Aims

Vascular compromise due to arterial injury is a rare but serious complication of a proximal humeral fracture. The aims of this study were to report its incidence in a large urban population, and to identify clinical and radiological factors which are associated with this complication. We also evaluated the results of the use of our protocol for the management of these injuries.

A prospective cohort of 222 patients who underwent revision hip replacement between April 2001 and March 2004 was evaluated to determine predictors of function, pain and activity level between one and two years post-operatively, and to define quality of life outcomes using validated patient reported outcome tools. Predictive models were developed and proportional odds regression analyses were performed to identify factors that predict quality of life outcomes at one and two years post-operatively. The dependent outcome variables were the Western Ontario and McMaster Osteoarthritis Index (WOMAC) function and pain scores, and University of California Los Angeles activity scores. The independent variables included patient demographics, operative factors, and objective quality of life parameters, including pre-operative WOMAC, and the Short Form-12 mental component score. There was a significant improvement (t-test, p < 0.001) in all patient quality of life scores. In the predictive model, factors predictive of improved function (original regression analyses, p < 0.05) included a higher pre-operative WOMAC function score (p < 0.001), age between 60 and 70 years (p < 0.037), male gender (p = 0.017), lower Charnley class (p < 0.001) and aseptic loosening being the indication for revision (p < 0.003). Using the WOMAC pain score as an outcome variable, factors predictive of improvement included the pre-operative WOMAC function score (p = 0.001), age between 60 and 70 years (p = 0.004), male gender (p = 0.005), lower Charnley class (p = 0.001) and no previous revision procedure (p = 0.023). The pre-operative WOMAC function score (p = 0.001), the indication for the operation (p = 0.007), and the operating surgeon (p = 0.008) were significant predictors of the activity assessment at follow-up. Predictors of quality of life outcomes after revision hip replacement were established. Although some patient-specific and surgery-specific variables were important, age, gender, Charnley class and pre-operative WOMAC function score had the most robust associations with outcome

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.

Aims

The incidence of bone metastases is between 20% to 75% depending on the type of cancer. As treatment improves, the number of patients who need surgical intervention is increasing. Identifying patients with a shorter life expectancy would allow surgical intervention with more durable reconstructions to be targeted to those most likely to benefit. While previous scoring systems have focused on surgical and oncological factors, there is a need to consider comorbidities and the physiological state of the patient, as these will also affect outcome. The primary aim of this study was to create a scoring system to estimate survival time in patients with bony metastases and to determine which factors may adversely affect this.

In recent years, machine learning (ML) and artificial neural networks (ANNs), a particular subset of ML, have been adopted by various areas of healthcare. A number of diagnostic and prognostic algorithms have been designed and implemented across a range of orthopaedic sub-specialties to date, with many positive results. However, the methodology of many of these studies is flawed, and few compare the use of ML with the current approach in clinical practice. Spinal surgery has advanced rapidly over the past three decades, particularly in the areas of implant technology, advanced surgical techniques, biologics, and enhanced recovery protocols. It is therefore regarded an innovative field. Inevitably, spinal surgeons will wish to incorporate ML into their practice should models prove effective in diagnostic or prognostic terms. The purpose of this article is to review published studies that describe the application of neural networks to spinal surgery and which actively compare ANN models to contemporary clinical standards allowing evaluation of their efficacy, accuracy, and relatability. It also explores some of the limitations of the technology, which act to constrain the widespread adoption of neural networks for diagnostic and prognostic use in spinal care. Finally, it describes the necessary considerations should institutions wish to incorporate ANNs into their practices. In doing so, the aim of this review is to provide a practical approach for spinal surgeons to understand the relevant aspects of neural networks.

Cite this article: Bone Joint J 2021;103-B(9):1442–1448.

Aims

The purpose of this study was to develop a personalized outcome prediction tool, to be used with knee arthroplasty patients, that predicts outcomes (lengths of stay (LOS), 90 day readmission, and one-year patient-reported outcome measures (PROMs) on an individual basis and allows for dynamic modifiable risk factors.

Aims

Debate remains whether the patella should be resurfaced during total knee replacement (TKR). For non-resurfaced TKRs, we estimated what the revision rate would have been if the patella had been resurfaced, and examined the risk of re-revision following secondary patellar resurfacing.