High correlation of the Oxford Knee Score with postoperative pain, but not with performance-based functioning
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The Oxford Knee Score (OKS) is a widely known patient-related outcome measure (PROM) to determine pain and knee functioning before and after total knee arthroplasty (TKA). Self-reported function is mainly influenced by change in pain; therefore, it was hypothesized that the OKS correlates more with pain than with performance-based functioning.
In a prospective cohort of 88 patients, who had a cementless mobile-bearing TKA, included in a randomized clinical trial, the correlation between the overall OKS, and its subscales for pain (PCS) and function (FCS), with performance-based functioning using the DynaPort® Knee Score (DKS), visual analogue scale score for pain (VAS) and the Knee Society Score (KSS) was evaluated. All scores were measured preoperatively, 6 months and 1 year after surgery. Overall change in outcome over time was analysed until 5 years after surgery.
All scores improved over time. The DKS was influenced by sex, preoperative BMI and age. The internal consistency of the OKS PCS increased over time, whereas the OKS FCS remained the same. The mean postoperative OKS FCS showed moderate correlation with the DKS (r = 0.65, p < 0.001), and the mean postoperative OKS and OKS PCS showed high correlation with the VAS (r = −0.79 and r = −0.82, respectively, p < 0.001). The mean postoperative KSS showed high correlations with the OKS (r = 0.80, p < 0.001), the OKS PCS (r = 0.72 p < 0.001) and OKS FCS (r = 0.74, p < 0.001).
The postoperative OKS and the OKS PCS showed high correlation with pain, but only the postoperative OKS FCS was well correlated with performance-based functioning. This suggests that the OKS is more related to pain and tells us less on postoperative functioning. This is important when the OKS as PROM is used to evaluate the quality of orthopaedic care of patients with TKA.
Level of evidence
KeywordsOxford Knee Score DynaPort Knee Score Correlations Total knee arthroplasty
We thank T. van der Ploeg for his valuable assistance during statistical analysis.
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