Development of a machine learning-derived model to predict unplanned ICU admissions after major non-cardiac surgery.

Journal: BMC anesthesiology
Published Date: Jul 17, 2025

Abstract

BACKGROUND: Unplanned postoperative intensive care unit admissions (UIAs) are rare events that cause significant challenges to perioperative workflow. We describe the development of a machine-learning derived model to predict UIAs using only widely used preoperative variables.

Authors

  • Catherine Chiu
    Department of Anesthesia and Perioperative Care, University of California, San Francisco, 521 Parnassus Avenue, San Francisco, CA, 94143, USA.
  • Matthias R Braehler
    Department of Anesthesia and Perioperative Care, University of California, San Francisco, USA.
  • Anne L Donovan
    Department of Anesthesia and Perioperative Care, University of California, San Francisco, 521 Parnassus Avenue, San Francisco, CA, 94143, USA. anne.donovan@ucsf.edu.
  • Atul J Butte
    Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA.
  • Romain Pirracchio
  • Andrew M Bishara
    Department of Anesthesia, University of California, San Francisco, San Francisco, CA, USA.

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