Introduction
stroke, Atrial Fibrillation, cardiac arrhythmia
ATRIAL FIBRILLATION (AF), STROKE, RECRUITMENT, CARDIAC ARRHYTHMIA, ATRIAL FIBRILLATION
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia worldwide and confers a fivefold increased risk of stroke.Systematic population screening for AF guided by age with or without the presence of additional stroke risk factors with a non-invasive ECG devices has been shown to be feasible, increase detection rates for AF compared with routine care, and increase initiation of oral anticoagulation. However, yields of new AF diagnosed are low at between 2.6% and 5.3%.A targeted screening approach towards a reliably identified subpopulation at higher risk of AF may be more effective and cost-effective. Guiding AF screening by predicted AF risk based on artificial intelligence analysis of ECGs in sinus rhythm has been demonstrated to improve yield of new AF,A previous randomised clinical trial (RCT) of intermittent non-invasive ECG monitoring compared with routine care for individuals identified as higher risk by an EHR-based risk prediction algorithm (PuLSE-AI) did not find a higher yield of AF detection from ECG monitoring.The Future Innovations in Novel Detection of Atrial Fibrillation (FIND-AF) machine learning algorithm was developed and validated in routinely collected EHRs from over two million UK patients for prediction of incident AF within the next 6 months. It demonstrates an area under the receiver operating characteristic (AUROC) of 0.824 (95% CI 0.814 to 0.834), with robust prediction performance across both sexes and different ethnic groups.FIND-AF was developed and validated in retrospective cohorts of patients where AF was diagnosed during routine care. The objectives of the FIND-AF pilot study are to determine yields of AF during non-invasive ECG monitoring across AF risk estimates and to establish recruitment and protocol adherence rates for a remote AF screening intervention.