Insights from Inputs: Enhancing Revision Total Joint Arthroplasty Resource Allocation with Machine Learning Prediction.

Journal: The Journal of arthroplasty
Published Date: May 6, 2025

Abstract

BACKGROUND: Revision total knee arthroplasty (rTKA) and revision total hip arthroplasty (rTHA) are among the most resource-intensive orthopaedic procedures. The primary aim of this study was to compare the accuracy of machine learning (ML) models between administrative and institutional datasets for predicting duration of surgery (DOS), length of stay (LOS), and 30-day hospital readmission for rTKA and rTHA based on preoperative factors and identify significant predictive features.

Authors

  • Johnathan R Lex
    Division of Orthopaedic Surgery, University of Toronto, Toronto, ON, Canada; Orthopaedic Biomechanics Lab, Sunnybrook Research Institute, Toronto, ON, Canada.
  • Bahar Entezari
    Division of Orthopaedic Surgery, University of Toronto, Toronto, ON, Canada; Granovsky Gluskin Division of Orthopaedics, Mount Sinai Hospital, Toronto, ON, Canada. Electronic address: bahar.entezari@mail.utoronto.ca.
  • Aazad Abbas
    Orthopaedic Biomechanics Lab, Sunnybrook Research Institute, Toronto, ON, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
  • Jay Toor
    Division of Orthopaedic Surgery, University of Toronto, Toronto, ON, Canada.
  • David J Backstein
    Division of Orthopaedic Surgery, University of Toronto, Toronto, ON, Canada; Granovsky Gluskin Division of Orthopaedics, Mount Sinai Hospital, Toronto, ON, Canada.
  • Cari Whyne
    Division of Orthopaedic Surgery, University of Toronto, Toronto, ON, Canada; Granovsky Gluskin Division of Orthopaedics, Mount Sinai Hospital, Toronto, ON, Canada; Division of Orthopaedic Surgery, Sunnybrook Health Science Centre, Toronto, ON, Canada.
  • Bheeshma Ravi
    Division of Orthopaedic Surgery, University of Toronto, Toronto, ON, Canada; Division of Orthopaedic Surgery, Sunnybrook Health Science Centre, Toronto, ON, Canada.

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