Aims. To develop and validate patient-centred algorithms that estimate individual risk of death over the first year after elective joint arthroplasty surgery for osteoarthritis. Methods. A total of 763,213 hip and knee joint arthroplasty episodes recorded in the National Joint Registry for England and Wales (NJR) and 105,407 episodes from the Norwegian Arthroplasty Register were used to model individual mortality risk over the first year after surgery using flexible parametric survival regression. Results. The one-year mortality rates in the NJR were 10.8 and 8.9 per 1,000 patient-years after hip and knee arthroplasty, respectively. The Norwegian mortality rates were 9.1 and 6.0 per 1,000 patient-years, respectively. The strongest predictors of death in the final models were age, sex, body mass index, and American Society of Anesthesiologists grade. Exposure variables related to the intervention, with the exception of knee arthroplasty type, did not add discrimination over patient factors alone. Discrimination was good in both cohorts, with c-indices above 0.76 for the hip and above 0.70 for the knee. Time-dependent Brier scores indicated appropriate estimation of the mortality rate (≤ 0.01, all models). Conclusion. Simple demographic and clinical information may be used to calculate an individualized estimation for one-year mortality risk after hip or knee arthroplasty (. https://jointcalc.shef.ac.uk. ). These models may be used to provide patients with an estimate of the risk of mortality after joint arthroplasty. Cite this article: Bone Joint Res 2020;9(11):808–820

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. Methods. This study included 2,388 patients from the HEALTH and FAITH trials, with 90-day and one-year mortality proportions of 3.0% (71/2,388) and 6.4% (153/2,388), respectively. The mean age was 75.9 years (SD 10.8) and 65.9% of patients (1,574/2,388) were female. The algorithms included patient and injury characteristics. Six algorithms were developed, internally validated and evaluated across discrimination (c-statistic; discriminative ability between those with risk of mortality and those without), calibration (observed outcome compared to the predicted probability), and the Brier score (composite of discrimination and calibration). Results. The developed algorithms distinguished between patients at high and low risk for 90-day and one-year mortality. The penalized logistic regression algorithm had the best performance metrics for both 90-day (c-statistic 0.80, calibration slope 0.95, calibration intercept -0.06, and Brier score 0.039) and one-year (c-statistic 0.76, calibration slope 0.86, calibration intercept -0.20, and Brier score 0.074) mortality prediction in the hold-out set. Conclusion. Using high-quality data, the ML-based prediction models accurately predicted 90-day and one-year mortality in patients aged 50 years or older with a FNF. The final models must be externally validated to assess generalizability to other populations, and prospectively evaluated in the process of shared decision-making. Cite this article: Bone Jt Open 2023;4(3):168–181

Aims. Though most humeral shaft fractures heal nonoperatively, up to one-third may lead to nonunion with inferior outcomes. The Radiographic Union Score for HUmeral Fractures (RUSHU) was created to identify high-risk patients for nonunion. Our study evaluated the RUSHU’s prognostic performance at six and 12 weeks in discriminating nonunion within a significantly larger cohort than before. Methods. Our study included 226 nonoperatively treated humeral shaft fractures. We evaluated the interobserver reliability and intraobserver reproducibility of RUSHU scoring using intraclass correlation coefficients (ICCs). Additionally, we determined the optimal cut-off thresholds for predicting nonunion using the receiver operating characteristic (ROC) method. Results. The RUSHU demonstrated good interobserver reliability with an ICC of 0.78 (95% CI 0.72 to 0.83) at six weeks and 0.77 (95% CI 0.71 to 0.82) at 12 weeks. Intraobserver reproducibility was good or excellent for all analyses. Area under the curve in the ROC analysis was 0.83 (95% CI 0.77 to 0.88) at six weeks and 0.89 (95% CI 0.84 to 0.93) at 12 weeks, indicating excellent discrimination. The optimal cut-off values for predicting nonunion were ≤ eight points at six weeks and ≤ nine points at 12 weeks, providing the best specificity-sensitivity trade-off. Conclusion. The RUSHU proves to be a reliable and reproducible radiological scoring system that aids in identifying patients at risk of nonunion at both six and 12 weeks post-injury during non-surgical treatment of humeral shaft fractures. The statistically optimal cut-off values for predicting nonunion are ≤ eight at six weeks and ≤ nine points at 12 weeks post-injury

