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The evidence base within trauma and orthopaedics has traditionally favoured quantitative research methodologies. Qualitative research can provide unique insights which illuminate patient experiences and perceptions of care. Qualitative methods reveal the subjective narratives of patients that are not captured by quantitative data, providing a more comprehensive understanding of patient-centred care. The aim of this study is to quantify the level of qualitative research within the orthopaedic literature.

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This study evaluates the association between consultant and hospital volume and the risk of re-revision and 90-day mortality following first-time revision of primary hip arthroplasty for aseptic loosening.

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Patients with proximal femoral fractures (PFFs) are often multimorbid, thus unplanned readmissions following surgery are common. We therefore aimed to analyze 30-day and one-year readmission rates, reasons for, and factors associated with, readmission risk in a cohort of patients with surgically treated PFFs across Austria.

Aims. Frailty greatly increases the risk of adverse outcome of trauma in older people. Frailty detection tools appear to be unsuitable for use in traumatically injured older patients. We therefore aimed to develop a method for detecting frailty in older people sustaining trauma using routinely collected clinical data. Methods. We analyzed prospectively collected registry data from 2,108 patients aged ≥ 65 years who were admitted to a single major trauma centre over five years (1 October 2015 to 31 July 2020). We divided the sample equally into two, creating derivation and validation samples. In the derivation sample, we performed univariate analyses followed by multivariate regression, starting with 27 clinical variables in the registry to predict Clinical Frailty Scale (CFS; range 1 to 9) scores. Bland-Altman analyses were performed in the validation cohort to evaluate any biases between the Nottingham Trauma Frailty Index (NTFI) and the CFS. Results. In the derivation cohort, five of the 27 variables were strongly predictive of the CFS (regression coefficient B = 6.383 (95% confidence interval 5.03 to 7.74), p < 0.001): age, Abbreviated Mental Test score, admission haemoglobin concentration (g/l), pre-admission mobility (needs assistance or not), and mechanism of injury (falls from standing height). In the validation cohort, there was strong agreement between the NTFI and the CFS (mean difference 0.02) with no apparent systematic bias. Conclusion. We have developed a clinically applicable tool using easily and routinely measured physiological and functional parameters, which clinicians and researchers can use to guide patient care and to stratify the analysis of quality improvement and research projects. Cite this article: Bone Joint J 2024;106-B(4):412–418

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Total hip arthroplasty (THA) is a common procedure to address pain and enhance function in hip disorders such as osteoarthritis. Despite its success, postoperative patient recovery exhibits considerable heterogeneity. This study aimed to investigate whether patients follow distinct pain trajectories following THA and identify the patient characteristics linked to suboptimal trajectories.

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Occult (clinical) injuries represent 15% of all scaphoid fractures, posing significant challenges to the clinician. MRI has been suggested as the gold standard for diagnosis, but remains expensive, time-consuming, and is in high demand. Conventional management with immobilization and serial radiography typically results in multiple follow-up attendances to clinic, radiation exposure, and delays return to work. Suboptimal management can result in significant disability and, frequently, litigation.

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Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool.

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In-hospital length of stay (LOS) and discharge dispositions following arthroplasty could act as surrogate measures for improvement in patient pathways, and have major cost saving implications for healthcare providers. With the ever-growing adoption of robotic technology in arthroplasty, it is imperative to evaluate its impact on LOS. The objectives of this study were to compare LOS and discharge dispositions following robotic arm-assisted total knee arthroplasty (RO TKA) and unicompartmental arthroplasty (RO UKA) versus conventional technique (CO TKA and UKA).

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This study describes the variation in the annual volumes of revision hip arthroplasty (RHA) undertaken by consultant surgeons nationally, and the rate of accrual of RHA and corresponding primary hip arthroplasty (PHA) volume for new consultants entering practice.

Cite this article: Bone Joint Res 2023;12(9):598–600.

Aims. This study aimed to investigate the risk of postoperative complications in COVID-19-positive patients undergoing common orthopaedic procedures. Methods. Using the National Surgical Quality Improvement Programme (NSQIP) database, patients who underwent common orthopaedic surgery procedures from 1 January to 31 December 2021 were extracted. Patient preoperative COVID-19 status, demographics, comorbidities, type of surgery, and postoperative complications were analyzed. Propensity score matching was conducted between COVID-19-positive and -negative patients. Multivariable regression was then performed to identify both patient and provider risk factors independently associated with the occurrence of 30-day postoperative adverse events. Results. Of 194,121 included patients, 740 (0.38%) were identified to be COVID-19-positive. Comparison of comorbidities demonstrated that COVID-19-positive patients had higher rates of diabetes, heart failure, and pulmonary disease. After propensity matching and controlling for all preoperative variables, multivariable analysis found that COVID-19-positive patients were at increased risk of several postoperative complications, including: any adverse event, major adverse event, minor adverse event, death, venous thromboembolism, and pneumonia. COVID-19-positive patients undergoing hip/knee arthroplasty and trauma surgery were at increased risk of 30-day adverse events. Conclusion. COVID-19-positive patients undergoing orthopaedic surgery had increased odds of many 30-day postoperative complications, with hip/knee arthroplasty and trauma surgery being the most high-risk procedures. These data reinforce prior literature demonstrating increased risk of venous thromboembolic events in the acute postoperative period. Clinicians caring for patients undergoing orthopaedic procedures should be mindful of these increased risks, and attempt to improve patient care during the ongoing global pandemic. Cite this article: Bone Jt Open 2023;4(9):704–712

