Machine Learning Differentiates Extracorporeal Membrane Oxygenation Mortality Risk Profiles Among Trauma Patients.

Journal: The American surgeon
PMID: 38752529

Abstract

BACKGROUND: Extracorporeal membrane oxygenation (ECMO) is resource intensive with high mortality. Identifying trauma patients most likely to derive a survival benefit remains elusive despite current ECMO guidelines. Our objective was to identify unique patient risk profiles using the largest database of trauma patients available.

Authors

  • Bryan R Campbell
    Division of Trauma and Acute Care Surgery, Department of Surgery, Scripps Mercy Hospital, San Diego, CA, USA.
  • Alexandra S Rooney
    Division of Trauma and Acute Care Surgery, Department of Surgery, Scripps Mercy Hospital, San Diego, CA, USA.
  • Andrea Krzyzaniak
    Division of Trauma and Acute Care Surgery, Department of Surgery, Scripps Mercy Hospital, San Diego, CA, USA.
  • Richard Y Calvo
    Division of Trauma and Acute Care Surgery, Department of Surgery, Scripps Mercy Hospital, San Diego, CA, USA.
  • Kyle D Checchi
    Division of Trauma and Acute Care Surgery, Department of Surgery, Scripps Mercy Hospital, San Diego, CA, USA.
  • Alyssa N Carroll
    Division of Trauma and Acute Care Surgery, Department of Surgery, Scripps Mercy Hospital, San Diego, CA, USA.
  • Michael J Sise
    Division of Trauma and Acute Care Surgery, Department of Surgery, Scripps Mercy Hospital, San Diego, CA, USA.
  • Vishal Bansal
    Division of Trauma and Acute Care Surgery, Department of Surgery, Scripps Mercy Hospital, San Diego, CA, USA.
  • Michael J Krzyzaniak

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