Aim

The aim of this study was to develop an in-house multiplex PCR real-time assay on the LightCycler 480 system (Roche, Basel, Switzerland) with the aim of rapid detection of common pathogens in prosthetic joint infections (PJI), followed by validation on clinical samples (sonication fluid and tissue biopsies) routinely collected for PJI diagnosis.

Aim

Periprosthetic Joint Infection (PJI) is a devastating complication in hip and knee joint arthroplasty. The “JS BACH” classification system was developed in 2021 to stratify the complexity of PJI, and more importantly, to act as a tool to guide referrals to specialist centers. The “JS BACH” classification has not been validated in an external cohort. This study aimed to do so using a large prospective cohort from Australia and New Zealand.

Aim. This study aimed to externally validate promising preoperative PJI prediction models in a recent, multinational European cohort. Method. Three preoperative PJI prediction models (by Tan et al., Del Toro et al., and Bülow et al.) which previously demonstrated high levels of accuracy were selected for validation. A multicenter retrospective observational analysis was performed of patients undergoing total hip arthroplasty (THA) and total knee arthroplasty (TKA) between January 2020 and December 2021 and treated at centers in the Netherlands, Portugal, and Spain. Patient characteristics were compared between our cohort and those used to develop the prediction models. Model performance was assessed through discrimination and calibration. Results. A total of 2684 patients were included of whom 60 developed a PJI (2.2%). Our patient cohort differed from the models’ original cohorts in terms of demographic variables, procedural variables, and the prevalence of comorbidities. The c-statistics for the Tan, Del Toro, and Bülow models were 0.72, 0.69, and 0.72 respectively. Calibration was reasonable, but precise percentage estimates for PJI risk were most accurate for predicted risks up to 3-4%; the Tan model overestimated risks above 4%, while the Del Toro model underestimated risks above 3%. Conclusions. In this multinational cohort study, the Tan, Del Toro, and Bülow PJI prediction models were found to be externally valid for classifying high risk patients for developing a PJI. These models hold promise for clinical application to enhance preoperative patient counseling and targeted prevention strategies. Keywords. Periprosthetic Joint Infection (PJI), High Risk Groups, Prediction Models, Validation, Infection Prevention

Introduction

Degenerative meniscal tears are the most common meniscal lesions, representing huge clinical and socio-economic burdens. Their role in knee osteoarthritis (OA) onset and progression is well established and demonstrated by several retrospective studies. Effective preventive measures and non-surgical treatments for degenerative meniscal lesions are still lacking, also because of the lack of specific and accurate animal models in which test them. Thus, we aim to develop and validate an accurate animal model of meniscus degeneration.

Introduction. The ACTIVE(Advanced Cartilage Treatment with Injectable-hydrogel Validation of the Effect) study investigates safety and performance of a novel dextran-tyramine hydrogel implant for treatment of small cartilage defects in the knee (0.5-2.0cm2). The hydrogel is composed of a mixture of natural polymer conjugates that are mixed intra-operatively and which cross-link in situ through a mild enzymatic reaction, providing a cell-free scaffold for cartilage repair. Method. The ACTIVE study is split into a safety (n=10) and a performance cohort (n=36). The Knee Injury and Osteoarthritis Outcome Score (KOOS), pain (numeric rating scale, NRS), Short-Form Health Survey (SF-36) were compared at baseline and 3, 6, and 12 months after surgery. The primary performance hypothesis is an average change in the KOOS from baseline to 12 months (ΔKOOS) greater than a minimal clinically important change (MIC) of 10. No statistical tests were performed as these are preliminary data on a smaller portion of the total study. Result. All patients of the safety cohort (n=10, mean age±SD, 30±9 years) were treated with the hydrogel for a symptomatic (NRS≥4) cartilage defect on the femoral condyle or trochlear groove (mean size±SD, 1.2±0.4cm2). No signs of an adverse foreign tissue reaction or serious adverse events were recorded within the safety cohort. At final follow-up mean KOOS±SD was 66.9±23.5, mean NRS resting±SD was 1.3±1.9, NRS activity±SD was 3.8±2.9 and mean SF-36±SD was 72.0±10.9. ΔKOOS was 21. One patient sustained new knee trauma prior to final follow-up, affecting final scores considerably. When excluded, ΔKOOS was 24(n=9). Conclusion. These promising initial findings provide a solid basis for continuation and expansion of this unique cartilage treatment. The MIC of 10 was surpassed. Though, results should be interpreted cautiously as they are based solely on preliminary data of the first 10 patients. Acknowledgements. Study is sponsored by Hy2Care, producer of the CartRevive®(dextran-tyramine) Hydrogel implant

Aims

This study examined the relationship between obesity (OB) and osteoporosis (OP), aiming to identify shared genetic markers and molecular mechanisms to facilitate the development of therapies that target both conditions simultaneously.

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

This study explored the shared genetic traits and molecular interactions between postmenopausal osteoporosis (POMP) and sarcopenia, both of which substantially degrade elderly health and quality of life. We hypothesized that these motor system diseases overlap in pathophysiology and regulatory mechanisms.

Aims

The metabolic variations between the cartilage of osteoarthritis (OA) and Kashin-Beck disease (KBD) remain largely unknown. Our study aimed to address this by conducting a comparative analysis of the metabolic profiles present in the cartilage of KBD and OA.