Aims. The aim of this study was to develop and internally validate a prognostic nomogram to predict the probability of gaining a functional range of motion (ROM ≥ 120°) after open arthrolysis of the elbow in patients with post-traumatic stiffness of the elbow. Methods. We developed the Shanghai Prediction Model for Elbow Stiffness Surgical Outcome (SPESSO) based on a dataset of 551 patients who underwent open arthrolysis of the elbow in four institutions. Demographic and clinical characteristics were collected from medical records. The least absolute shrinkage and selection operator regression model was used to optimize the selection of relevant features. Multivariable logistic regression analysis was used to build the SPESSO. Its prediction performance was evaluated using the concordance index (C-index) and a calibration graph. Internal validation was conducted using bootstrapping validation. Results. BMI, the duration of stiffness, the preoperative ROM, the preoperative intensity of pain, and grade of post-traumatic osteoarthritis of the elbow were identified as predictors of outcome and incorporated to construct the nomogram. SPESSO displayed good discrimination with a C-index of 0.73 (95% confidence interval 0.64 to 0.81). A high C-index value of 0.70 could still be reached in the interval validation. The calibration graph showed good agreement between the nomogram prediction and the outcome. Conclusion. The newly developed SPESSO is a valid and convenient model which can be used to predict the outcome of open arthrolysis of the elbow. It could assist clinicians in counselling patients regarding the choice and expectations of treatment. Cite this article: Bone Joint J 2022;104-B(4):486–494

Aims. Treatment of high-grade limb bone sarcoma that invades a joint requires en bloc extra-articular excision. MRI can demonstrate joint invasion but is frequently inconclusive, and its predictive value is unknown. We evaluated the diagnostic accuracy of direct and indirect radiological signs of intra-articular tumour extension and the performance characteristics of MRI findings of intra-articular tumour extension. Methods. We performed a retrospective case-control study of patients who underwent extra-articular excision for sarcoma of the knee, hip, or shoulder from 1 June 2000 to 1 November 2020. Radiologists blinded to the pathology results evaluated preoperative MRI for three direct signs of joint invasion (capsular disruption, cortical breach, cartilage invasion) and indirect signs (e.g. joint effusion, synovial thickening). The discriminatory ability of MRI to detect intra-articular tumour extension was determined by receiver operating characteristic analysis. Results. Overall, 49 patients underwent extra-articular excision. The area under the curve (AUC) ranged from 0.65 to 0.76 for direct signs of joint invasion, and was 0.83 for all three combined. In all, 26 patients had only one to two direct signs of invasion, representing an equivocal result. In these patients, the AUC was 0.63 for joint effusion and 0.85 for synovial thickening. When direct signs and synovial thickening were combined, the AUC was 0.89. Conclusion. MRI provides excellent discrimination for determining intra-articular tumour extension when multiple direct signs of invasion are present. When MRI results are equivocal, assessment of synovial thickening increases MRI’s discriminatory ability to predict intra-articular joint extension. These results should be interpreted in the context of the study’s limitations. The inclusion of only extra-articular excisions enriched the sample for true positive cases. Direct signs likely varied with tumour histology and location. A larger, prospective study of periarticular bone sarcomas with spatial correlation of histological and radiological findings is needed to validate these results before their adoption in clinical practice. Cite this article: Bone Jt Open 2024;5(10):944–952

