Application of machine learning models to identify predictors of good outcome after laparoscopic fundoplication.

Journal: Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
PMID: 40122374

Abstract

BACKGROUND: Laparoscopic fundoplication remains the gold standard treatment for gastroesophageal reflux disease. However, 10% to 20% of patients experience new, persistent, or recurrent symptoms warranting further treatment. Potential predictors for the best outcome after laparoscopic fundoplication were tested using a mature prospectively maintained database.

Authors

  • Rippan N Shukla
    Flinders University Discipline of Surgery, College of Medicine and Public Health, Flinders Medical Centre, Bedford Park, South Australia, Australia.
  • Richard Woodman
    Flinders University Discipline of Surgery, College of Medicine and Public Health, Flinders Medical Centre, Bedford Park, South Australia, Australia.
  • Jennifer C Myers
    Flinders University Discipline of Surgery, College of Medicine and Public Health, Flinders Medical Centre, Bedford Park, South Australia, Australia; Discipline of Surgery, Department of Surgery, The Queen Elizabeth Hospital, The University of Adelaide, Woodville, South Australia, Australia.
  • David I Watson
    Flinders University Discipline of Surgery, College of Medicine and Public Health, Flinders Medical Centre, Bedford Park, South Australia, Australia.
  • Tim Bright
    Flinders University Discipline of Surgery, College of Medicine and Public Health, Flinders Medical Centre, Bedford Park, South Australia, Australia.
  • Sarah K Thompson
    Flinders University Discipline of Surgery, College of Medicine and Public Health, Flinders Medical Centre, Bedford Park, South Australia, Australia. Electronic address: sarah.thompson@flinders.edu.au.

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