Length of Stay Prediction Models for Oral Cancer Surgery: Machine Learning, Statistical and ACS-NSQIP.

Journal: The Laryngoscope
PMID: 38651539

Abstract

OBJECTIVE: Accurate prediction of hospital length of stay (LOS) following surgical management of oral cavity cancer (OCC) may be associated with improved patient counseling, hospital resource utilization and cost. The objective of this study was to compare the performance of statistical models, a machine learning (ML) model, and The American College of Surgeons National Surgical Quality Improvement Program's (ACS-NSQIP) calculator in predicting LOS following surgery for OCC.

Authors

  • Amirpouyan Namavarian
    Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada.
  • Alexander Gabinet-Equihua
    Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada.
  • Yangqing Deng
    Department of Biostatistics, University Health Network, Toronto, Canada.
  • Shuja Khalid
    Surgical Safety Technologies, Toronto, Ontario, Canada.
  • Hedyeh Ziai
    Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada.
  • Konrado Deutsch
    Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada.
  • Jingyue Huang
    Department of Biostatistics, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada.
  • Ralph W Gilbert
    Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada.
  • David P Goldstein
    Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada.
  • Christopher M K L Yao
    Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada.
  • Jonathan C Irish
    Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada.
  • Danny J Enepekides
    Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada.
  • Kevin M Higgins
    Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada.
  • Frank Rudzicz
    University of Toronto, Toronto, Canada.
  • Antoine Eskander
    Department of Otolaryngology-Head and Neck Surgery, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada.
  • Wei Xu
    College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, 471023 China.
  • John R de Almeida
    Department of Otolaryngology-Head and Neck Surgery, Princess Margaret Cancer Centre/University of Toronto, Toronto, Ontario, Canada.

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