Aims

Radiological residual acetabular dysplasia (RAD) has been reported in up to 30% of children who had successful brace treatment of infant developmental dysplasia of the hip (DDH). Predicting those who will resolve and those who may need corrective surgery is important to optimize follow-up protocols. In this study we have aimed to identify the prevalence and predictors of RAD at two years and five years post-bracing.

Background

The Manchester-Oxford Foot Questionnaire (MOxFQ) is a condition specific patient reported outcome measure (PROM) for foot and ankle surgery. It consists of 16 items across three subscales measuring distinct, but related traits: walking/standing ability, pain, and social interaction. Although it is the most used foot and ankle PROM in the UK, initial MOxFQ validation involved analysis of only 100 individuals undergoing hallux valgus surgery. This project aimed to establish whether an individual's response to the MOxFQ varies with anatomical region of disease (measurement invariance), and to explore structural validity of the factor structure (subscale items) of the MOxFQ.

Aims

Isolated fractures of the ulnar diaphysis are uncommon, occurring at a rate of 0.02 to 0.04 per 1,000 cases. Despite their infrequency, these fractures commonly give rise to complications, such as nonunion, limited forearm pronation and supination, restricted elbow range of motion, radioulnar synostosis, and prolonged pain. Treatment options for this injury remain a topic of debate, with limited research available and no consensus on the optimal approach. Therefore, this trial aims to compare clinical, radiological, and functional outcomes of two treatment methods: open reduction and internal fixation (ORIF) versus nonoperative treatment in patients with isolated ulnar diaphyseal fractures.

Introduction

Debridement, antibiotics irrigation and implant retention (DAIR) is a common management strategy for hip and knee prosthetic joint infections (PJI). However, failure rates remain high, which has led to the development of predictive tools to help determine success. These tools include KLIC and CRIME80 for acute-postoperative (AP) and acute haematogenous (AH) PJI respectively. We investigated whether these tools were applicable to a Waikato cohort.

OpenPredictor, a machine learning-enabled clinical decision aid, has been developed to manage backlogs in elective surgeries. It aims to optimise the use of high volume, low complexity surgical pathways by accurately stratifying patient risk, thereby facilitating the allocation of patients to the most suitable surgical sites. The tool augments elective surgical pathways by providing automated secondary opinions for perioperative risk assessments, enhancing decision-making. Its primary application is in elective sites utilising lighter pre-assessment methods, identifying patients with minimal complication risks and those high-risk individuals who may benefit from early pre-assessment.

The Phase 1 clinical evaluation of OpenPredictor entailed a prospective analysis of 156 patient records from elective hip and knee joint replacement surgeries. Using a polynomial logistic regression model, patients were categorised into high, moderate, and low-risk groups. This categorisation incorporated data from various sources, including patient demographics, co-morbidities, blood tests, and overall health status.

In identifying patients at risk of postoperative complications, OpenPredictor demonstrated parity with consultant-led preoperative assessments. It accurately flagged 70% of patients who later experienced complications as moderate or high risk. The tool's efficiency in risk prediction was evidenced by its balanced accuracy (75.6%), sensitivity (70% with a 95% confidence interval of 62.05% to 76.91%), and a high negative predictive value (96.7%).

OpenPredictor presents a scalable and consistent solution for managing elective surgery pathways, comparable in performance to secondary consultant opinions. Its integration into pre-assessment workflows assists in efficient patient categorisation, reduces late surgery cancellations, and optimises resource allocation. The Phase 1 evaluation of OpenPredictor underscores its potential for broader clinical application and highlights the need for ongoing data refinement and system integration to enhance its performance.

Aims

Surgical site infection (SSI) after soft-tissue sarcoma (STS) resection is a serious complication. The purpose of this retrospective study was to investigate the risk factors for SSI after STS resection, and to develop a nomogram that allows patient-specific risk assessment.

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.

Aims

This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA.

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.

Artificial Intelligence (AI) is becoming more powerful but is barely used to counter the growth in health care burden. AI applications to increase efficiency in orthopedics are rare. We questioned if (1) we could train machine learning (ML) algorithms, based on answers from digitalized history taking questionnaires, to predict treatment of hip osteoartritis (either conservative or surgical); (2) such an algorithm could streamline clinical consultation.

Multiple ML models were trained on 600 annotated (80% training, 20% test) digital history taking questionnaires, acquired before consultation. Best performing models, based on balanced accuracy and optimized automated hyperparameter tuning, were build into our daily clinical orthopedic practice. Fifty patients with hip complaints (>45 years) were prospectively predicted and planned (partly blinded, partly unblinded) for consultation with the physician assistant (conservative) or orthopedic surgeon (operative). Tailored patient information based on the prediction was automatically sent to a smartphone app. Level of evidence: IV.

Random Forest and BernoulliNB were the most accurate ML models (0.75 balanced accuracy). Treatment prediction was correct in 45 out of 50 consultations (90%), p<0.0001 (sign and binomial test). Specialized consultations where conservatively predicted patients were seen by the physician assistant and surgical patients by the orthopedic surgeon were highly appreciated and effective.

Treatment strategy of hip osteoartritis based on answers from digital history taking questionnaires was accurately predicted before patients entered the hospital. This can make outpatient consultation scheduling more efficient and tailor pre-consultation patient education.