Aims. A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three patient-reported outcome measures (PROMs), our aim was: 1) to assess machine learning (ML), the simple pre-surgery PROM score, and logistic-regression (LR)-derived performance in their prediction of whether patients undergoing HA or KA achieve an improvement as high or higher than a calculated MCID; and 2) to test whether ML is able to outperform LR or pre-surgery PROM scores in predictive performance. Methods. MCIDs were derived using the change difference method in a sample of 1,843 HA and 1,546 KA patients. An artificial neural network, a gradient boosting machine, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic net, random forest, LR, and pre-surgery PROM scores were applied to predict MCID for the following PROMs: EuroQol five-dimension, five-level questionnaire (EQ-5D-5L), EQ visual analogue scale (EQ-VAS), Hip disability and Osteoarthritis Outcome Score-Physical Function Short-form (HOOS-PS), and Knee injury and Osteoarthritis Outcome Score-Physical Function Short-form (KOOS-PS). Results. Predictive performance of the best models per outcome ranged from 0.71 for HOOS-PS to 0.84 for EQ-VAS (HA sample). ML statistically significantly outperformed LR and pre-surgery PROM scores in two out of six cases. Conclusion. MCIDs can be predicted with reasonable performance. ML was able to outperform traditional methods, although only in a minority of cases. Cite this article: Bone Joint Res 2023;12(9):512–521

Objectives. To assess the responsiveness and ceiling/floor effects of the Forgotten Joint Score -12 and to compare these with that of the more widely used Oxford Hip Score (OHS) in patients six and 12 months after primary total hip arthroplasty. Methods. We prospectively collected data at six and 12 months following total hip arthroplasty from 193 patients undergoing surgery at a single centre. Ceiling effects are outlined with frequencies for patients obtaining the lowest or highest possible score. Change over time from six months to 12 months post-surgery is reported as effect size (Cohen’s d). Results. The mean OHS improved from 40.3 (. sd. 7.9) at six months to 41.9 (. sd. 7.2) at 12 months. The mean FJS-12 improved from 56.8 (. sd. 30.1) at six months to 62.1 (. sd. 29.0) at 12 months. At six months, 15.5% of patients reached the best possible score (48 points) on the OHS and 8.3% obtained the best score (100 points) on the FJS-12. At 12 months, this percentage increased to 20.8% for the OHS and to 10.4% for the FJS-12. In terms of the effect size (Cohen’s d), the change was d = 0.10 for the OHS and d = 0.17 for the FJS-12. Conclusions. The FJS-12 is more responsive to change between six and 12 months following total hip arthroplasty than is the OHS, with the measured ceiling effect for the OHS twice that of the FJS-12. The difference in effect size of change results in substantial differences in required sample size if aiming to detect change between these two time points. This has important implications for powering clinical trials with patient-reported measures as the primary outcome. Cite this article: Dr D. F. Hamilton. Responsiveness and ceiling effects of the Forgotten Joint Score-12 following total hip arthroplasty. Bone Joint Res 2016;5:87–91. DOI: 10.1302/2046-3758.53.2000480

Objectives

The Oxford Hip and Knee Scores (OHS, OKS) have been demonstrated to vary according to age and gender, making it difficult to compare results in cohorts with different demographics. The aim of this paper was to calculate reference values for different patient groups and highlight the concept of normative reference data to contextualise an individual’s outcome.