Aims. The risk factors for recurrent instability (RI) following a primary traumatic anterior shoulder dislocation (PTASD) remain unclear. In this study, we aimed to determine the rate of RI in a large cohort of patients managed nonoperatively after PTASD and to develop a clinical prediction model. Methods. A total of 1,293 patients with PTASD managed nonoperatively were identified from a trauma database (mean age 23.3 years (15 to 35); 14.3% female). We assessed the prevalence of RI, and used multivariate regression modelling to evaluate which demographic- and injury-related factors were independently predictive for its occurrence. Results. The overall rate of RI at a mean follow-up of 34.4 months (SD 47.0) was 62.8% (n = 812), with 81.0% (n = 658) experiencing their first recurrence within two years of PTASD. The median time for recurrence was 9.8 months (IQR 3.9 to 19.4). Independent predictors increasing risk of RI included male sex (p < 0.001), younger age at PTASD (p < 0.001), participation in contact sport (p < 0.001), and the presence of a bony Bankart (BB) lesion (p = 0.028). Greater tuberosity fracture (GTF) was protective (p < 0.001). However, the discriminative ability of the resulting predictive model for two-year risk of RI was poor (area under the curve (AUC) 0.672). A subset analysis excluding identifiable radiological predictors of BB and GTF worsened the predictive ability (AUC 0.646). Conclusion. This study clarifies the prevalence and risk factors for RI following PTASD in a large, unselected patient cohort. Although these data permitted the development of a predictive tool for RI, its discriminative ability was poor. Predicting RI remains challenging, and as-yet-undetermined risk factors may be important in determining the risk. Cite this article: Bone Joint J 2024;106-B(10):1111–1117

Despite the vast quantities of published artificial intelligence (AI) algorithms that target trauma and orthopaedic applications, very few progress to inform clinical practice. One key reason for this is the lack of a clear pathway from development to deployment. In order to assist with this process, we have developed the Clinical Practice Integration of Artificial Intelligence (CPI-AI) framework – a five-stage approach to the clinical practice adoption of AI in the setting of trauma and orthopaedics, based on the IDEAL principles (https://www.ideal-collaboration.net/). Adherence to the framework would provide a robust evidence-based mechanism for developing trust in AI applications, where the underlying algorithms are unlikely to be fully understood by clinical teams.

Cite this article: Bone Joint Res 2024;13(9):507–512.

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.

Aims

The underlying natural history of suspected scaphoid fractures (SSFs) is unclear and assumed poor. There is an urgent requirement to develop the literature around SSFs to quantify the actual prevalence of intervention following SSF. Defining the risk of intervention following SSF may influence the need for widespread surveillance and screening of SSF injuries, and could influence medicolegal actions around missed scaphoid fractures.

The October 2023 Hip & Pelvis Roundup³⁶⁰ looks at: Femoroacetabular impingement syndrome at ten years – how do athletes do?; Venous thromboembolism in patients following total joint replacement: are transfusions to blame?; What changes in pelvic sagittal tilt occur 20 years after total hip arthroplasty?; Can stratified care in hip arthroscopy predict successful and unsuccessful outcomes?; Hip replacement into your nineties; Can large language models help with follow-up?; The most taxing of revisions – proximal femoral replacement for periprosthetic joint infection – what’s the benefit of dual mobility?

Aims

To map literature on prognostic factors related to outcomes of revision total knee arthroplasty (rTKA), to identify extensively studied factors and to guide future research into what domains need further exploration.

Aims

To develop prediction models using machine-learning (ML) algorithms for 90-day and one-year mortality prediction in femoral neck fracture (FNF) patients aged 50 years or older based on the Hip fracture Evaluation with Alternatives of Total Hip arthroplasty versus Hemiarthroplasty (HEALTH) and Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trials.

Acute bone and joint infections in children are serious, and misdiagnosis can threaten limb and life. Most young children who present acutely with pain, limping, and/or loss of function have transient synovitis, which will resolve spontaneously within a few days. A minority will have a bone or joint infection. Clinicians are faced with a diagnostic challenge: children with transient synovitis can safely be sent home, but children with bone and joint infection require urgent treatment to avoid complications. Clinicians often respond to this challenge by using a series of rudimentary decision support tools, based on clinical, haematological, and biochemical parameters, to differentiate childhood osteoarticular infection from other diagnoses. However, these tools were developed without methodological expertise in diagnostic accuracy and do not consider the importance of imaging (ultrasound scan and MRI). There is wide variation in clinical practice with regard to the indications, choice, sequence, and timing of imaging. This variation is most likely due to the lack of evidence concerning the role of imaging in acute bone and joint infection in children. We describe the first steps of a large UK multicentre study, funded by the National Institute for Health Research, which seeks to integrate definitively the role of imaging into a decision support tool, developed with the assistance of individuals with expertise in the development of clinical prediction tools. Cite this article: Bone Joint J 2023;105-B(3):227–229

