Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium.

Tools for the prediction of atrial fibrillation (af) may identify high-risk individuals more likely to benefit from preventive interventions and serve as a benchmark to test novel putative risk factors. individual-level data from 3 large cohorts in the united states (atherosclerosis risk in communities [aric] study,the cardiovascular health study [chs],and the framingham heart study [fhs]),including 18 556 men and women aged 46 to 94 years (19% african americans,81% whites) were pooled to derive predictive models for af using clinical variables. validation of the derived models was performed in 7672 participants from the age,gene and environment-reykjavik study (ages) and the rotterdam study (rs). the analysis included 1186 incident af cases in the derivation cohorts and 585 in the validation cohorts. a simple 5-year predictive model including the variables age,race,height,weight,systolic and diastolic blood pressure,current smoking,use of antihypertensive medication,diabetes,and history of myocardial infarction and heart failure had good discrimination (c-statistic,0.765; 95% ci,0.748 to 0.781). addition of variables from the electrocardiogram did not improve the overall model discrimination (c-statistic,0.767; 95% ci,0.750 to 0.783; categorical net reclassification improvement,-0.0032; 95% ci,-0.0178 to 0.0113). in the validation cohorts,discrimination was acceptable (ages c-statistic,0.664; 95% ci,0.632 to 0.697 and rs c-statistic,0.705; 95% ci,0.664 to 0.747) and calibration was adequate. a risk model including variables readily available in primary care settings adequately predicted af in diverse populations from the united states and europe.