Applying Machine Learning to the ANZELA-QI Database to Predict Adverse Outcomes for Patients Undergoing Emergency Laparotomy.

Journal: ANZ journal of surgery
Published Date: May 19, 2025

Abstract

BACKGROUND: Emergency laparotomy is associated with high rates of morbidity and mortality. Accurate, individualised risk prediction models can be used to improve shared decision-making, discharge planning and enhance patient flow. This study used the ANZELA-QI database to apply novel machine learning models to stratify the risk of adverse outcomes in patients undergoing emergency laparotomy.

Authors

  • Dafydd Jones
    Department of General Surgery, Royal Hobart Hospital, Hobart, Australia.
  • Joshua Blum
    Department of General Surgery, Royal Hobart Hospital, Hobart, Tasmania, Australia.
  • Catherine Cartwright
    Department of General Surgery, Royal Hobart Hospital, Hobart, Australia.
  • Nikki Verhagen
    University of Tasmania, School of Medicine, Hobart, Australia.
  • Steven Xu
    Department of Medical Administration, Royal Hobart Hospital, Hobart, Australia.
  • Benjamin Denholm
    Department of General Surgery, Royal Hobart Hospital, Hobart, Australia.
  • Lucinda Southcott
    Department of General Surgery, Royal Hobart Hospital, Hobart, Australia.
  • Richard Turner
    Department of General Surgery, Royal Hobart Hospital, Hobart, Tasmania, Australia.

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