Identification of undiagnosed atrial fibrillation patients using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI): Study protocol for a randomised controlled trial

Atrial fibrillation (AF) is associated with an increased risk of stroke, enhanced stroke severity, and other comorbidities. However, AF is often asymptomatic, and frequently remains undiagnosed until complications occur. Current screening approaches for AF lack either cost-effectiveness or diagnostic sensitivity; thus, there is interest in tools that could be used for population screening. An AF risk prediction algorithm, developed using machine learning from a UK dataset of 2,994,837 patients, was found to be more effective than existing models at identifying patients at risk of AF.

Source: Contemporary Clinical Trials - October 19, 2020 Category: Radiology Authors: Nathan R. Hill, Chris Arden, Lee Beresford-Hulme, A. John Camm, David Clifton, D. Wyn Davies, Usman Farooqui, Jason Gordon, Lara Groves, Michael Hurst, Sarah Lawton, Steven Lister, Christian Mallen, Anne-Celine Martin, Phil McEwan, Kevin G. Pollock, Jenni Source Type: research