Consider this a WARNing

TL;DR

This paper introduces a model called WARN that predicts atrial fibrillation in the near-term, aiming to improve preventive treatments and reduce healthcare burdens.

Contribution

The novel contribution is the development of a model for short-term atrial fibrillation prediction using continuous Holter data.

Findings

WARN is trained to predict atrial fibrillation in the timescale of minutes.

The model could enable preventive therapies with rapid mechanisms of action.

Algorithmic monitoring of AF risk may reduce the burden on healthcare workers.

Abstract

Atrial fibrillation (AF) prediction can be valuable at many timescales and in many populations. In this issue of Patterns, Gavidia et al. train a model called WARN for short-term prediction of AF in the timescale of minutes in patients wearing 24-h continuous Holter electrocardiograms. The ability to predict near-term (e.g., 30 min) AF has the potential to enable preventive therapies with rapid mechanisms of action (e.g., oral anticoagulation, anti-arrhythmic drugs). In this way, efficient, continuous, and algorithmic monitoring of AF risk could reduce burden on healthcare workers and represents a valuable clinical pursuit.

Atrial fibrillation (AF) prediction can be valuable at many timescales and in many populations. In this issue of Patterns, Gavidia et al. train a model called WARN for short-term prediction of AF in the timescale of minutes in patients wearing 24-h continuous Holter electrocardiograms. The ability to predict near-term (e.g., 30 min) AF has the potential to enable preventive therapies with rapid mechanisms of action (e.g., oral anticoagulation, anti-arrhythmic drugs). In this way, efficient, continuous, and algorithmic monitoring of AF risk could reduce burden on healthcare workers and represents a valuable clinical pursuit.