What does this research mean for the field?

A simple 5-year risk model using readily available clinical variables adequately predicts the incidence of atrial fibrillation in diverse populations, and the addition of electrocardiogram variables does not improve its predictive performance. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

March 12, 2013Journal of the American Heart AssociationOpen Access

Simple Risk Model Predicts Incidence of Atrial Fibrillation in a Racially and Geographically Diverse Population: the CHARGE‐AF Consortium

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Why the study?

Does a simple clinical risk model accurately predict the 5-year incidence of atrial fibrillation in a diverse population?

Structured PICO

Does a simple clinical risk model accurately predict the 5-year incidence of atrial fibrillation in a diverse population?

Population

Men and women aged 46 to 94 years from diverse populations in the United States and Europe

Intervention

A simple 5-year predictive risk model using clinical 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)

Comparator

Addition of variables from the electrocardiogram (ECG)

Outcome

Incidence of Atrial Fibrillation (AF)hard clinical

A simple risk model using readily available primary care variables can adequately predict the 5-year risk of incident atrial fibrillation across diverse populations.

Implication

May support simple clinical models for 5-year AF prediction in primary care; leaves open whether ECG adds value or improves outcomes.

Abstract

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.