Can Machine-learning Algorithms Predict Early Revision TKA in the Danish Knee Arthroplasty Registry?

Journal: Clinical orthopaedics and related research
PMID: 32667760

Abstract

BACKGROUND: Revision TKA is a serious adverse event with substantial consequences for the patient. As the demand for TKA rises, reducing the risk of revision TKA is becoming increasingly important. Predictive tools based on machine-learning algorithms could reform clinical practice. Few attempts have been made to combine machine-learning algorithms with data from nationwide arthroplasty registries and, to the authors' knowledge, none have tried to predict the likelihood of early revision TKA.

Authors

  • Anders El-Galaly
    A. El-Galaly, A. Kappel, P. T. Nielsen, S. L. Jensen, Orthopedic Research Unit, Aalborg University Hospital, Aalborg, Denmark.
  • Clare Grazal
    C. Grazal, J. A. Forsberg, Uniformed Services University-Walter Reed Department of Surgery, Bethesda, MD, USA.
  • Andreas Kappel
    A. El-Galaly, A. Kappel, P. T. Nielsen, S. L. Jensen, Orthopedic Research Unit, Aalborg University Hospital, Aalborg, Denmark.
  • Poul Torben Nielsen
    A. El-Galaly, A. Kappel, P. T. Nielsen, S. L. Jensen, Orthopedic Research Unit, Aalborg University Hospital, Aalborg, Denmark.
  • Steen Lund Jensen
    A. El-Galaly, A. Kappel, P. T. Nielsen, S. L. Jensen, Orthopedic Research Unit, Aalborg University Hospital, Aalborg, Denmark.
  • Jonathan A Forsberg
    A. B. Anderson, J. A. Forsberg, Department of Surgery, Division of Orthopaedics, Walter Reed National Military Medical Center, Bethesda, MD, USA.

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