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Friedman, Sam Freesun; Khurshid, Shaan.
  • Friedman SF; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Khurshid S; Cardiology Division, Massachusetts General Hospital, Boston, MA, USA.
Patterns (N Y) ; 5(6): 101009, 2024 Jun 14.
Article en En | MEDLINE | ID: mdl-39005488
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.