Aim. Recurrence of bone and joint infection, despite appropriate therapy, is well recognised and stimulates ongoing interest in identifying host factors that predict infection recurrence. Clinical prediction models exist for those treated with DAIR, but to date no models with a low risk of bias predict orthopaedic infection recurrence for people with surgically excised infection and removed metalwork. The aims of this study were to construct and internally validate a risk prediction model for infection recurrence at 12 months, and to identify factors that predict recurrence. Predictive factors must be easy to check in pre-operative assessment and relevant across patient groups. Methods. Four prospectively collected datasets including 1173 participants treated in European centres between 2003 and 2021, followed up to 12 months after surgery for orthopaedic infections, were included in logistic regression modelling [1–3]. The definition of infection recurrence was identical and ascertained separately from baseline factors in three contributing cohorts. Eight predictive factors were investigated following a priori sample size calculation: age, gender, BMI, ASA score, the number of prior operations, immunosuppressive medication, glycosylated haemoglobin (HbA1c), and smoking. Missing data, including systematically missing predictors, were imputed using Multiple Imputation by Chained Equations. Weekly alcohol intake was not included in modelling due to low inter-observer reliability (mean reported intake 12 units per week, 95% CI for mean inter-rater error −16.0 to +15.4 units per week). Results. Participants were 64% male, with a median age of 60 years (range 18–95). 86% of participants had lower limb orthopaedic infections. 732 participants were treated for osteomyelitis, including FRI, and 432 for PJI. 16% of participants experienced treatment failure by 12 months. The full prediction model had moderate apparent discrimination: AUROC (C statistic) 0.67, Brier score 0.13, and reasonable apparent calibration. Of the predictors of interest, associations with failure were seen with prior operations at the same anatomical site (odds ratio for failure 1.51 for each additional prior surgery; 95% CI 1.02 to 2.22, p=0.06), and the current use of immunosuppressive medications (odds ratio for failure 2.94; 95% CI 0.89 to 9.77, p=0.08). Conclusions. This association between number of prior surgeries and treatment failure supports the urgent need to streamline referral pathways for people with orthopaedic infection to specialist multidisciplinary units

Aims

The aim of this study was to describe the introduction of a virtual pathway for the management of patients with a suspected fracture of the scaphoid, and to report patient-reported outcome measures (PROMs) and satisfaction following treatment using this service.

Aims. To develop and internally validate a preoperative clinical prediction model for acute adjacent vertebral fracture (AVF) after vertebral augmentation to support preoperative decision-making, named the after vertebral augmentation (AVA) score. Methods. In this prognostic study, a multicentre, retrospective single-level vertebral augmentation cohort of 377 patients from six Japanese hospitals was used to derive an AVF prediction model. Backward stepwise selection (p < 0.05) was used to select preoperative clinical and imaging predictors for acute AVF after vertebral augmentation for up to one month, from 14 predictors. We assigned a score to each selected variable based on the regression coefficient and developed the AVA scoring system. We evaluated sensitivity and specificity for each cut-off, area under the curve (AUC), and calibration as diagnostic performance. Internal validation was conducted using bootstrapping to correct the optimism. Results. Of the 377 patients used for model derivation, 58 (15%) had an acute AVF postoperatively. The following preoperative measures on multivariable analysis were summarized in the five-point AVA score: intravertebral instability (≥ 5 mm), focal kyphosis (≥ 10°), duration of symptoms (≥ 30 days), intravertebral cleft, and previous history of vertebral fracture. Internal validation showed a mean optimism of 0.019 with a corrected AUC of 0.77. A cut-off of ≤ one point was chosen to classify a low risk of AVF, for which only four of 137 patients (3%) had AVF with 92.5% sensitivity and 45.6% specificity. A cut-off of ≥ four points was chosen to classify a high risk of AVF, for which 22 of 38 (58%) had AVF with 41.5% sensitivity and 94.5% specificity. Conclusion. In this study, the AVA score was found to be a simple preoperative method for the identification of patients at low and high risk of postoperative acute AVF. This model could be applied to individual patients and could aid in the decision-making before vertebral augmentation. Cite this article: Bone Joint J 2022;104-B(1):97–102

There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for various aspects of Trauma & Orthopaedic surgery. However, correct interpretation of these models is difficult for those without specific knowledge of computing or health data science methodology. Lack of current reporting standards leads to the potential for significant heterogeneity in the design and quality of published studies. We provide an overview of machine-learning techniques for the lay individual, including key terminology and best practice reporting guidelines.

