Aims

Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are common orthopaedic procedures requiring postoperative radiographs to confirm implant positioning and identify complications. Artificial intelligence (AI)-based image analysis has the potential to automate this postoperative surveillance. The aim of this study was to prepare a scoping review to investigate how AI is being used in the analysis of radiographs following THA and TKA, and how accurate these tools are.

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The purpose of this study was to determine the association between prior sleeve gastrectomy in patients undergoing primary total hip and knee arthroplasty, and 90-day complications, incidence of revision arthroplasty, and patient-reported outcome scores at final follow-up.

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Breast cancer survivors have known risk factors that might influence the results of total hip arthroplasty (THA) or total knee arthroplasty (TKA). This study evaluated clinical outcomes of patients with breast cancer history after primary THA and TKA.

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Due to the opioid epidemic in the USA, our service progressively decreased the number of opioid tablets prescribed at discharge after primary hip (THA) and knee (TKA) arthroplasty. The goal of this study was to analyze the effect on total morphine milligram equivalents (MMEs) prescribed and post-discharge opioid repeat prescriptions.

Our study has determined the response of C-reactive protein (CRP) after total knee replacement (TKR). The peak level occurs on the second postoperative day and is significantly greater than that after total hip replacement (THR). The level returns to normal at similar times after both procedures. The physiological response to TKR as measured by the area under the CRP/ time curve is significantly greater than that after THR. Rising CRP levels after the third postoperative day may indicate a complication of surgery such as infection.

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Unicompartmental knee arthroplasty (UKA) has a higher risk of revision than total knee arthroplasty (TKA), particularly for younger patients. The outcome of knee arthroplasty is typically defined as implant survival or revision incidence after a defined number of years. This can be difficult for patients to conceptualize. We aimed to calculate the ‘lifetime risk’ of revision for UKA as a more meaningful estimate of risk projection over a patient’s remaining lifetime, and to compare this to TKA.

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Intra-articular administration of antibiotics during primary total knee arthroplasty (TKA) may represent a safe, cost-effective strategy to reduce the risk of acute periprosthetic joint infection (PJI). Vancomycin with an aminoglycoside provides antimicrobial cover for most organisms isolated from acute PJI after TKA. However, the intra-articular doses required to achieve sustained therapeutic intra-articular levels while remaining below toxic serum levels is unknown. The purpose of this study is to determine the intra-articular and serum levels of vancomycin and tobramycin over the first 24 hours postoperatively after intra-articular administration in primary cementless TKA.

Aims. For the increasing number of working-age patients undergoing total hip or total knee arthroplasty (THA/TKA), return to work (RTW) after surgery is crucial. We investigated the association between occupational class and time to RTW after THA or TKA. Methods. Data from the prospective multicentre Longitudinal Leiden Orthopaedics Outcomes of Osteoarthritis Study were used. Questionnaires were completed preoperatively and six and 12 months postoperatively. Time to RTW was defined as days from surgery until RTW (full or partial). Occupational class was preoperatively assessed and categorized into four categories according to the International Standard Classification of Occupations 2008 (blue-/white-collar, high-/low-skilled). Cox regression analyses were conducted separately for THA and TKA patients. Low-skilled blue-collar work was used as the reference category. Results. A total of 360 THA and 276 TKA patients, preoperatively employed, were included. Patients were mainly high-skilled (THA 57%; TKA 41%) or low-skilled (THA 24%; TKA 38%) white-collar workers. Six months post-THA, RTW rates were 78% of low-skilled blue-collar workers compared to 83% to 86% within other occupational classes, increasing after 12 months to 87% to 90% in all occupational classes. Six months post-TKA, RTW rates were 58% of low-skilled and 64% of high-skilled blue-collar workers compared to 80% to 89% of white-collar workers, and after 12 months 79% of low-skilled blue-collar workers compared to 87% to 92% within other occupational classes. High-skilled white-collar workers (THA: hazard ratio (HR) 2.12 (95% confidence interval (CI) 1.32 to 3.40); TKA: HR 2.31 (95% CI 1.34 to 4.00)) and low-skilled white-collar workers (TKA: HR 1.82 (95% CI 1.04 to 3.18)) had a higher hazard to RTW within six months postoperatively. Conclusion. Clear differences existed in time to RTW among both THA and TKA patients in each of the groups studied. These findings may help guide tailored patient-specific information during preoperative consultation and advice postoperatively, as well as to create awareness among workers and their employers. Cite this article: Bone Joint J 2023;105-B(9):977–984

