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. 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).Aims
Methods
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. 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).Objectives
Methods
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. We accessed prospectively collected OHS and OKS data for patients
undergoing lower limb joint arthroplasty at a single orthopaedic
teaching hospital during a five-year period.
T-scores were calculated based on the OHS and OKS distributions. Objectives
Methods