Aims. National hip fracture registries audit similar aspects of care but there is variation in the actual data collected; these differences restrict international comparison, benchmarking, and research. The Fragility Fracture Network (FFN) published a revised minimum common dataset (MCD) in 2022 to improve consistency and interoperability. Our aim was to assess compatibility of existing registries with the MCD. Methods. We compared 17 hip fracture registries covering 20 countries (Argentina; Australia and New Zealand; China; Denmark; England, Wales, and Northern Ireland; Germany; Holland; Ireland; Japan; Mexico; Norway; Pakistan; the Philippines; Scotland; South Korea; Spain; and Sweden), setting each of these against the 20 core and 12 optional fields of the MCD. Results. The highest MCD adherence was demonstrated by the most recently established registries. The first-generation registries in Scandinavia collect data for 60% of MCD fields, second-generation registries (UK, other European, and Australia and New Zealand) collect for 75%, and third-generation registries collect data for 85% of MCD fields. Five of the 20 core fields were collected by all 17 registries (age; sex; surgery date/time of operation; surgery type; and death during acute admission). Two fields were collected by most (16/17; 94%) registries (date/time of presentation and American Society of Anesthesiologists grade), and five more by the majority (15/17; 88%) registries (type, side, and pathological nature of fracture; anaesthetic modality; and discharge destination). Three core fields were each collected by only 11/17 (65%) registries: prefracture mobility/activities of daily living; cognition on admission; and bone protection medication prescription. Conclusion. There is moderate but improving compatibility between existing registries and the FFN MCD, and its introduction in 2022 was associated with an improved level of adherence among the most recently established programmes. Greater interoperability could be facilitated by improving consistency of data collection relating to prefracture function, cognition, bone protection, and follow-up duration, and this could improve international collaborative benchmarking, research, and quality improvement. Cite this article: Bone Joint J 2023;105-B(9):1013–1019

The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as ‘big data’, AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI’s limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction.

Cite this article: Bone Joint Res 2023;12(7):447–454.

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This is a multicentre, prospective assessment of a proportion of the overall orthopaedic trauma caseload of the UK. It investigates theatre capacity, cancellations, and time to surgery in a group of hospitals that is representative of the wider population. It identifies barriers to effective practice and will inform system improvements.

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Delirium is associated with adverse outcomes following hip fracture, but the prevalence and significance of delirium for the prognosis and ongoing rehabilitation needs of patients admitted from home is less well studied. Here, we analyzed relationships between delirium in patients admitted from home with 1) mortality; 2) total length of hospital stay; 3) need for post-acute inpatient rehabilitation; and 4) hospital readmission within 180 days.

Aims. To identify variables independently associated with same-day discharge (SDD) of patients following revision total knee arthroplasty (rTKA) and to develop machine learning algorithms to predict suitable candidates for outpatient rTKA. Methods. Data were obtained from the American College of Surgeons National Quality Improvement Programme (ACS-NSQIP) database from the years 2018 to 2020. Patients with elective, unilateral rTKA procedures and a total hospital length of stay between zero and four days were included. Demographic, preoperative, and intraoperative variables were analyzed. A multivariable logistic regression (MLR) model and various machine learning techniques were compared using area under the curve (AUC), calibration, and decision curve analysis. Important and significant variables were identified from the models. Results. Of the 5,600 patients included in this study, 342 (6.1%) underwent SDD. The random forest (RF) model performed the best overall, with an internally validated AUC of 0.810. The ten crucial factors favoring SDD in the RF model include operating time, anaesthesia type, age, BMI, American Society of Anesthesiologists grade, race, history of diabetes, rTKA type, sex, and smoking status. Eight of these variables were also found to be significant in the MLR model. Conclusion. The RF model displayed excellent accuracy and identified clinically important variables for determining candidates for SDD following rTKA. Machine learning techniques such as RF will allow clinicians to accurately risk-stratify their patients preoperatively, in order to optimize resources and improve patient outcomes. Cite this article: Bone Jt Open 2023;4(6):399–407

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The aim of this study was to describe services available to patients with periprosthetic femoral fracture (PPFF) in England and Wales, with focus on variation between centres and areas for care improvement.

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Early detection of developmental dysplasia of the hip (DDH) is associated with improved outcomes of conservative treatment. Therefore, we aimed to evaluate a novel screening programme that included both the primary risk factors of breech presentation and family history, and the secondary risk factors of oligohydramnios and foot deformities.

Cite this article: Bone Joint Res 2023;12(4):256–258.

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The aim of this study is to report the implant survival and factors associated with revision of total elbow arthroplasty (TEA) using data from the Dutch national registry.