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dc.contributor.authorAdeniji, Mary
dc.contributor.authorBrimicombe, James
dc.contributor.authorCowie, Martin
dc.contributor.authorDymond, Andrew
dc.contributor.authorClair Linden, Hannah
dc.contributor.authorLip, Gregory
dc.contributor.authorMant, Jonathan
dc.contributor.authorPandiaraja, Madhumitha
dc.contributor.authorWilliams, Kate
dc.contributor.authorCharlton, Peter
dc.date.accessioned2022-04-11T23:30:23Z
dc.date.available2022-04-11T23:30:23Z
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/335991
dc.description.abstractScreening for atrial fibrillation (AF) could reduce the incidence of stroke by identifying undiagnosed AF and prompting anticoagulation. However, screening may involve recording many electrocardiograms (ECGs) from each participant, several of which require manual review which is costly and time-consuming. The aim of this study was to investigate whether the number of ECG reviews could be reduced by using a model to prioritise ECGs for review, whilst still accurately diagnosing AF. A multiple logistic regression model was created to estimate the likelihood of an ECG exhibiting AF based on the mean RR-interval and variability in RR-intervals. It was trained on 1,428 manually labelled ECGs from 144 AF screening programme participants, and evaluated using 11,443 ECGs from 1,521 participants. When using the model to order ECGs for review, the number of reviews for AF participants was reduced by 76% since no further reviews are required after an AF ECG is identified; however, it did not impact the number of reviews in non-AF participants (the vast majority of participants), so the overall number of reviews was reduced by 3% with no missed AF diagnoses. When using the model to also exclude ECGs from review, the overall number of reviews was reduced by 27% with no missed AF diagnoses, and by 52% with only 2% of AF diagnoses missed. In conclusion, the workload can be reduced by using a model to prioritise ECGs for review. Ordering ECGs alone only provides only a moderate reduction in workload. The additional use of a threshold to exclude ECGs from review provides a much greater reduction in workload at the expense of some missed AF diagnoses.
dc.description.sponsorshipThis study is funded by the National Institute for Health Research (NIHR), grant number RP-PG-0217-20007; and the British Heart Foundation (BHF), grant number FS/20/20/34626.
dc.rightsAll Rights Reserved
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserved
dc.titlePrioritising electrocardiograms for manual review to improve the efficiency of atrial fibrillation screening
dc.typeConference Object
dc.publisher.departmentDepartment of Public Health And Primary Care, The Primary Care Unit
dc.date.updated2022-04-07T09:51:46Z
dc.identifier.doi10.17863/CAM.83423
dcterms.dateAccepted2022-04-01
rioxxterms.versionofrecord10.17863/CAM.83423
rioxxterms.versionAM
dc.contributor.orcidBrimicombe, James [0000-0002-3443-3256]
dc.contributor.orcidCharlton, Peter [0000-0003-3836-8655]
pubs.funder-project-idBritish Heart Foundation (FS/20/20/34626)
pubs.conference-name44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
pubs.conference-start-date2022-07-11
cam.orpheus.counter8
cam.depositDate2022-04-07
pubs.conference-finish-date2022-07-15
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement


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