A risk model to predict atrial fibrillation in diabetes using machine learning: The ACCORD study

Machine learning (ML) may improve prediction of atrial fibrillation (AF), but its value compared with traditional models such as Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE-AF) in patients with diabetes remains unclear.

Among 9,307 patients in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) with type 2 diabetes and no prior AF, a random forest (RF) classifier using clinical and metabolic variables was compared with a CHARGE-AF Cox model. Discrimination was assessed by five-fold cross-validated area under receiver operating curve (AUC).

Over 6.26 years, 175 patients developed AF. The RF model (AUC = 0.731) performed comparably to CHARGE-AF (AUC = 0.756; p = 0.18). Age, waist circumference, race, total cholesterol, and estimated glomerular filtration rate were the top predictors.

ML matched CHARGE-AF performance and revealed distinct predictors supporting personalized AF risk prevention.