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Background: Functional tests such as the 6‐minute walk test (6MWT) are used in many areas of medicine to monitor disease progression and outcomes of treatment. They are particularly helpful in Orthopaedic surgery as the outcomes have been shown to be responsive to recovery over time. The 6MWT is typically performed in a controlled supervised environment which may limit its widespread use (Terwee et al., Rheumatology (Oxford), 2006, 45, 890–902). The primary aim is to investigate if a patient measured 6MWT is comparable to a 6MWT performed under formal testing conditions (Täger et al., Int. J. Cardiol. 2014, 176, 94–98). Methods: A total of 55 patients undergoing elective primary hip or knee replacement were instructed to use a smart watch to record a 6MWT in their home community environment (C‐6MWT). These measurements were compared to a formally tested 6MWT (F‐6MWT) at 2 separate time periods—6 and 12 weeks post‐surgery. Results: At 6 weeks post operation the Intraclass Correlation Coefficient between a formally tested and a patient self‐administered 6MWT was 0.928 (95% CI 0.832–0.970) and at 12 weeks it was 0.831 (0594–0.935). Conclusion: A patient recorded 6 minute walk test shows high agreement with a formally recorded one under research conditions. This makes this test a suitable way to monitor rehabilitation progression and research outcomes at multiple time periods and in remote situations. [ABSTRACT FROM AUTHOR]

Registries have significant roles in assessing the comparative performance of devices. Ideally, early identification of outliers should use a time-to-event outcome while reducing the confounding effects of other components in the device and patient characteristics. Machine learning (ML), which contains self-learning algorithms, is one approach to consider many variables simultaneously to reduce the impact of confounding. The principal objective of this study was to investigate the effectiveness of using either random survival forest (RSF) or regularised/unregularised Cox regression to account for patient and associated device confounding factors in comparison with current standard techniques. This study evaluated RSF and regularised/unregularised Cox regression using data from the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) to detect outlier devices among 213 individual primary total hip components performed in 163,356 primary procedures from 1 January 2015 to the end of 2019. Device components and patient characteristics were the inputs, and time to first revision surgery was the primary outcome treated as a censored case for death. The effectiveness of the ML approaches was assessed based on the ability to detect the outliers identified by the AOANJRR standard approach. In the study cohort, the standardised AOANJRR approach identified three acetabular components and seven femoral stems as outliers. The ML approaches identified some but not all the outliers detected by the AOANJRR. Both the methods identified three of the same femoral stems, and the RSF identified the other five components, including two of the same acetabular cups and three of the same femoral stems. In addition, both the RSF and Cox techniques detected a number of additional device components that were not previously identified by the standard approach. The results showed that ML may be able to offer a supplementary approach to enhance the early identification of outlier devices. Random survival forest was a more comparable technique to the AOANJRR standard than the Cox regression, but further studies are required to better understand the potential of ML to improve the early identification of outliers. [ABSTRACT FROM AUTHOR]

Background: Obesity is a known risk factor for the development of osteoarthritis and the subsequent need for joint replacement. Weight loss has been shown to reduce pain, disability, and the need for joint replacement, particularly in patients with knee osteoarthritis. The aim of this study was to investigate pre‐operative weight change in patients with hip, knee, and shoulder osteoarthritis at a regional, public hospital in Australia, to identify opportunities for pre‐operative weight‐loss intervention. Methods: A retrospective review of patients who underwent elective primary total hip (THR), knee (TKR), and shoulder (TSR) replacement for osteoarthritis was conducted between December 2019 and December 2022. BMI data were collected at three time points: (1) general practitioner (GP) referral; (2) orthopaedic clinic review; and (3) pre‐admission clinic (PAC) assessment. Results: A total of 496 patients were included in the study, of which 205 underwent THR, 251 underwent TKR, and 40 underwent TSR. The mean patient age was 67 years, and 46.4% were female. At the time of GP referral, the mean body mass index (BMI) was 31.4 kg/m2. Across the study period, only 2% of patients experienced clinically significant weight loss pre‐operatively (≥5% of total body weight). Conclusion: This study has demonstrated that very few patients lose weight prior to undergoing joint replacement in the public sector in Australia. This highlights the need for targeted non‐surgical weight loss interventions for patients currently awaiting joint replacement. [ABSTRACT FROM AUTHOR]

Background: There are variations in the performance of individual prostheses used in hip replacements. Some of which have unexpectedly higher revision rates – outliers. The Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) has established a standardised multi-stage approach for identifying these devices. This is done by comparing the revision rates of individual prostheses to all other prostheses in class, with the exception of large head metal-on-metal (LHMoM) prostheses. However, improvements in device design and performance over time have required a need to reconsider the comparator group. This study aimed to identify a more specific comparator to better reflect contemporary surgical practice. Methods: The time to first revision was estimated on the data of 413,417 primary total conventional hip replacements undertaken for osteoarthritis (OA) from 01 January 2003 to 31 December 2019. Survivorship analyses with stepwise exclusions were undertaken. The first exclusion was LHMoM, followed by other non-modern bearing surfaces (defined as all the bearing couples except metal or ceramic heads on cross-linked polyethylene and mixed ceramic-on-ceramic), and then devices with modular neck-stem design or used for specific purposes (incl. constrained, dual-mobility, and head size <28 mm). Lastly, all remaining prostheses previously identified as having a higher than anticipated rate of revision (HTARR) were also excluded. Results: These exclusions progressively reduced the cumulative percent revision (CPR) rate. The final comparator, which only includes satisfactory-performed prostheses of contemporary design and use, has a 10-year CPR of 4.30% (95% CI, 4.2–4.41) which is lower than 4.93% (95% CI, 4.84–5.02) for the current comparator used by the AOANJRR (all prostheses excluding LHMOM). Over the study period, 13 additional components were identified utilising the modified comparator. Conclusions: The calculation of the comparator revision rate should be re-evaluated to include only modern prosthesis constructs to ensure that poorly performing prostheses are identified early. [ABSTRACT FROM AUTHOR]