Performance of the CHARGE-AF risk model for incident atrial fibrillation in the EPIC Norfolk cohort.
European Journal of Preventive Cardiology 2014 ; 22: 932-9.
Pfister R, Brägelmann J, Michels G, Wareham NJ, Luben R, Khaw KT
DOI : 10.1177/2047487314544045
PubMed ID : 25059930
PMCID : 0
Identification of individuals at risk for developing atrial fibrillation (AF) will help to target screening and preventive interventions. We aimed to validate the CHARGE-AF model (including variables age, race, height, weight, blood pressure, smoking, antihypertensive medication, diabetes, myocardial infarction and heart failure) for prediction of five-year incident AF in a representative European population with a wide age range.
The CHARGE-AF model was calculated in 24,020 participants of the population-based EPIC Norfolk study with 236 cases of hospitalization with diagnosis of AF within five years. The model showed good discrimination (c-statistic 0.81, 95% confidence interval (CI) 0.75-0.85), but weak calibration (Chi(2)-statistic 142) with an almost two-fold overestimation of AF incidence. A recalibration to characteristics of the European Prospective Investigation into Cancer and Nutrition (EPIC) Norfolk cohort improved calibration considerably (Chi(2)-statistic 13.3), with acceptable discrimination in participants both >65 and ≤65 years of age (c-statistics 0.70, 95% CI 0.61-0.77 and 0.83, 95% CI 0.74-0.88). The recalibrated model also showed good discrimination in participants free of cardiovascular disease (c-statistics 0.80, 95% CI 0.75-0.84). Categories of predicted risk (<2.5%, 2.5-5% or >5%) showed good concordance with observed five-year AF incidence of 0.62%, 3.49% and 8.74% (log rank test p < 0.001), respectively.
A recalibration of the CHARGE-AF model is necessary for accurate predictions of five-year risk of AF in the EPIC Norfolk population. The recalibrated model showed good discrimination across a wide age range and in individuals free of cardiovascular disease, and hence is broadly applicable in primary care to identify people at risk for development of AF.