• atrial fibrillation
• electrophysiology
• electrocardiography

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 …