Aims. Recent studies have suggested that corticosteroid injections into the knee may harm the joint resulting in cartilage loss and possibly accelerating the progression of osteoarthritis (OA). The aim of this study was to assess whether patients with, or at risk of developing, symptomatic osteoarthritis of the knee who receive intra-articular corticosteroid injections have an increased risk of requiring arthroplasty. Methods. We used data from the Osteoarthritis Initiative (OAI), a multicentre observational cohort study that followed 4,796 patients with, or at risk of developing, osteoarthritis of the knee on an annual basis with follow-up available up to nine years. Increased risk for symptomatic OA was defined as frequent knee symptoms (pain, aching, or stiffness) without radiological evidence of OA and two or more risk factors, while OA was defined by the presence of both femoral osteophytes and frequent symptoms in one or both knees. Missing data were imputed with multiple imputations using chained equations. Time-dependent propensity score matching was performed to match patients at the time of receving their first injection with controls. The effect of corticosteroid injections on the rate of subsequent (total and partial) knee arthroplasty was estimated using Cox proportional-hazards survival analyses. Results. After removing patients lost to follow-up, 3,822 patients remained in the study. A total of 249 (31.3%) of the 796 patients who received corticosteroid injections, and 152 (5.0%) of the 3,026 who did not, had knee arthroplasty. In the matched cohort, Cox proportional-hazards regression resulted in a hazard ratio of 1.57 (95% confidence interval (CI) 1.37 to 1.81; p < 0.001) and each injection increased the absolute risk of arthroplasty by 9.4% at nine years’ follow-up compared with those who did not receive injections. Conclusion. Corticosteroid injections seem to be associated with an increased risk of knee arthroplasty in patients with, or at risk of developing, symptomatic OA of the knee. These findings suggest that a conservative approach regarding the treatment of these patients with corticosteroid injections should be recommended. Cite this article: Bone Joint J 2020;102-B(5):586–592

Aims. Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials. Methods. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures. Results. Out of 1,160 studies initially identified, 39 were included. Most studies (85%) were published between 2020 and 2024, with 82% using publicly available datasets, primarily the Osteoarthritis Initiative. ML methods were predominantly supervised, with significant variability in the definitions of OA progression: most studies focused on structural changes (59%), while fewer addressed pain progression or both. Deep learning was used in 44% of studies, while automated ML was used in 5%. There was a lack of standardization in evaluation metrics and limited external validation. Interpretability was explored in 54% of studies, primarily using SHapley Additive exPlanations. Conclusion. Our systematic review demonstrates the feasibility of ML models in predicting OA progression, but also uncovers critical limitations that currently restrict their clinical applicability. Future priorities should include diversifying data sources, standardizing outcome measures, enforcing rigorous validation, and integrating more sophisticated algorithms. This paradigm shift from predictive modelling to actionable clinical tools has the potential to transform patient care and disease management in orthopaedic practice. Cite this article: Bone Joint J 2024;106-B(11):1216–1222

Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered.

Cite this article: Bone Joint J 2022;104-B(12):1292–1303.

We studied whether the presence of lateral osteophytes on plain radiographs was a predictor for the quality of cartilage in the lateral compartment of patients with varus osteoarthritic of the knee (Kellgren and Lawrence grade 2 to 3).

The baseline MRIs of 344 patients from the Osteoarthritis Initiative (OAI) who had varus osteoarthritis (OA) of the knee on hip-knee-ankle radiographs were reviewed. Patients were categorised using the Osteoarthritis Research Society International (OARSI) osteophyte grading system into 174 patients with grade 0 (no osteophytes), 128 grade 1 (mild osteophytes), 28 grade 2 (moderate osteophytes) and 14 grade 3 (severe osteophytes) in the lateral compartment (tibia). All patients had Kellgren and Lawrence grade 2 or 3 arthritis of the medial compartment. The thickness and volume of the lateral cartilage and the percentage of full-thickness cartilage defects in the lateral compartment was analysed.

There was no difference in the cartilage thickness or cartilage volume between knees with osteophyte grades 0 to 3. The percentage of full-thickness cartilage defects on the tibial side increased from < 2% for grade 0 and 1 to 10% for grade 3.

The lateral compartment cartilage volume and thickness is not influenced by the presence of lateral compartment osteophytes in patients with varus OA of the knee. Large lateral compartment osteophytes (grade 3) increase the likelihood of full-thickness cartilage defects in the lateral compartment.

Cite this article: Bone Joint J 2015;97-B:1634–9.

Early and accurate prediction of hospital length-of-stay (LOS) in patients undergoing knee replacement is important for economic and operational reasons. Few studies have systematically developed a multivariable model to predict LOS. We performed a retrospective cohort study of 1609 patients aged ≥ 50 years who underwent elective, primary total or unicompartmental knee replacements. Pre-operative candidate predictors included patient demographics, knee function, self-reported measures, surgical factors and discharge plans. In order to develop the model, multivariable regression with bootstrap internal validation was used. The median LOS for the sample was four days (interquartile range 4 to 5). Statistically significant predictors of longer stay included older age, greater number of comorbidities, less knee flexion range of movement, frequent feelings of being down and depressed, greater walking aid support required, total (versus unicompartmental) knee replacement, bilateral surgery, low-volume surgeon, absence of carer at home, and expectation to receive step-down care. For ease of use, these ten variables were used to construct a nomogram-based prediction model which showed adequate predictive accuracy (optimism-corrected R² = 0.32) and calibration. If externally validated, a prediction model using easily and routinely obtained pre-operative measures may be used to predict absolute LOS in patients following knee replacement and help to better manage these patients.

Cite this article: Bone Joint J 2013;95-B:1490–6.