A Study to Assess the Effectiveness of an Atrial Fibrillation (AF) Risk Prediction Algorithm and Diagnostic Test in Identifying Patients With AF.

A Randomised Controlled Trial for the Identification of Undiagnosed Atrial Fibrillation Patients Using a Machine Learning Risk Prediction Algorithm and Diagnostic Test

This is a trial to assess the effectiveness of an atrial fibrillation (AF) risk prediction algorithm and diagnostic test for the identification of patients with atrial fibrillation

Inclusion Criteria:

Practice inclusion criteria for the trial are as follows;

• GP Practices within National Institute for Healthcare Research (NIHR) Clinical Research Network: West Midlands (CRN: WM) CRN: WM
• GP Practices using EMIS as their electronic medical record system of choice. Patient inclusion criteria for the trial are as follows;
• Patients registered at a participating practice, aged ≥30 years and without an AF diagnosis.
• Patients with a valid index date (see Section 3.3) Participant inclusion criteria for participation in the intervention arm are;
• As above, and who have provided written consent to: attend a research clinic, AF diagnosis check using a 12-lead ECG, and for access to medical records Participant inclusion criteria for participation in further AF remote diagnosis with an AliveCor Heart Monitor are;
• As above, and those with a negative or indeterminant ECG
• As above, and those with access to a smartphone

Exclusion criteria:

• Patients <30 years
• Patients with an existing diagnosis of AF
• Patients for whom the healthcare professional feels the study is unsuitable