Improving clinical trial efficiency using a machine learning-based risk score to enrich study populations.

Journal: European journal of heart failure
Published Date: May 22, 2022

Abstract

AIMS: Prognostic enrichment strategies can make trials more efficient, although potentially at the cost of diminishing external validity. Whether using a risk score to identify a population at increased mortality risk could improve trial efficiency is uncertain. We aimed to assess whether Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a previously validated risk score, could improve clinical trial efficiency.

Authors

  • Karola S Jering
    Cardiovascular Division, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
  • Claudio Campagnari
    Physics Department, University of California, Santa Barbara, California, USA.
  • Brian Claggett
    Brigham and Women's Hospital, Boston, MA, USA.
  • Eric Adler
    Department of Cardiology, University of California, San Diego, California, USA.
  • Liviu Klein
    From the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta (O.T.I., M.B.P., A.Q.J., A.D., A.O.B.); Division of Cardiology (S.D., T.D.M., L.K.) and Department of Bioengineering and Therapeutic Sciences (S.R.), University of California, San Francisco; and Department of Anesthesiology and Department of Biomedical Engineering, Northwestern University, Chicago, IL (M.E., J.A.H.).
  • Faraz S Ahmad
    Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
  • Adriaan A Voors
    University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
  • Scott Solomon
    Cardiovascular Division, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 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.

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