Long-term PM2.5 exposure and the clinical application of machine learning for predicting incident atrial fibrillation

Abstract

Clinical impact of fine particulate matter (PM_2.5) air pollution on incident atrial fibrillation (AF) had not been well studied. We used integrated machine learning (ML) to build several incident AF prediction models that include average hourly measurements of PM_2.5 for the 432,587 subjects of Korean general population. We compared these incident AF prediction models using c-index, net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI). ML using the boosted ensemble method exhibited a higher c-index (0.845 [0.837-0.853]) than existing traditional regression models using CHA₂DS₂-VASc (0.654 [0.646-0.661]), CHADS₂ (0.652 [0.646-0.657]), or HATCH (0.669 [0.661-0.676]) scores (each p < 0.001) for predicting incident AF. As feature selection algorithms identified PM_2.5 as a highly important variable, we applied PM_2.5 for predicting incident AF and constructed scoring systems. The prediction performances significantly increased compared with models without PM_2.5 (c-indices: boosted ensemble ML, 0.954 [0.949-0.959]; PM-CHA₂DS₂-VASc, 0.859 [0.848-0.870]; PM-CHADS₂, 0.823 [0.810-0.836]; or PM-HATCH score, 0.849 [0.837-0.860]; each interaction, p < 0.001; NRI and IDI were also positive). ML combining readily available clinical variables and PM_2.5 data was found to predict incident AF better than models without PM_2.5 or even established risk prediction approaches in the general population exposed to high air pollution levels.