random forests to determine the benefits of including air quality data with meteorological and clinical data in prediction of COPD exacerbations requiring medical care. Logistic models were not improved by inclusion of air quality. However, the net benefit curves of random forest models showed greater clinical utility with the addition of air quality data. These models demonstrate a practical and relatively low-cost way to include environmental information into clinical prediction tools to improve clinical utility of COPD prediction. Findings can be used to provide population level health warnings as well as individual-patient risk assessments.