Aims

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.

Aims

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. 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

Aims

The aim of this study was to determine if a three-month course of microorganism-directed oral antibiotics reduces the rate of failure due to further infection following two-stage revision for chronic prosthetic joint infection (PJI) of the hip and knee.

Patients with osteoarthritis undergoing knee replacement have been reported to have an overall reduced mortality compared with that of the general population. This has been attributed to the selection of healthier patients for surgery. However, previous studies have had a maximum follow-up time of ten years. We have used information from the Swedish Knee Arthroplasty Register to study the mortality of a large national series of patients with total knee replacement for up to 28 years after surgery and compared their mortality with that of the normal population. In addition, for a subgroup of patients operated on between 1980 and 2002 we analysed their registered causes of death to determine if they differed from those expected.

We found a reduced overall mortality during the first 12 post-operative years after which it increased and became significantly higher than that of the general population. Age-specific analysis indicated an inverse correlation between age and mortality, where the younger the patients were, the higher their mortality. The shift at 12 years was caused by a relative over-representation of younger patients with a longer follow-up. Analysis of specific causes of death showed a higher mortality for cardiovascular, gastrointestinal and urogenital diseases. The observation that early onset of osteoarthritis of the knee which has been treated by total knee replacement is linked to an increased mortality should be a reason for increased general awareness of health problems in these patients.

A postal questionnaire was sent to 10 000 patients more than one year after their total knee replacement (TKR). They were assessed using the Oxford knee score and were asked whether they were satisfied, unsure or unsatisfied with their TKR. The response rate was 87.4% (8231 of 9417 eligible questionnaires) and a total of 81.8% (6625 of 8095) of patients were satisfied. Multivariable regression modelling showed that patients with higher scores relating to the pain and function elements of the Oxford knee score had a lower level of satisfaction (p < 0.001), and that ongoing pain was a stronger predictor of this. Female gender and a primary diagnosis of osteoarthritis were found to be predictors of lower levels of patient satisfaction. Differences in the rate of satisfaction were also observed in relation to age, the American Society of Anesthesiologists grade and the type of prosthesis.

This study has provided data on the Oxford knee score and the expected levels of satisfaction at one year after TKR. The results should act as a benchmark of practice in the United Kingdom and provide a baseline for peer comparison between institutions.