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Predicting incident atrial fibrillation in sinus rhythm: more than just trusting the ‘black box’
Heart ( IF 5.7 ) Pub Date : 2021-11-01 , DOI: 10.1136/heartjnl-2021-319385
Anthony Kashou 1 , Peter Noseworthy 2
Affiliation  

With an ageing population, the growing prevalence of atrial fibrillation (AF) is becoming a public health crisis. In the USA alone, the prevalence of AF is expected to more than double over the next 50 years.1 This undoubtedly has important clinical implications given the morbidity, mortality and tremendous healthcare cost burden associated with AF.2 3 In addition to the increasing numbers of known AF cases, another issue looms: a subset of individuals with silent and subclinical AF coexists in whom capturing the arrhythmia on single standard 12-lead ECG or even extended non-invasive ambulatory ECG monitoring remains a challenge. In fact, the first clinical presentation of those with asymptomatic AF may be an acute ischaemic stroke or decompensated heart failure.4 Therefore, the ability to identify individuals at increased risk of AF can aid in screening, surveillance, management and prevention strategies. The current study by Sanz-Garcia and colleagues,5 represents an innovative approach to doing just that. Until recently, AF prediction has been based on clinical variables integrated into scoring systems such as CHARGE-AF (Cohorts for Aging and Research in Genomic Epidemiology–AF) score, which is the best studied and most well-established clinical AF scoring tool.6 Others include the FHS (Framingham Heart Study), ARIC (Atherosclerosis Risk in Communities) and CHA2DS2-VASc risk scores.6 7 These approaches, however, require time-intensive data abstraction of multiple variables—some …

中文翻译:

预测窦性心律中的房颤事件:不仅仅是相信“黑匣子”

随着人口老龄化,心房颤动 (AF) 的日益流行正在成为公共卫生危机。仅在美国,未来 50 年 AF 的患病率预计将增加一倍以上。 1 鉴于与 AF 相关的发病率、死亡率和巨大的医疗费用负担,这无疑具有重要的临床意义。2 3 除了不断增加的人数在已知的 AF 病例中,另一个问题迫在眉睫:一部分患有无症状和亚临床 AF 的个体并存,在这些个体中,通过单一标准 12 导联心电图甚至扩展的无创动态心电图监测来捕捉心律失常仍然是一个挑战。事实上,无症状 AF 患者的第一个临床表现可能是急性缺血性卒中或失代偿性心力衰竭。 4 因此,识别 AF 风险增加的个体的能力有助于筛查、监测、管理和预防策略。Sanz-Garcia 及其同事目前的研究 5 代表了一种创新方法来做到这一点。直到最近,AF 预测还是基于集成到评分系统中的临床变量,例如 CHARGE-AF(基因组流行病学研究队列 – AF)评分,这是研究最深入、最完善的临床 AF 评分工具。 6其他包括 FHS(Framingham 心脏研究)、ARIC(社区动脉粥样硬化风险)和 CHA2DS2-VASc 风险评分。6 7 然而,这些方法需要对多个变量进行耗时的数据抽象——有些…… 5 代表了一种创新方法来做到这一点。直到最近,AF 预测还是基于集成到评分系统中的临床变量,例如 CHARGE-AF(基因组流行病学研究队列 – AF)评分,这是研究最深入、最完善的临床 AF 评分工具。 6其他包括 FHS(Framingham 心脏研究)、ARIC(社区动脉粥样硬化风险)和 CHA2DS2-VASc 风险评分。6 7 然而,这些方法需要对多个变量进行耗时的数据抽象——有些…… 5 代表了一种创新方法来做到这一点。直到最近,AF 预测还是基于集成到评分系统中的临床变量,例如 CHARGE-AF(基因组流行病学研究队列 – AF)评分,这是研究最深入、最完善的临床 AF 评分工具。 6其他包括 FHS(Framingham 心脏研究)、ARIC(社区动脉粥样硬化风险)和 CHA2DS2-VASc 风险评分。6 7 然而,这些方法需要对多个变量进行耗时的数据抽象——有些……
更新日期:2021-10-27
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