Predicting patient outcomes and risk for revision surgery after hip and knee replacement surgery: study protocol for a comparison of modelling approaches using the Swiss National Joint Registry (SIRIS).

Journal: Diagnostic and prognostic research
Published Date:

Abstract

BACKGROUND: Prediction of postoperative patient-reported outcomes and risk for revision surgery after total hip arthroplasty (THA) or total knee arthroplasty (TKA) can inform clinical decision-making, health resource allocation, and care planning. Machine learning (ML) algorithms are increasingly used as an alternative to traditional logistic regression (LR) prediction, but there is uncertainty about their superiority in overall model performance. The aim of this study is to compare the predictive performance of LR with different ML approaches for predicting patient outcomes and risk for revision surgery after THA and TKA.

Authors

  • Léonie Hofstetter
    Musculoskeletal Epidemiology Research Group, University of Zurich and Balgrist University Hospital, Forchstrasse 340, Zurich, 8008, Switzerland.
  • Nathalie Schweyckart
    Musculoskeletal Epidemiology Research Group, University of Zurich and Balgrist University Hospital, Forchstrasse 340, Zurich, 8008, Switzerland.
  • Christof Seiler
    Department of Advanced Computing Sciences, Maastricht University, Maastricht, the Netherlands.
  • Christian Brand
    Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.
  • Laura C Rosella
    Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada laura.rosella@utoronto.ca.
  • Mazda Farshad
    Balgrist University Hospital, 8008, Zurich, Switzerland.
  • Milo A Puhan
    Department of Epidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
  • Cesar A Hincapié
    Musculoskeletal Epidemiology Research Group, University of Zurich and Balgrist University Hospital, Forchstrasse 340, Zurich, 8008, Switzerland. cesar.hincapie@uzh.ch.

Keywords

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