Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium.
Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium.
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DOI：
10.1161/jaha.112.000102
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发表时间：
2013-03-18
影响因子：
5.4
通讯作者：
Benjamin EJ
中科院分区：
文献类型：
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作者：
Alonso A;Krijthe BP;Aspelund T;Stepas KA;Pencina MJ;Moser CB;Sinner MF;Sotoodehnia N;Fontes JD;Janssens AC;Kronmal RA;Magnani JW;Witteman JC;Chamberlain AM;Lubitz SA;Schnabel RB;Agarwal SK;McManus DD;Ellinor PT;Larson MG;Burke GL;Launer LJ;Hofman A;Levy D;Gottdiener JS;Kääb S;Couper D;Harris TB;Soliman EZ;Stricker BH;Gudnason V;Heckbert SR;Benjamin EJ
预测房颤(AF)的工具可以识别更有可能从预防性干预中受益的高危个体，并作为测试新的假定危险因素的基准。来自美国3个大型队列(社区动脉粥样硬化风险研究[ARIC]、心血管健康研究[CHS]和弗雷明翰心脏研究[FHS])的个人水平的数据被汇集在一起，其中包括年龄在46岁到94岁之间的18556名男性和女性(19%的非裔美国人，81%的白人)，以使用临床变量推导出房颤的预测模型。对来自年龄、基因和环境-雷克雅未克研究(AGES)和鹿特丹研究(RS)的7672名参与者进行了导出模型的验证。分析包括派生队列中的1186例房颤事件和验证队列中的585例。一个简单的5年预测模型，包括年龄、种族、身高、体重、收缩和舒张压、当前吸烟、抗高血压药物的使用、糖尿病、心肌梗死和心力衰竭病史，具有良好的区分性(C统计，0.765；95%CI，0.748至0.781)。从心电图中添加变量并没有改善总体模型辨别能力(C统计量，0.767；95%可信区间，0.750至0.783；分类净重分类改进，−0.0032；95%可信区间，−0.0178至0.0113)。在验证队列中，判别是可接受的(年龄C-统计量，0.664；95%可信区间，0.632-0.697；RS-C统计量，0.705；95%可信区间，0.664-0.747)，校准是足够的。一个风险模型，包括初级保健环境中容易获得的变量，充分预测了美国和欧洲不同人群中的房颤。
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.