Cite this article: Bone Joint J 2021;103-B(12):1754–1758.

Over 300,000 total hip arthroplasties (THA) are performed annually in the USA. Surgical Site Infections (SSI) are one of the most common complications and are associated with increased morbidity, mortality and cost. Risk factors for SSI include obesity, diabetes and smoking, but few studies have reported on the predictive value of pre-operative blood markers for SSI. The purpose of this study was to create a clinical prediction model for acute SSI (classified as either superficial, deep and overall) within 30 days of THA based on commonly ordered pre-operative lab markers and using data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. All adult patients undergoing an elective unilateral THA for osteoarthritis from 2011–2016 were identified from the NSQIP database using Current Procedural Terminology (CPT) codes. Patients with active or chronic, local or systemic infection/sepsis or disseminated cancer were excluded. Multivariate logistic regression was used to determine coefficients, with manual stepwise reduction. Receiver Operating Characteristic (ROC) curves were also graphed. The SSI prediction model included the following covariates: body mass index (BMI) and sex, comorbidities such as congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), smoking, current/previous steroid use, as well as pre-operative blood markers, albumin, alkaline phosphate, blood urea nitrogen (BUN), creatinine, hematocrit, international normalized ratio (INR), platelets, prothrombin time (PT), sodium and white blood cell (WBC) levels. Since the data met logistic assumption requirements, bootstrap estimation was used to measure internal validity. The area under the ROC curve for final derivations along with McFadden's R-squared were utilized to compare prediction models. A total of 130,619 patients were included with the median age of patients at time of THA was 67 years (mean=66.6+11.6 years) with 44.8% (n=58,757) being male. A total of 1,561 (1.20%) patients had a superficial or deep SSI (overall SSI). Of all SSI, 45.1% (n=704) had a deep SSI and 55.4% (n=865) had a superficial SSI. The incidence of SSI occurring annually decreased from 1.44% in 2011 to 1.16% in 2016. Area under the ROC curve for the SSI prediction model was 0.79 and 0.78 for deep and superficial SSI, respectively and 0.71 for overall SSI. CHF had the largest effect size (Odds Ratio(OR)=2.88, 95% Confidence Interval (95%CI): 1.56 – 5.32) for overall SSI risk. Albumin (OR=0.44, 95% CI: 0.37 – 0.52, OR=0.31, 95% CI: 0.25 – 0.39, OR=0.48, 95% CI: 0.41 – 0.58) and sodium (OR=0.95, 95% CI: 0.93 – 0.97, OR=0.94, 95% CI: 0.91 – 0.97, OR=0.95, 95% CI: 0.93 – 0.98) levels were consistently significant in all clinical prediction models for superficial, deep and overall SSI, respectively. In terms of pre-operative blood markers, hypoalbuminemia and hyponatremia are both significant risk factors for superficial, deep and overall SSI. In this large NSQIP database study, we were able to create an SSI prediction model and identify risk factors for predicting acute superficial, deep and overall SSI after THA. To our knowledge, this is the first clinical model whereby pre-operative hyponatremia (in addition to hypoalbuminemia) levels have been predictive of SSI after THA. Although the model remains without external validation, it is a vital starting point for developing a risk prediction model for SSI and can help physicians mitigate risk factors for acute SSI post THA

Aims

The aim of this study was to evaluate the diagnostic value of preoperative serum CRP, white blood cell count (WBC), percentage of neutrophils (%N), and neutrophil to lymphocyte ratio (NLR) when using the fracture-related infection (FRI) consensus definition.

Fractures through the physis account for 18–30% of all paediatric fractures, leading to growth arrest in 5.5% of cases. We have limited knowledge to predict which physeal fractures result in growth arrest and subsequent deformity or limb length discrepancy. The purpose of this study is to identify factors associated with physeal growth arrest to improve patient outcomes. This prospective cohort study was designed to develop a clinical prediction model for growth arrest after physeal injury. Patients < 1 8 years old presenting within four weeks of injury were enrolled if they had open physes and sustained a physeal fracture of the humerus, radius, ulna, femur, tibia or fibula. Patients with prior history of same-site fracture or a condition known to alter bone growth or healing were excluded. Demographic data, potential prognostic indicators and radiographic data were collected at baseline, one and two years post-injury. A total of 167 patients had at least one year of follow-up. Average age at injury was 10.4 years, 95% CI [9.8,10.94]. Reduction was required in 51% of cases. Right-sided (52.5%) and distal (90.1%) fractures were most common. After initial reduction 52.5% of fractures had some form of residual angulation and/or displacement (38.5% had both). At one year follow-up, 34 patients (21.1%) had evidence of a bony bridge on plain radiograph, 10 (6.2%) had residual angulation (average 12.6°) and three had residual displacement. Initial angulation (average 22.4°) and displacement (average 5.8mm) were seen in 16/34 patients with bony bridge (48.5%), with 10 (30.3%) both angulated and displaced. Salter-Harris type II fractures were most common across all patients (70.4%) and in those with bony bridges (57.6%). At one year, 44 (27.3%) patients had evidence of closing/closed physes. At one year follow-up, there was evidence of a bony bridge across the physis in 21.1% of patients on plain film, and residual angulation and/or displacement in 8.1%. Initial angulation and/or displacement was present in 64.7% of patients showing possible evidence of growth arrest. The incidence of growth arrest in this patient population appears higher than past literature reports. However, plain film is an unreliable modality for assessing physeal bars and the true incidence may be lower. A number of patients were approaching skeletal maturity at time of injury and any growth arrest is likely to have less clinical significance in these cases. Further prospective long-term follow-up is required to determine the true incidence and impact of growth arrest

