Machine learning model outperforms the ACS Risk Calculator in predicting non-home discharge following primary total knee arthroplasty.

Journal: Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
Published Date: Sep 30, 2024

Abstract

PURPOSE: Despite the increase in outpatient total knee arthroplasty (TKA) procedures, many patients are still discharged to non-home locations following index surgery. The ability to accurately predict non-home discharge (NHD) following TKAs has the potential to promote a reduction in associated adverse events and excess healthcare costs. This study aimed to evaluate whether a machine learning (ML) model could outperform the American College of Surgeons (ACS) Risk Calculator in predicting NHD following TKA, using the same set of clinical variables. We hypothesised that the ML model would outperform the ACS Risk Calculator.

Authors

  • Blake M Bacevich
    Bioengineering Laboratory, Department of Orthopaedic Surgery, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Tony Lin-Wei Chen
    Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Anirudh Buddhiraju
    Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Michelle R Shimizu
    Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
  • Henry H Seo
    Bioengineering Laboratory, Department of Orthopaedic Surgery, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Young-Min Kwon

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