Postoperative delirium prediction using machine learning models and preoperative electronic health record data.

Journal: BMC anesthesiology
Published Date: Jan 3, 2022

Abstract

BACKGROUND: Accurate, pragmatic risk stratification for postoperative delirium (POD) is necessary to target preventative resources toward high-risk patients. Machine learning (ML) offers a novel approach to leveraging electronic health record (EHR) data for POD prediction. We sought to develop and internally validate a ML-derived POD risk prediction model using preoperative risk features, and to compare its performance to models developed with traditional logistic regression.

Authors

  • Andrew Bishara
    Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA, United States.
  • Catherine Chiu
    Department of Anesthesia and Perioperative Care, University of California, San Francisco, 521 Parnassus Avenue, San Francisco, CA, 94143, USA.
  • Elizabeth L Whitlock
    Department of Anesthesia and Perioperative Care, University of California, San Francisco, 521 Parnassus Avenue, San Francisco, CA, 94143, USA.
  • Vanja C Douglas
    Department of Neurology, University of California, San Francisco.
  • Sei Lee
    Division of Geriatrics, University of California, San Francisco, 505 Parnassus Avenue, San Francisco, CA, 94143, USA.
  • Atul J Butte
    Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA.
  • Jacqueline M Leung
    Department of Anesthesia and Perioperative Care, University of California, San Francisco, 521 Parnassus Avenue, San Francisco, CA, 94143, 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.

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