Machine learning-based prediction for incidence of endoscopic retrograde cholangiopancreatography after emergency laparoscopic cholecystectomy: A retrospective, multicenter cohort study.

Journal: Surgical endoscopy
PMID: 39820602

Abstract

BACKGROUND: Laparoscopic cholecystectomy is the preferred treatment for symptomatic cholelithiasis and acute cholecystitis, with increasing applications even in severe cases. However, the possibility of postoperative endoscopic retrograde cholangiopancreatography (ERCP) to manage choledocholithiasis or biliary injuries poses significant clinical challenges. This study aimed to develop a predictive model for ERCP incidence following emergency laparoscopic cholecystectomy using advanced machine learning techniques.

Authors

  • Shota Akabane
    Department of General Surgery, Shonan Fujisawa Tokushukai Hospital, 1-5-1 Tsujidokandai, Fujisawa, Kanagawa, Japan. akap.sh.3381@gmail.com.
  • Masao Iwagami
    Department of Health Services Research, University of Tsukuba, Tsukuba, Japan.
  • Nicholas Bell-Allen
    Department of General Surgery, Fiona Stanley Hospital, 11 Robin Warren Dr, Murdoch, WA, Australia.
  • Suresh Navadgi
    Department of General Surgery, Royal Perth Hospital, Victoria Square, Perth, WA, Australia.
  • Toshiyasu Kawahara
    Department of Hepatopancreatic Biliary Surgery, Shonan Kamakura General Hospital, 1370-1 Okamoto, Kamakura, Kanagawa, Japan.
  • Mayank Bhandari
    Department of General Surgery, Fiona Stanley Hospital, 11 Robin Warren Dr, Murdoch, WA, Australia.

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