
Simple Risk Model Predicts Incidence of Atrial Fibrillation in a Racially and Geographically Diverse Population: the CHARGE ‐ AF Consortium
Author(s) -
Alonso Alvaro,
Krijthe Bouwe P.,
Aspelund Thor,
Stepas Katherine A.,
Pencina Michael J.,
Moser Carlee B.,
Sinner Moritz F.,
Sotoodehnia a,
Fontes João D.,
Janssens A. Cecile J. W.,
Kronmal Richard A.,
Magnani Jared W.,
Witteman Jacqueline C.,
Chamberlain Alanna M.,
Lubitz Steven A.,
Schnabel Renate B.,
Agarwal Sunil K.,
McManus David D.,
Ellinor Patrick T.,
Larson Martin G.,
Burke Gregory L.,
Launer Lenore J.,
Hofman Albert,
Levy Daniel,
Gottdiener John S.,
Kääb Stefan,
Couper David,
Harris Tamara B.,
Soliman Elsayed Z.,
Stricker Bruno H. C.,
Gudnason Vilmundur,
Heckbert Susan R.,
Benjamin Emelia J.
Publication year - 2013
Publication title -
journal of the american heart association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.494
H-Index - 85
ISSN - 2047-9980
DOI - 10.1161/jaha.112.000102
Subject(s) - medicine , atrial fibrillation , atherosclerosis risk in communities , framingham risk score , population , stroke (engine) , myocardial infarction , rotterdam study , cardiology , demography , epidemiology , confidence interval , disease , mechanical engineering , environmental health , sociology , engineering
Background 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. Methods and Results Individual‐level data from 3 large cohorts in the U nited S tates (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% A frican A mericans, 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. Conclusion A risk model including variables readily available in primary care settings adequately predicted AF in diverse populations from the U nited S tates and E urope.