Aims. To examine whether natural language processing (NLP) using a clinically based large language model (LLM) could be used to predict patient selection for total hip or total knee arthroplasty (THA/TKA) from routinely available free-text radiology reports. Methods. Data pre-processing and analyses were conducted according to the Artificial intelligence to Revolutionize the patient Care pathway in Hip and knEe aRthroplastY (ARCHERY) project protocol. This included use of de-identified Scottish regional clinical data of patients referred for consideration of THA/TKA, held in a secure data environment designed for artificial intelligence (AI) inference. Only preoperative radiology reports were included. NLP algorithms were based on the freely available GatorTron model, a LLM trained on over 82 billion words of de-identified clinical text. Two inference tasks were performed: assessment after model-fine tuning (50 Epochs and three cycles of k-fold cross validation), and external validation. Results. For THA, there were 5,558 patient radiology reports included, of which 4,137 were used for model training and testing, and 1,421 for external validation. Following training, model performance demonstrated average (mean across three folds) accuracy, F1 score, and area under the receiver operating curve (AUROC) values of 0.850 (95% confidence interval (CI) 0.833 to 0.867), 0.813 (95% CI 0.785 to 0.841), and 0.847 (95% CI 0.822 to 0.872), respectively. For TKA, 7,457 patient radiology reports were included, with 3,478 used for model training and testing, and 3,152 for external validation. Performance metrics included accuracy, F1 score, and AUROC values of 0.757 (95% CI 0.702 to 0.811), 0.543 (95% CI 0.479 to 0.607), and 0.717 (95% CI 0.657 to 0.778) respectively. There was a notable deterioration in performance on external validation in both cohorts. Conclusion. The use of routinely available preoperative radiology reports provides promising potential to help screen suitable candidates for THA, but not for TKA. The external validation results demonstrate the importance of further model testing and training when confronted with new clinical cohorts. Cite this article: Bone Joint J 2024;106-B(7):688–695

Aims. Debridement, antibiotics, and implant retention (DAIR) is a widely accepted form of surgical treatment for patients with an early periprosthetic joint infection (PJI) after primary arthroplasty. The outcome of DAIR after revision arthroplasty, however, has not been reported. The aim of this study was to report the success rate of DAIR after revision arthroplasty with a follow-up of two years. Methods. This retrospective study, conducted at the Sint Maartenskliniek, Nijmegen, the Netherlands, included 88 patients who underwent DAIR within 90 days of revision total hip or total knee arthroplasty between 2012 and 2019. Details of the surgical procedures and PJI were collected. Univariate analysis and a subgroup analysis of the culture-positive group were performed. Kaplan-Meier survivorship curves were constructed. Results. The overall success rate of DAIR, with respect to the retention of components and the cure of infection, was 68% after two years. DAIR performed with an interval of > 30 days after the index revision procedure (odds ratio (OR) 0.24 (95% confidence interval (CI) 0.08 to 0.72); p = 0.008), a repeated DAIR within 90 days (OR 0.37 (95% CI 0.14 to 0.97); p = 0.040), and the use of an immunosuppressive agent (OR 0.13 (95% CI 0.02 to 0.67); p = 0.012) were associated with a significantly reduced success rate. In the culture-positive group, a mismatch between the antibiotic treatment and the susceptibility of the organism was associated with a significantly lower success rate (OR 0.13 (95% CI 0.03 to 0.62); p = 0.007). Conclusion. DAIR is an acceptable form of surgical treatment for patients with a suspected early PJI after revision arthroplasty of the hip or knee. DAIRs performed after a prolonged interval, multiple DAIRs, and antibiotic mismatches were significantly associated with an increased risk of failure. Optimization of the host immune response and the prevention of antibiotic mismatch are modifiable factors that may improve the outcome. The high rate of mismatches was an important finding, underlining the need for a review of the local microbiological data, which might improve the outcome. Cite this article: Bone Joint J 2022;104-B(4):464–471

Aims. The aim of this study was to determine the prevalence of depressive and anxiety disorders prior to total hip (THA) and total knee arthroplasty (TKA) and to assess their impact on the rates of any infection, revision, or reoperation. Methods. Between January 2000 and March 2019, 21,469 primary and revision arthroplasties (10,011 THAs; 11,458 TKAs), which were undertaken in 15,504 patients at a single academic medical centre, were identified from a 27-county linked electronic medical record (EMR) system. Depressive and anxiety disorders were identified by diagnoses in the EMR or by using a natural language processing program with subsequent validation from review of the medical records. Patients with mental health diagnoses other than anxiety or depression were excluded. Results. Depressive and/or anxiety disorders were common before THA and TKA, with a prevalence of 30% in those who underwent primary THA, 33% in those who underwent revision THA, 32% in those who underwent primary TKA, and 35% in those who underwent revision TKA. The presence of depressive or anxiety disorders was associated with a significantly increased risk of any infection (primary THA, hazard ratio (HR) 1.5; revision THA, HR 1.9; primary TKA, HR 1.6; revision TKA, HR 1.8), revision (THA, HR 1.7; TKA, HR 1.6), re-revision (THA, HR 2.0; TKA, HR 1.6), and reoperation (primary THA, HR 1.6; revision THA, HR 2.2; primary TKA, HR 1.4; revision TKA, HR 1.9; p < 0.03 for all). Patients with preoperative depressive and/or anxiety disorders were significantly less likely to report “much better” joint function after primary THA (78% vs 87%) and primary TKA (86% vs 90%) compared with those without these disorders at two years postoperatively (p < 0.001 for all). Conclusion. The presence of depressive or anxiety disorders prior to primary or revision THA and TKA is common, and associated with a significantly higher risk of infection, revision, reoperation, and dissatisfaction. This topic deserves further study, and surgeons may consider mental health optimization to be of similar importance to preoperative variables such as diabetic control, prior to arthroplasty. Cite this article: Bone Joint J 2023;105-B(5):526–533

