Forecasting a Crisis: Machine-Learning Models Predict Occurrence of Intraoperative Bradycardia Associated With Hypotension.

Journal: Anesthesia and analgesia
PMID: 32287127

Abstract

BACKGROUND: Predictive analytics systems may improve perioperative care by enhancing preparation for, recognition of, and response to high-risk clinical events. Bradycardia is a fairly common and unpredictable clinical event with many causes; it may be benign or become associated with hypotension requiring aggressive treatment. Our aim was to build models to predict the occurrence of clinically significant intraoperative bradycardia at 3 time points during an operative course by utilizing available preoperative electronic medical record and intraoperative anesthesia information management system data.

Authors

  • Stuart C Solomon
    From the Department of Anesthesiology & Pain Medicine, University of Washington, Seattle, Washington.
  • Rajeev C Saxena
    Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA.
  • Moni B Neradilek
    Mountain-Whisper-Light Statistics, Seattle, Washington.
  • Vickie Hau
    From the Department of Anesthesiology & Pain Medicine, University of Washington, Seattle, Washington.
  • Christine T Fong
    Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA.
  • John D Lang
    Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA.
  • Karen L Posner
    From the Department of Anesthesiology & Pain Medicine, University of Washington, Seattle, Washington.
  • Bala G Nair
    Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA.

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