Super Learner Enhances Postoperative Complication Prediction in Colorectal Surgery.

Journal: Annals of surgery
Published Date: Jul 23, 2025

Abstract

OBJECTIVE: To determine if a Super Learner (SL) machine learning approach could improve the predictive accuracy of the American College of Surgeons Risk Calculator (ACS-RC) for postoperative complications in patients undergoing colorectal surgery.

Authors

  • Tommaso Violante
    Department of Colon and Rectal Surgery, Mayo Clinic, Rochester, MN, USA.
  • Davide Ferrari
    Department of Surgical, Medical, Dental and Morphological Sciences, University of Modena and Reggio Emilia, Modena, Italy.
  • Marco Novelli
    Dept of Pathology, University College London Hospital (UCLH), London, United Kingdom.
  • William R Perry
    Department of Colon and Rectal Surgery, Mayo Clinic, Rochester, MN, USA.
  • Kellie L Mathis
    Department of Colon and Rectal Surgery, Mayo Clinic, Rochester, MN, USA.
  • Eric J Dozois
    Department of Colon and Rectal Surgery, Mayo Clinic, Rochester, MN, USA.
  • David W Larson
    Department of Surgery Mayo Clinic College of Medicine, Rochester MN.

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