Development and Internal Validation of Machine Learning Algorithms for Predicting Hyponatremia After TJA.

Journal: The Journal of bone and joint surgery. American volume
PMID: 34898530

Abstract

BACKGROUND: The development of hyponatremia after total joint arthroplasty (TJA) may lead to several adverse events and is associated with prolonged inpatient length of stay as well as increased hospital costs. The purpose of this study was to develop and internally validate machine learning algorithms for predicting hyponatremia after TJA.

Authors

  • Kyle N Kunze
    Department of Orthopedic Surgery, Hospital for Special Surgery, New York, NY, USA.
  • Peter K Sculco
    The Stavros Niarchos Foundation Complex Joint Reconstruction Center, Department of Orthopaedic Surgery, Hospital for Special Surgery, Washington, District of Columbia, USA.
  • Haoyan Zhong
    Department of Anesthesiology, Weill Cornell Medical College, New York, NY.
  • Stavros G Memtsoudis
    Department of Anesthesiology, Weill Cornell Medical College, New York, NY.
  • Michael P Ast
    Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY.
  • Thomas P Sculco
    Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY.
  • Kethy M Jules-Elysee
    Department of Anesthesiology, Weill Cornell Medical College, New York, NY.

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