Atrial fibrillation (AF) is increasingly common, though often undiagnosed, leaving many people untreated and at elevated risk of ischaemic stroke. Current European guidelines do not recommend systematic screening for AF, even though a number of studies have shown that periods of serial or continuous rhythm monitoring in older people in the general population increase detection of AF and the prescription of oral anticoagulation. This article discusses the conflicting results of two contemporary landmark trials, STROKESTOP and the LOOP, which provided the first evidence on whether screening for AF confers a benefit for people in terms of clinical outcomes. The benefit and efficiency of systematic screening for AF in the general population could be optimized by targeting screening to only those at higher risk of developing AF. For this purpose, evidence is emerging that prediction models developed using artificial intelligence in routinely collected electronic health records can provide strong discriminative performance for AF and increase detection rates when combined with rhythm monitoring in a clinical study. We consider future directions for investigation in this field and how this could be best aligned to the current evidence base to target screening in people at elevated risk of stroke.
Keywords: Artificial intelligence; Atrial fibrillation; Prediction model; Screening; Stroke.
© The Author(s) 2021. Published by Oxford University Press on behalf of the European Society of Cardiology.