Objectives

Wear debris released from bearing surfaces has been shown to provoke negative immune responses in the recipient. Excessive wear has been linked to early failure of prostheses. Analysis using coordinate measuring machines (CMMs) can provide estimates of total volumetric material loss of explanted prostheses and can help to understand device failure. The accuracy of volumetric testing has been debated, with some investigators stating that only protocols involving hundreds of thousands of measurement points are sufficient. We looked to examine this assumption and to apply the findings to the clinical arena.

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

Aims

The present study aimed to investigate whether patients with inflammatory bowel disease (IBD) undergoing joint arthroplasty have a higher incidence of adverse outcomes than those without IBD.

Aims

To develop and validate patient-centred algorithms that estimate individual risk of death over the first year after elective joint arthroplasty surgery for osteoarthritis.

Objectives

Wound complications are reported in up to 10% hip and knee arthroplasties and there is a proven association between wound complications and deep prosthetic infections. In this randomised controlled trial (RCT) we explore the potential benefits of a portable, single use, incisional negative pressure wound therapy dressing (iNPWTd) on wound exudate, length of stay (LOS), wound complications, dressing changes and cost-effectiveness following total hip and knee arthroplasties.

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.

Objectives

Patient function after arthroplasty should ideally quickly improve. It is not known which peri-operative function assessments predict length of stay (LOS) and short-term functional recovery. The objective of this study was to identify peri-operative functions assessments predictive of hospital LOS and short-term function after hospital discharge in hip or knee arthroplasty patients.

Objectives

We wanted to investigate regional variations in the organisms reported to be causing peri-prosthetic infections and to report on prophylaxis regimens currently in use across England.

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.