Prediction of new-onset atrial fibrillation in patients with hypertrophic cardiomyopathy using machine learning.

Journal: European journal of heart failure
PMID: 39694602

Abstract

AIMS: Atrial fibrillation (AF) is the most common sustained arrhythmia among patients with hypertrophic cardiomyopathy (HCM), leading to increased symptom burden and risk of thromboembolism. The HCM-AF score was developed to predict new-onset AF in patients with HCM, though sensitivity and specificity of this conventional tool are limited. Thus, there is a need for more accurate tools to predict new-onset AF in HCM. The objective of the present study was to develop a better model to predict new-onset AF in patients with HCM using machine learning (ML).

Authors

  • Ree Lu
    Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA.
  • Heidi S Lumish
    Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA.
  • Kohei Hasegawa
    Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America.
  • Mathew S Maurer
    Division of Cardiology, Department of Medicine (L.W.L., M.S.M., M.P.R., Y.J.S.), Columbia University Irving Medical Center, New York, NY.
  • Muredach P Reilly
    Division of Cardiology, Department of Medicine, Columbia University Medical Center, New York, NY, 10032, USA.
  • Shepard D Weiner
    Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA.
  • Albree Tower-Rader
    Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Michael A Fifer
    Cardiology Division, Department of Medicine (M.A.F.), Massachusetts General Hospital, Boston.
  • Yuichi J Shimada
    Division of Cardiology, Department of Medicine (L.W.L., M.S.M., M.P.R., Y.J.S.), Columbia University Irving Medical Center, New York, NY.

Keywords

External Resources

Popular Topics
Recent Journals