Temporal shift and predictive performance of machine learning for heart transplant outcomes.

Journal: The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation
Published Date: Jul 1, 2022

Abstract

BACKGROUND: Outcome prediction following heart transplant is critical to explaining risks and benefits to patients and decision-making when considering potential organ offers. Given the large number of potential variables to be considered, this task may be most efficiently performed using machine learning (ML). We trained and tested ML and statistical algorithms to predict outcomes following cardiac transplant using the United Network of Organ Sharing (UNOS) database.

Authors

  • Robert J H Miller
    Department of Imaging and Medicine and the Smidt Heart Institute (E.E., S.C., H.G., S.C., R.J.H.M., P.J.S., B.K.T., D.S.B.), Cedars-Sinai Medical Center, Los Angeles, CA.
  • František Sabovčik
    KU Leuven Department of Cardiovascular Sciences, Research Unit Hypertension and Cardiovascular Epidemiology, University of Leuven, Leuven, Belgium.
  • Nicholas Cauwenberghs
    KU Leuven Department of Cardiovascular Sciences, Research Unit Hypertension and Cardiovascular Epidemiology, University of Leuven, Leuven, Belgium.
  • Celine Vens
    Department of Computer Science, KU Leuven, Leuven, Belgium.
  • Kiran K Khush
    Cardiac Transplant, and Mechanical Circulatory Support, and Department of Medicine, Section of Heart Failure, Stanford University, Stanford, California.
  • Paul A Heidenreich
    Veterans Administration Palo Alto Health Care System, Palo Alto, California.
  • Francois Haddad
    Division of Cardiovascular Medicine (F.H.), in the Department of Medicine, Stanford University, CA.
  • Tatiana Kuznetsova
    3 Research Unit Hypertension and Cardiovascular Epidemiology KU Leuven Department of Cardiovascular Sciences University of Leuven Belgium.

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