A machine learning-based Coagulation Risk Index predicts acute traumatic coagulopathy in bleeding trauma patients.

Journal: The journal of trauma and acute care surgery
PMID: 39330762

Abstract

BACKGROUND: Acute traumatic coagulopathy (ATC) is a well-described phenomenon known to begin shortly after injury. This has profound implications for resuscitation from hemorrhagic shock, as ATC is associated with increased risk for massive transfusion (MT) and mortality. We describe a large-data machine learning-based Coagulation Risk Index (CRI) to test the early prediction of ATC in bleeding trauma patients.

Authors

  • Justin E Richards
    From the Department of Anesthesiology (J.E.R., S.Y., P.H.), Department of Surgery (S.Y., R.A.K., T.M.S., P.H.), Shock, Trauma, and Anesthesia Research (R.A.K.), University of Maryland School of Medicine (J.E.R., S.Y., R.A.K., T.M.S., P.H.), Program in Trauma (J.E.R., S.Y., R.A.K., T.M.S., P.H.), R Adams Cowley Shock Trauma Center, Baltimore, Maryland.
  • Shiming Yang
    University of Maryland School of Medicine, Baltimore, MD 21201, United States.
  • Rosemary A Kozar
  • Thomas M Scalea
    University of Maryland School of Medicine, Baltimore, MD 21201, United States.
  • Peter Hu
    Janssen Research & Development, LLC, Raritan, New Jersey, United States of America.

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