Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement.

Journal: Pacing and clinical electrophysiology : PACE
PMID: 33433905

Abstract

BACKGROUND: An accurate assessment of permanent pacemaker implantation (PPI) risk following transcatheter aortic valve replacement (TAVR) is important for clinical decision making. The aims of this study were to investigate the significance and utility of pre- and post-TAVR ECG data and compare machine learning approaches with traditional logistic regression in predicting pacemaker risk following TAVR.

Authors

  • Vien T Truong
    The Christ Hospital Health Network and The Lindner Research Center, Cincinnati, Ohio, USA.
  • Daniel Beyerbach
    The Christ Hospital Health Network and The Lindner Research Center, Cincinnati, Ohio, USA.
  • Wojciech Mazur
    The Christ Hospital Health Network and The Lindner Research Center, Cincinnati, Ohio, USA.
  • Matthew Wigle
    The Christ Hospital Health Network and The Lindner Research Center, Cincinnati, Ohio, USA.
  • Emma Bateman
    University of Kentucky, Lexington, Kentucky, USA.
  • Akhil Pallerla
    University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Tam N M Ngo
    The Christ Hospital Health Network and The Lindner Research Center, Cincinnati, Ohio, USA.
  • Satya Shreenivas
    The Christ Hospital Health Network and The Lindner Research Center, Cincinnati, Ohio, USA.
  • Justin T Tretter
    Heart Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
  • Cassady Palmer
    The Christ Hospital Health Network and The Lindner Research Center, Cincinnati, Ohio, USA.
  • Dean J Kereiakes
    The Christ Hospital Health Network and The Lindner Research Center, Cincinnati, Ohio, USA.
  • Eugene S Chung
    The Christ Hospital Health Network and The Lindner Research Center, Cincinnati, Ohio, USA.

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