The appropriate use of risk scores in the prediction of atrial fibrillation

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice (1).It is associated with a significant risk for several adverse cardiovascular outcomes, including stroke (2), myocardial infarction (3), heart failure (4), and mortality (5).Additionally, AF is associated with significant cost to the health care system, with annual projected costs between $6 and $26 billion dollars (6). The aforementioned complications and financial burden associated with this arrhythmia underscore the importance of accurate AF risk assessment, as this will allow for the development of targeted preventive strategies.This need for the accurate prediction of AF has given rise to the development of several scoring systems from population-based cohort studies (7).Risk scores have been developed in the Framingham Heart Study (FHS) (8), the Atherosclerosis Risk In Communities (ARIC) study (9), and the Women's Health Study (WHS) (10).However, the risk scores developed from these individual cohorts were limited in their predictive ability, as each cohort varied widely in the diversity (e.g., age, sex, and race/ethnicity) of recruited participants.Accordingly, the Cohorts for Aging and Research in Genomic Epidemiology (CHARGE)-AF consortium derived a 5-year predictive model to address some of these limitations (11).This score used pooled data from 18,556 participants of the FHS, the Cardiovascular Health Study, and ARIC, and included the following characteristics: age, race, height, weight, systolic and diastolic blood pressure, current smoking, treatment of hypertension, diabetes, and history of myocardial infarction and heart failure.