Nadarajah, R, Wu, J orcid.org/0000-0001-6093-599X, Frangi, AF orcid.org/0000-0002-2675-528X et al. (3 more authors) (2021) What is next for screening for undiagnosed atrial fibrillation? Artificial intelligence may hold the key. European Heart Journal - Quality of Care and Clinical Outcomes. ISSN 2058-5225
Abstract
Atrial fibrillation 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 atrial fibrillation even though a number of studies have shown that periods of serial or continuous rhythm monitoring in older people in the general population increases detection of atrial fibrillation 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 optimised 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 atrial fibrillation 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | Atrial Fibrillation, Screening, Stroke, Prediction model, Artificial intelligence |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Dentistry (Leeds) > Applied Health and Clinical Translation (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 14 Jan 2022 10:58 |
Last Modified: | 14 Jan 2022 10:58 |
Status: | Published online |
Publisher: | Oxford University Press |
Identification Number: | 10.1093/ehjqcco/qcab094 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:182397 |