Machine learning to predict periprosthetic joint infections following primary total hip arthroplasty using a national database.

Journal: Archives of orthopaedic and trauma surgery
PMID: 39820648

Abstract

INTRODUCTION: Periprosthetic joint infection (PJI) following total hip arthroplasty (THA) remains a devastating complication for patients and surgeons. Given the implications of these infections and the current paucity of risk calculators utilizing machine learning (ML), this study aimed to develop an ML algorithm that could accurately identify risk factors for developing a PJI following primary THA using a national database.

Authors

  • Mehdi S Salimy
    Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
  • Anirudh Buddhiraju
    Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Tony L-W Chen
    Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
  • Ashish Mittal
    Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Pengwei Xiao
    Mechanical Engineering, USA.
  • Young-Min Kwon

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