Can We Improve Prediction of Adverse Surgical Outcomes? Development of a Surgical Complexity Score Using a Novel Machine Learning Technique.

Journal: Journal of the American College of Surgeons
Published Date: Jan 1, 2020

Abstract

BACKGROUND: An optimal method to quantify surgical complexity using patient comorbidities derived from administrative billing data is lacking. We sought to develop a novel, easy-to-use surgical Complexity Score to accurately predict adverse outcomes among patients undergoing elective surgery.

Authors

  • J Madison Hyer
    Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Cancer Hospital and Solove Research Institute, Columbus, OH.
  • Susan White
    Department of Financial Services, The Ohio State University Wexner Medical Center and James Cancer Hospital and Solove Research Institute, Columbus, OH.
  • Jordan Cloyd
    Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Cancer Hospital and Solove Research Institute, Columbus, OH.
  • Mary Dillhoff
    Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Cancer Hospital and Solove Research Institute, Columbus, OH.
  • Allan Tsung
    Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA tsunga@upmc.edu.
  • Timothy M Pawlik
    Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Cancer Hospital and Solove Research Institute, Columbus, OH.
  • Aslam Ejaz
    Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Cancer Hospital and Solove Research Institute, Columbus, OH. Electronic address: aslam.ejaz@osumc.edu.

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