Aims

To investigate whether pre-operative functional mobility is a determinant of delayed inpatient recovery of activities (IRoA) after total knee arthroplasty (TKA) in three periods that coincided with changes in the clinical pathway.

Objectives

High failure rates of metal-on-metal hip arthroplasty implants have highlighted the need for more careful introduction and monitoring of new implants and for the evaluation of the safety of medical devices. The National Joint Registry and other regulatory services are unable to detect failing implants at an early enough stage. We aimed to identify validated surrogate markers of long-term outcome in patients undergoing primary total hip arthroplasty (THA).

Objectives. The goal of this study was to describe and evaluate the implementation of a tailored care pathway as an alternative to a standard joint care protocol in the postoperative in-hospital rehabilitation after total knee replacement (TKR) on clinically relevant outcome parameters. Methods. We monitored an orthopaedic department regarding postoperative rehabilitation after TKR on several outcome parameters throughout a period of 32 months, whilst introducing a new care pathway after 17 months. Outcome parameters were monitored and comprised: Time to get functionally recovered (in days), length of stay (in days) and destination of discharge. Key-differences between the joint care protocol and the new tailored pathway were: 1. determination of individual short term rehabilitation goals on the basis of a preoperative clinical prediction rule and postoperative monitoring of functional recovery, 2. Enhancement of expertise of and collaboration between health care professionals and 3. implementation of fast track rehabilitation. We compared the patients operated after implementation of the tailored care pathway with those who were treated according to the joint care protocol. Regression analysis was used to estimate differences between the two groups of patients while correcting for baseline differences in risk profile between the groups. Results. Introduction of the tailored care pathway decreased the length of stay on average from 5.2 days to 4.2 days, (p< 0.01). In addition, there was a 7% non-statistically significant reduction in the number of patients who required inpatient rehabilitation after hospital discharge. Conclusion. Introduction of the tailored care pathway reduced the mean length of stay by one day, whilst patient safety and satisfaction remained unaltered

Background. Establishing the diagnosis in a child presenting with an atraumatic limp can be difficult. Clinical prediction algorithms have been devised to distinguish septic arthritis (SA) from transient synovitis (TS). Within Europe measurement of the Erythrocyte Sedimentation Rate (ESR) has largely been replaced with assessment of C-Reactive Protein (CRP) as an acute phase protein. We produce a prediction algorithm to determine the significance of CRP in distinguishing between TS and SA. Method. All children with a presentation of ‘atraumatic limp’ and a proven effusion on hip ultrasound between 2004 and 2009 were included. Patient demographics, details of the clinical presentation and laboratory investigations were documented to identify a response to each of the four variables (Weight bearing status, WCC >12,000 cells/m3, CRP >20mg/L and Temperature >38.5°C). SA was defined based upon culture and microscopy of the operative findings. Results. 311 hips were included within the study. Of these 282 were considered to have transient synovitis. 29 patients met criteria to be classified as SA based upon laboratory assessment of the synovial fluid. The introduction of CRP eliminated the need for a four variable model as the use of two variables (CRP and weight bearing status) had similar efficacy. Treating individuals who were non-weight-bearing and a CRP >20mg/L as SA correctly classified 94.8% individuals, with a sensitivity of 75.9%, specificity of 96.8%, positive predictive value of 71.0%, and negative predictive value of 97.5%. CRP was a significant independent predictor of septic arthritis. Conclusions. CRP was a strong independent risk factor of septic arthritis, and its inclusion within a regression model simplifies the diagnostic algorithm. Nevertheless, this and other models are generally more reliable in excluding SA, than confirming SA, and therefore a clinician's acumen remains important in identifying SA in those individuals with a single abnormal variable