The FIND-AF pilot study will evaluate the yield of atrial fibrillation diagnosis using remote ECG monitoring guided by a machine learning algorithm in 1955 participants aged 30 years or older.
Does an EHR machine learning algorithm-guided remote ECG monitoring pathway identify undiagnosed atrial fibrillation in adults aged 30 years or older without a history of AF?
The FIND-AF pilot study will evaluate the feasibility and yield of using a machine learning algorithm applied to primary care electronic health records to guide remote ECG screening for undiagnosed atrial fibrillation.
INTRODUCTION: Atrial fibrillation (AF) is associated with a fivefold increased risk of stroke. Oral anticoagulation reduces the risk of stroke, but AF is elusive. A machine learning algorithm (Future Innovations in Novel Detection of Atrial Fibrillation (FIND-AF)) developed to predict incident AF within 6 months using data in primary care electronic health records (EHRs) could be used to guide AF screening. The objectives of the FIND-AF pilot study are to determine yields of AF during ECG monitoring across AF risk estimates and establish rates of recruitment and protocol adherence in a remote AF screening pathway. METHODS AND ANALYSIS: The FIND-AF Pilot is an interventional, non-randomised, single-arm, open-label study that will recruit 1955 participants aged 30 years or older, without a history of AF and eligible for oral anticoagulation, identified as higher risk and lower risk by the FIND-AF risk score from their primary care EHRs, to a period of remote ECG monitoring with a Zenicor-ECG device. The primary outcome is AF diagnosis during ECG monitoring, and secondary outcomes include recruitment rates, withdrawal rates, adherence to ECG monitoring and prescription of oral anticoagulation to participants diagnosed with AF during ECG monitoring. ETHICS AND DISSEMINATION: The study has ethical approval (the North West-Greater Manchester South Research Ethics Committee reference 23/NW/0180). Findings will be announced at relevant conferences and published in peer-reviewed journals in line with the Funder's open access policy. TRIAL REGISTRATION NUMBER: NCT05898165.
Nadarajah et al. (Fri,) conducted a other in Atrial fibrillation (n=1,955). Remote ECG monitoring with a Zenicor-ECG device guided by FIND-AF machine learning algorithm was evaluated on AF diagnosis during ECG monitoring. The FIND-AF pilot study will evaluate the yield of atrial fibrillation diagnosis using remote ECG monitoring guided by a machine learning algorithm in 1955 participants aged 30 years or older.