Development and Validation of a Machine Learning Model to Identify Patients Before Surgery at High Risk for Postoperative Adverse Events.

Journal: JAMA network open
Published Date: Jul 3, 2023

Abstract

IMPORTANCE: Identifying patients at high risk of adverse outcomes prior to surgery may allow for interventions associated with improved postoperative outcomes; however, few tools exist for automated prediction.

Authors

  • Aman Mahajan
    Department of Anaesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
  • Stephen Esper
    Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
  • Thien Htay Oo
    Department of Clinical Analytics, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
  • Jeffery McKibben
    Department of Clinical Analytics, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
  • Michael Garver
    Department of Clinical Analytics, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
  • Jamie Artman
    Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
  • Cynthia Klahre
    Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
  • John Ryan
    Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
  • Senthilkumar Sadhasivam
    Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
  • Jennifer Holder-Murray
    Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
  • Oscar C Marroquin
    Department of Clinical Analytics, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.

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