Does the CHARGE-AF risk model accurately predict five-year incident atrial fibrillation in a European population?
The CHARGE-AF risk model requires recalibration for accurate prediction of five-year incident AF in a European population, after which it shows good discrimination across a wide age range.
BACKGROUND: 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. METHODS AND RESULTS: 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 (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. CONCLUSION: 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.
Pfister et al. (2014) studied this question.