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
This study aimed to investigate the estimated change in primary and revision arthroplasty rate in the Netherlands and Denmark for hips, knees, and shoulders during the COVID-19 pandemic in 2020 (COVID-period). Additional points of focus included the comparison of patient characteristics and hospital type (2019 vs COVID-period), and the estimated loss of quality-adjusted life years (QALYs) and impact on waiting lists. All hip, knee, and shoulder arthroplasties (2014 to 2020) from the Dutch Arthroplasty Register, and hip and knee arthroplasties from the Danish Hip and Knee Arthroplasty Registries, were included. The expected number of arthroplasties per month in 2020 was estimated using Poisson regression, taking into account changes in age and sex distribution of the general Dutch/Danish population over time, calculating observed/expected (O/E) ratios. Country-specific proportions of patient characteristics and hospital type were calculated per indication category (osteoarthritis/other elective/acute). Waiting list outcomes including QALYs were estimated by modelling virtual waiting lists including 0%, 5% and 10% extra capacity.Aims
Methods
The aim of this study was to estimate the 90-day periprosthetic joint infection (PJI) rates following total knee arthroplasty (TKA) and total hip arthroplasty (THA) for osteoarthritis (OA). This was a data linkage study using the New South Wales (NSW) Admitted Patient Data Collection (APDC) and the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR), which collect data from all public and private hospitals in NSW, Australia. Patients who underwent a TKA or THA for OA between 1 January 2002 and 31 December 2017 were included. The main outcome measures were 90-day incidence rates of hospital readmission for: revision arthroplasty for PJI as recorded in the AOANJRR; conservative definition of PJI, defined by T84.5, the PJI diagnosis code in the APDC; and extended definition of PJI, defined by the presence of either T84.5, or combinations of diagnosis and procedure code groups derived from recursive binary partitioning in the APDC.Aims
Methods
Day-case arthroplasty is gaining popularity in Europe. We report outcomes from the first 12 months following implementation of a day-case pathway for unicompartmental knee arthroplasty (UKA) and total hip arthroplasty (THA) in an NHS hospital. A total of 47 total hip arthroplasty (THA) and 24 unicompartmental knee arthroplasty (UKA) patients were selected for the day-case arthroplasty pathway, based on preoperative fitness and agreement to participate. Data were likewise collected for a matched control group (n = 58) who followed the standard pathway three months prior to the implementation of the day-case pathway. We report same-day discharge (SDD) success, reasons for delayed discharge, and patient-reported outcomes. Overall length of stay (LOS) for all lower limb arthroplasty was recorded to determine the wider impact of implementing a day-case pathway.Aims
Methods
In 2020, the COVID-19 pandemic meant that proceeding with elective surgery was restricted to minimize exposure on wards. In order to maintain throughput of elective cases, our hospital (St Michaels Hospital, Toronto, Canada) was forced to convert as many cases as possible to same-day procedures rather than overnight admission. In this retrospective analysis, we review the cases performed as same-day arthroplasty surgeries compared to the same period in the previous 12 months. We conducted a retrospective analysis of patients undergoing total hip and knee arthroplasties over a three-month period between October and December in 2019, and again in 2020, in the middle of the COVID-19 pandemic. Patient demographics, number of outpatient primary arthroplasty cases, length of stay for admissions, 30-day readmission, and complications were collated.Aims
Methods
Restarting elective services presents a challenge to restore and improve many of the planned patient care pathways which have been suspended during the response to the COVID-19 pandemic. A significant backlog of planned elective work has built up representing a considerable volume of patient need. We aimed to investigate the health status, quality of life, and the impact of delay for patients whose referrals and treatment for symptomatic joint arthritis had been delayed as a result of the response to COVID-19. We interviewed 111 patients referred to our elective outpatient service and whose first appointments had been cancelled as a result of the response to the COVID-19 pandemic.Aim
Methods
