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A Predictive Model for Knee Joint Replacement in Older Women

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  • Joshua R Lewis
  • Satvinder S Dhaliwal
  • Kun Zhu
  • Richard L Prince

Abstract

Knee replacement (KR) is expensive and invasive. To date no predictive algorithms have been developed to identify individuals at high risk of surgery. This study assessed whether patient self-reported risk factors predict 10-year KR in a population-based study of 1,462 women aged over 70 years recruited for the Calcium Intake Fracture Outcome Study (CAIFOS). Complete hospital records of prevalent (1980-1998) and incident (1998-2008) total knee replacement were available via the Western Australian Data Linkage System. Potential risk factors were assessed for predicative ability using a modeling approach based on a pre-planned selection of risk factors prior to model evaluation. There were 129 (8.8%) participants that underwent KR over the 10 year period. Baseline factors including; body mass index, knee pain, previous knee replacement and analgesia use for joint pain were all associated with increased risk, (P

Suggested Citation

  • Joshua R Lewis & Satvinder S Dhaliwal & Kun Zhu & Richard L Prince, 2013. "A Predictive Model for Knee Joint Replacement in Older Women," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.
    • Handle: RePEc:plo:pone00:0083665
    DOI: 10.1371/journal.pone.0083665
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