A Predictive Model for Knee Joint Replacement in Older Women
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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 < 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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