Predicting the onset of end-stage knee osteoarthritis over two- and five-years using machine learning.

Journal: Seminars in arthritis and rheumatism
Published Date: Mar 16, 2024

Abstract

OBJECTIVE: Identifying participants who will progress to advanced stage in knee osteoarthritis (KOA) trials remains a significant challenge. Current tools, relying on total knee replacements (TKR), fall short in reliability due to the extraneous factors influencing TKR decisions. Acknowledging these limitations, our study identifies a critical need for a more robust metric to assess severe KOA. The end-stage KOA (esKOA) measure, which combines symptomatic and radiographic criteria, serves as a solid indicator. To enhance future trials that use esKOA as an endpoint, our study focuses on developing and validating a machine-learning tool to identify individuals likely to develop esKOA within 2 to 5 years.

Authors

  • Zubeyir Salis
    Division of Rheumatology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland; School of Human Sciences, the University of Western Australia, Perth, WA, Australia; Centre for Big Data Research in Health, the University of New South Wales, Kensington, NSW, Australia. Electronic address: Zubeyir.Salis@etu.unige.ch.
  • Jeffrey B Driban
    UMass Chan Medical School, Department of Population and Quantitative Health Sciences, Worcester, MA, USA.
  • Timothy E McAlindon
    Division of Rheumatology, Allergy, and Immunology; Tufts Medical Center, Boston, MA, USA.

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