Objectives. The objective of this study was to develop a test for the rapid (within 25 minutes) intraoperative detection of bacteria from synovial fluid to diagnose periprosthetic joint infection (PJI). Methods. The 16s rDNA test combines a polymerase chain reaction (PCR) for amplification of 16s rDNA with a lateral flow immunoassay in one fully automated system. The synovial fluid of 77 patients undergoing joint aspiration or primary or revision total hip or knee surgery was prospectively collected. The cohort was divided into a proof-of-principle cohort (n = 17) and a validation cohort (n = 60). Using the proof-of-principle cohort, an optimal cut-off for the discrimination between PJI and non-PJI samples was determined. PJI was defined as detection of the same bacterial species in a minimum of two microbiological samples, positive histology, and presence of a sinus tract or intra-articular pus. Results. The 16s rDNA test proved to be very robust and was able to provide a result in 97% of all samples within 25 minutes. The 16s rDNA test was able to diagnose PJI with a sensitivity of 87.5% and 82%, and a specificity of 100% and 89%, in the proof-of-principle and validation cohorts, respectively. The microbiological culture of synovial fluid achieved a sensitivity of 80% and a specificity of 93% in the validation cohort. Conclusion. The 16s rDNA test offers reliable intraoperative detection of all bacterial species within 25 minutes with a sensitivity and specificity comparable with those of conventional microbiological culture of synovial fluid for the detection of PJI. The 16s rDNA test performance is independent of possible blood contamination, culture time and bacterial species. Cite this article: V. Janz, J. Schoon, C. Morgenstern, B. Preininger, S. Reinke, G. Duda, A. Breitbach, C. F. Perka, S. Geissler. Rapid detection of periprosthetic joint infection using a combination of 16s rDNA polymerase chain reaction and lateral flow immunoassay: A Pilot Study. Bone Joint Res 2018;7:12–19. DOI: 10.1302/2046-3758.71.BJR-2017-0103.R2

Objectives. To evaluate the applicability of MRI for the quantitative assessment of anterior talofibular ligaments (ATFLs) in symptomatic chronic ankle instability (CAI). Methods. Between 1997 and 2010, 39 patients with symptomatic CAI underwent surgical treatment (22 male, 17 female, mean age 25.4 years (15 to 40)). In all patients, the maximum diameters of the ATFLs were measured on pre-operative T2-weighted MR images in planes parallel to the path of the ATFL. They were classified into three groups based on a previously published method with modifications: ‘normal’, diameter = 1.0 - 3.2 mm; ‘thickened’, diameter > 3.2 mm; ‘thin or absent’, diameter < 1.0 mm. Stress radiography was performed with the maximum manual force in inversion under general anaesthesia immediately prior to surgery. In surgery, ATFLs were macroscopically divided into two categories: ‘thickened’, an obvious thickened ligament and ‘thin or absent’. The imaging results were compared with the macroscopic results that are considered to be of a gold standard. Results. Agreement was reached when comparison was made between groups, based on MRI and macroscopic findings. ATFLs were abnormal in all 39 cases and classified as ten ‘thickened’ and 29 ‘thin or absent’. As to talar tilt stress radiography, a clear cut-off angle, which would allow discrimination between ‘thickened’ and ‘thin or absent’ patients, was not identified. Conclusion. MRI is valuable as a pre-operative assessment tool that can provide the quantitative information of ATFLs in patients with CAI. Cite this article Bone Joint Res 2014;3:241–5

Aims

The aims were to assess whether joint-specific outcome after total knee arthroplasty (TKA) was influenced by implant design over a 12-year follow-up period, and whether patient-related factors were associated with loss to follow-up and mortality risk.

Aims

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.

Aims

The Oxford Shoulder Score (OSS) is a 12-item measure commonly used for the assessment of shoulder surgeries. This study explores whether computerized adaptive testing (CAT) provides a shortened, individually tailored questionnaire while maintaining test accuracy.

Aims

This study aimed to define the histopathology of degenerated humeral head cartilage and synovial inflammation of the glenohumeral joint in patients with omarthrosis (OmA) and cuff tear arthropathy (CTA). Additionally, the potential of immunohistochemical tissue biomarkers in reflecting the degeneration status of humeral head cartilage was evaluated.

