Personalized Surgical Transfusion Risk Prediction Using Machine Learning to Guide Preoperative Type and Screen Orders.

Journal: Anesthesiology
Published Date: Jul 1, 2022

Abstract

BACKGROUND: Accurate estimation of surgical transfusion risk is essential for efficient allocation of blood bank resources and for other aspects of anesthetic planning. This study hypothesized that a machine learning model incorporating both surgery- and patient-specific variables would outperform the traditional approach that uses only procedure-specific information, allowing for more efficient allocation of preoperative type and screen orders.

Authors

  • Sunny S Lou
    Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri.
  • Hanyang Liu
    Department of Computer Science and Engineering, Washington University School of Medicine, St. Louis, Missouri.
  • Chenyang Lu
    Department of Computer Science and Engineering, Washington University, St. Louis, MO.
  • Troy S Wildes
    Department of Anesthesiology, Washington University in St Louis, St Louis, Missouri, USA.
  • Bruce L Hall
    Department of Surgery and Olin Business School, Washington University in Saint Louis, St. Louis, Missouri.
  • Thomas Kannampallil
    Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, USA.

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