Identifying high-risk Fontan phenotypes using K-means clustering of cardiac magnetic resonance-based dyssynchrony metrics.

Journal: Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
PMID: 39004418

Abstract

BACKGROUND: Individuals with a Fontan circulation encompass a heterogeneous group with adverse outcomes linked to ventricular dilation, dysfunction, and dyssynchrony. The purpose of this study was to assess if unsupervised machine learning cluster analysis of cardiovascular magnetic resonance (CMR)-derived dyssynchrony metrics can separate ventricles in the Fontan circulation from normal control left ventricles and identify prognostically distinct subgroups within the Fontan cohort.

Authors

  • Addison Gearhart
    Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts, USA.
  • Sunakshi Bassi
    Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.
  • Rahul H Rathod
    Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts, USA.
  • Rebecca S Beroukhim
    Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts, USA; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.
  • Stuart Lipsitz
    Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts, USA; Division of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.
  • Maxwell P Gold
    Massachusetts Institute of Technology, Boston, Massachusetts, USA.
  • David M Harrild
    Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts, USA; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.
  • Audrey Dionne
    Division of Pediatric Cardiology, CHU Ste-Justine, University of Montreal, 3175, Côte Sainte-Catherine, Montreal, QC, H3T 1C5, Canada.
  • Sunil J Ghelani
    Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts, USA.

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