Prediction tools are instruments which are commonly used to estimate the prognosis in oncology and facilitate clinical decision-making in a more personalized manner. Their popularity is shown by the increasing numbers of prediction tools, which have been described in the medical literature. Many of these tools have been shown to be useful in the field of soft-tissue sarcoma of the extremities (eSTS). In this annotation, we aim to provide an overview of the available prediction tools for eSTS, provide an approach for clinicians to evaluate the performance and usefulness of the available tools for their own patients, and discuss their possible applications in the management of patients with an eSTS.

Cite this article: Bone Joint J 2022;104-B(9):1011–1016.

Aims

Our primary aim was to establish the proportion of female orthopaedic consultants who perform arthroplasty via cases submitted to the National Joint Registry (NJR), which covers England, Wales, Northern Ireland, the Isle of Man, and Guernsey. Secondary aims included comparing time since specialist registration, private practice participation, and number of hospitals worked in between male and female surgeons.

Aims

To map the Oxford Knee Score (OKS) and High Activity Arthroplasty Score (HAAS) items to a common scale, and to investigate the psychometric properties of this new scale for the measurement of knee health.

Objectives. The National Hip Fracture Database (NHFD) publishes hospital-level risk-adjusted mortality rates following hip fracture surgery in England, Wales and Northern Ireland. The performance of the risk model used by the NHFD was compared with the widely-used Nottingham Hip Fracture Score. Methods. Data from 94 hospitals on patients aged 60 to 110 who had hip fracture surgery between May 2013 and July 2013 were analysed. Data were linked to the Office for National Statistics (ONS) death register to calculate the 30-day mortality rate. Risk of death was predicted for each patient using the NHFD and Nottingham models in a development dataset using logistic regression to define the models’ coefficients. This was followed by testing the performance of these refined models in a second validation dataset. Results. The 30-day mortality rate was 5.36% in the validation dataset (n = 3861), slightly lower than the 6.40% in the development dataset (n = 4044). The NHFD and Nottingham models showed a slightly lower discrimination in the validation dataset compared with the development dataset, but both still displayed moderate discriminative power (c-statistic for NHFD = 0.71, 95% confidence interval (CI) 0.67 to 0.74; Nottingham model = 0.70, 95% CI 0.68 to 0.75). Both models defined similar ranges of predicted mortality risk (1% to 18%) in assessment of calibration. Conclusions. Both models have limitations in predicting mortality for individual patients after hip fracture surgery, but the NHFD risk adjustment model performed as well as the widely-used Nottingham prognostic tool and is therefore a reasonable alternative for risk adjustment in the United Kingdom hip fracture population. Cite this article: Bone Joint Res 2017;6:550–556

Aims

The COVID-19 pandemic has caused unprecedented disruption to elective orthopaedic services. The primary objective of this study was to examine changes in functional scores in patients awaiting total hip arthroplasty (THA), total knee arthroplasty (TKA), and unicompartmental knee arthroplasty (UKA). Secondary objectives were to investigate differences between these groups and identify those in a health state ‘worse than death’ (WTD).

Aims

This study was designed to develop a model for predicting bone mineral density (BMD) loss of the femur after total hip arthroplasty (THA) using artificial intelligence (AI), and to identify factors that influence the prediction. Additionally, we virtually examined the efficacy of administration of bisphosphonate for cases with severe BMD loss based on the predictive model.

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.

Aims

Several previously identified patient-, injury-, and treatment-related factors are associated with the development of nonunion in distal femur fractures. However, the predictive value of these factors is not well defined. We aimed to assess the predictive ability of previously identified risk factors in the development of nonunion leading to secondary surgery in distal femur fractures.

Aims

This study aimed to evaluate the clinical application of the PJI-TNM classification for periprosthetic joint infection (PJI) by determining intraobserver and interobserver reliability. To facilitate its use in clinical practice, an educational app was subsequently developed and evaluated.