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    A Predictive Model for Knee Joint Replacement in Older Women

    Access Status
    Open access via publisher
    Authors
    Lewis, Joshua
    Dhaliwal, Satvinder
    Zhu, Kun
    Prince, Richard
    Date
    2013
    Type
    Journal Article
    
    Metadata
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    Citation
    Lewis, Joshua and Dhaliwal, Satvinder and Zhu, Kun and Prince, Richard. 2013. A Predictive Model for Knee Joint Replacement in Older Women. PLoS ONE. 8 (12): e83665.
    Source Title
    PLoS ONE
    DOI
    10.1371/journal.pone.0083665
    ISSN
    19326203
    URI
    http://hdl.handle.net/20.500.11937/48115
    Collection
    • Curtin Research Publications
    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 &lt; 0.001). These factors in addition to age demonstrated good discrimination with a C-statistic of 0.79 ± 0.02 as well as calibration determined by the Hosmer-Lemeshow Goodness-of-Fit test.For clinical recommendations, three categories of risk for 10-year knee replacement were selected; low < 5%; moderate 5 to < 10% and high ≥ 10% predicted risk. The actual risk of knee replacement was; low 16 / 741 (2.2%); moderate 32 / 330 (9.7%) and high 81 / 391 (20.7%), P < 0.001. Internal validation of this 5-variable model on 6-year knee replacements yielded a similar C-statistic of 0.81 ± 0.02, comparable to the WOMAC weighted score; C-statistic 0.75 ± 0.03, P = 0.064. In conclusion 5 easily obtained patient self-reported risk factors predict 10-year KR risk well in this population. This algorithm should be considered as the basis for a patient-based risk calculator to assist in the development of treatment regimens to reduce the necessity for surgery in high risk groups such as the elderly.

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