Total hip and total knee arthroplasty both provide a considerable improvement in quality of life, but there is no evidence to suggest that one is more successful than the other. We studied 72 patients in a prospective trial before and after total hip or total knee replacement. We recorded scores for disability and distress derived from the Harris hip score and the British Orthopaedic Association knee assessment score, and used them to generate quality of life (QoL) scores using the Rosser Index Matrix immediately before and at one year after surgery. The patients awaiting knee replacement had significantly lower QoL scores than those awaiting hip replacement (p = 0.011). The QoL scores at one year were high and almost identical for both groups (p = 0.46). Further analysis showed that gender and weight were not significant predictors of improvement of QoL scores, but age (p = 0.03) and whether the hip or knee was replaced (p = 0.006) were significant factors

Total hip and knee arthroplasty (THA, TKA) are largely successful procedures; however, both have variable outcomes, resulting in some patients being dissatisfied with the outcome. Surgeons are turning to technologies such as robotic-assisted surgery in an attempt to improve outcomes. Robust studies are needed to find out if these innovations are really benefitting patients. The Robotic Arthroplasty Clinical and Cost Effectiveness Randomised Controlled Trials (RACER) trials are multicentre, patient-blinded randomized controlled trials. The patients have primary osteoarthritis of the hip or knee. The operation is Mako-assisted THA or TKA and the control groups have operations using conventional instruments. The primary clinical outcome is the Forgotten Joint Score at 12 months, and there is a built-in analysis of cost-effectiveness. Secondary outcomes include early pain, the alignment of the components, and medium- to long-term outcomes. This annotation outlines the need to assess these technologies and discusses the design and challenges when conducting such trials, including surgical workflows, isolating the effect of the operation, blinding, and assessing the learning curve. Finally, the future of robotic surgery is discussed, including the need to contemporaneously introduce and evaluate such technologies.

Cite this article: Bone Joint J 2024;106-B(2):114–120.

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Total femoral arthroplasty (TFA) is a rare procedure used in cases of significant femoral bone loss, commonly from cancer, infection, and trauma. Low patient numbers have resulted in limited published work on long-term outcomes, and even less regarding TFA undertaken for non-oncological indications. The aim of this study was to evaluate the long-term clinical outcomes of all TFAs in our unit.

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We sought to compare functional outcomes and survival between non-smokers, former smokers, and current smokers who underwent anatomical total shoulder arthroplasty (aTSA) in a large cohort of patients.

Aims. The aim of this study was to evaluate the ability of a machine-learning algorithm to diagnose prosthetic loosening from preoperative radiographs and to investigate the inputs that might improve its performance. Methods. A group of 697 patients underwent a first-time revision of a total hip (THA) or total knee arthroplasty (TKA) at our institution between 2012 and 2018. Preoperative anteroposterior (AP) and lateral radiographs, and historical and comorbidity information were collected from their electronic records. Each patient was defined as having loose or fixed components based on the operation notes. We trained a series of convolutional neural network (CNN) models to predict a diagnosis of loosening at the time of surgery from the preoperative radiographs. We then added historical data about the patients to the best performing model to create a final model and tested it on an independent dataset. Results. The convolutional neural network we built performed well when detecting loosening from radiographs alone. The first model built de novo with only the radiological image as input had an accuracy of 70%. The final model, which was built by fine-tuning a publicly available model named DenseNet, combining the AP and lateral radiographs, and incorporating information from the patient’s history, had an accuracy, sensitivity, and specificity of 88.3%, 70.2%, and 95.6% on the independent test dataset. It performed better for cases of revision THA with an accuracy of 90.1%, than for cases of revision TKA with an accuracy of 85.8%. Conclusion. This study showed that machine learning can detect prosthetic loosening from radiographs. Its accuracy is enhanced when using highly trained public algorithms, and when adding clinical data to the algorithm. While this algorithm may not be sufficient in its present state of development as a standalone metric of loosening, it is currently a useful augment for clinical decision making. Cite this article: Bone Joint J 2020;102-B(6 Supple A):101–106

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The aim of this study was to evaluate the optimal deep tissue specimen sample number for histopathological analysis in the diagnosis of periprosthetic joint infection (PJI).