A Predictive Model for Knee Joint Replacement in Older Women

Joshua R Lewis, Satvinder S Dhaliwal, Kun Zhu and Richard L Prince

PLOS ONE, 2013, vol. 8, issue 12, 1-

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

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0083665

DOI: 10.1371/journal.pone.0083665

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