Department of General Practice, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Meibergdreef 9, 1105 AZ Amsterdam, Netherlands.
Department of General and Interventional Cardiology, University Heart Center Hamburg/German Center for Cardiovascular Research, Partner Site Hamburg/Kiel/Luebeck, Martinistrasse 52, 20246 Hamburg, Germany.
Europace. 2020 May 1;22(5):684-694. doi: 10.1093/europace/euaa005.
Atrial fibrillation (AF) is a common arrhythmia associated with an increased stroke risk. The use of multivariable prediction models could result in more efficient primary AF screening by selecting at-risk individuals. We aimed to determine which model may be best suitable for increasing efficiency of future primary AF screening efforts.
We performed a systematic review on multivariable models derived, validated, and/or augmented for AF prediction in community cohorts using Pubmed, Embase, and CINAHL (Cumulative Index to Nursing and Allied Health Literature) through 1 August 2019. We performed meta-analysis of model discrimination with the summary C-statistic as the primary expression of associations using a random effects model. In case of high heterogeneity, we calculated a 95% prediction interval. We used the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) checklist for risk of bias assessment. We included 27 studies with a total of 2 978 659 unique participants among 20 cohorts with mean age ranging from 42 to 76 years. We identified 21 risk models used for incident AF risk in community cohorts. Three models showed significant summary discrimination despite high heterogeneity: CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology) [summary C-statistic 0.71; 95% confidence interval (95% CI) 0.66-0.76], FHS-AF (Framingham Heart Study risk score for AF) (summary C-statistic 0.70; 95% CI 0.64-0.76), and CHA2DS2-VASc (summary C-statistic 0.69; 95% CI 0.64-0.74). Of these, CHARGE-AF and FHS-AF had originally been derived for AF incidence prediction. Only CHARGE-AF, which comprises easily obtainable measurements and medical history elements, showed significant summary discrimination among cohorts that had applied a uniform (5-year) risk prediction window.
CHARGE-AF appeared most suitable for primary screening purposes in terms of performance and applicability in older community cohorts of predominantly European descent.
心房颤动（AF）是一种常见的心律失常，与卒中风险增加相关。使用多变量预测模型可以通过选择高危个体来提高对 AF 的初级筛查效率。我们旨在确定哪种模型可能最适合提高未来对 AF 的初级筛查效果。
我们在 2019 年 8 月 1 日之前，通过 Pubmed、Embase 和 CINAHL（护理和联合健康文献累积索引）对源于社区队列的、经过验证和/或增强的用于 AF 预测的多变量模型进行了系统评价。我们使用随机效应模型，以汇总 C 统计量作为关联的主要表达形式，对模型的判别力进行了荟萃分析。在存在高度异质性的情况下，我们计算了 95%预测区间。我们使用 CHARMS（系统评价中预测模型研究的批判性评估和数据提取）清单进行风险偏倚评估。我们纳入了 27 项研究，这些研究共纳入了 20 个队列的 2978659 名独特参与者，平均年龄为 42 至 76 岁。我们确定了 21 个用于社区队列中 AF 风险的风险模型。尽管存在高度异质性，但有 3 个模型显示出显著的汇总判别力：CHARGE-AF（基因组流行病学中的心脏和衰老研究队列）[汇总 C 统计量 0.71；95%置信区间（95%CI）0.66-0.76]、FHS-AF（Framingham 心脏研究的 AF 风险评分）（汇总 C 统计量 0.70；95%CI 0.64-0.76）和 CHA2DS2-VASc（汇总 C 统计量 0.69；95%CI 0.64-0.74）。其中，CHARGE-AF 和 FHS-AF 最初是为 AF 发生率预测而开发的。只有 CHARGE-AF，其包含了易于获得的测量和医学史元素，在应用统一（5 年）风险预测窗口的队列中显示出显著的汇总判别力。
CHARGE-AF 在性能和在以欧洲裔为主的老年社区队列中的适用性方面，似乎最适合用于初级筛查目的。
