A Machine Learning-derived Risk Score Improves Prediction of Outcomes After LVAD Implantation: An Analysis of the INTERMACS Database.

Journal: Journal of cardiac failure
PMID: 39486760

Abstract

BACKGROUND: Significant variability in outcomes after left ventricular assist device (LVAD) implantation emphasize the importance of accurately assessing patients' risk before surgery. This study assesses the Machine Learning Assessment of Risk and Early Mortality in Heart Failure (MARKER-HF) mortality risk model, a machine learning-based tool using 8 clinical variables, to predict post-LVAD implantation mortality and its prognostic enhancement over the Interagency Registry of Mechanically Assisted Circulatory Support (INTERMACS) profile.

Authors

  • Jin Joo Park
    Department of Cardiology, University of California, San Diego, California, USA.
  • Sonya John
    Cardiology Department, University of California San Diego, La Jolla, California.
  • Claudio Campagnari
    Physics Department, University of California, Santa Barbara, California, USA.
  • Avi Yagil
    Department of Cardiology, University of California, San Diego, California, USA.
  • Barry Greenberg
    Department of Cardiology, University of California, San Diego, California, USA.
  • Eric Adler
    Department of Cardiology, University of California, San Diego, California, USA.

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