Development and preliminary assessment of a machine learning model to predict myocardial infarction and cardiac arrest after major operations.

Journal: Resuscitation
Published Date: May 15, 2024

Abstract

INTRODUCTION: Accurate prediction of complications often informs shared decision-making. Derived over 10 years ago to enhance prediction of intra/post-operative myocardial infarction and cardiac arrest (MI/CA), the Gupta score has been criticized for unreliable calibration and inclusion of a wide spectrum of unrelated operations. In the present study, we developed a novel machine learning (ML) model to estimate perioperative risk of MI/CA and compared it to the Gupta score.

Authors

  • Yas Sanaiha
    Center for Health Sciences (CHS), Department of Surgery, University of California Los Angeles (UCLA), 72-247, Box 956904, Los Angeles, CA, 90095, USA.
  • Arjun Verma
  • Ayesha P Ng
    Cardiovascular Outcomes Research Laboratories, David Geffen School of Medicine at University of California-Las Angeles, CA. Electronic address: http://www.twitter.com/Ng_Ayesha.
  • Joseph Hadaya
    Cardiovascular Outcomes Research Laboratories, Department of Surgery, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA.
  • Clifford Y Ko
    Department of Surgery, University of California Los Angeles, Los Angeles, California.
  • Christian deVirgilio
    Department of Surgery, Harbor-University of California, Los Angeles Medical Center, Torrance, California, USA.
  • Peyman Benharash

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