Aims. Our aim was to develop and validate nomograms that would predict the cumulative incidence of sarcoma-specific death (CISSD) and disease progression (CIDP) in patients with localized high-grade primary central and dedifferentiated chondrosarcoma. Methods. The study population consisted of 391 patients from two international sarcoma centres (development cohort) who had undergone definitive surgery for a localized high-grade (histological grade II or III) conventional primary central chondrosarcoma or dedifferentiated chondrosarcoma. Disease progression captured the first event of either metastasis or local recurrence. An independent cohort of 221 patients from three additional hospitals was used for external validation. Two nomograms were internally and externally validated for discrimination (c-index) and calibration plot. Results. In the development cohort, the CISSD at ten years was 32.9% (95% confidence interval (CI) 19.8% to 38.4%). Age at diagnosis, grade, and surgical margin were found to have significant effects on CISSD and CIDP in multivariate analyses. Maximum tumour diameter was also significantly associated with CISSD. In the development cohort, the c-indices for CISSD and CIDP at five years were 0.743 (95% CI 0.700 to 0.819) and 0.761 (95% CI 0.713 to 0.800), respectively. When applied to the validation cohort, the c-indices for CISSD and CIDP at five years were 0.839 (95% CI 0.763 to 0.916) and 0.749 (95% CI 0.672 to 0.825), respectively. The calibration plots for these two nomograms demonstrated good fit. Conclusion. Our nomograms performed well on internal and external validation and can be used to predict CISSD and CIDP after resection of localized high-grade conventional primary central and dedifferentiated chondrosarcomas. They provide a new tool with which clinicians can assess and advise individual patients about their prognosis. Cite this article: Bone Joint J 2020;102-B(12):1752–1759

Aims. To develop and externally validate a parsimonious statistical prediction model of 90-day mortality after elective total hip arthroplasty (THA), and to provide a web calculator for clinical usage. Methods. We included 53,099 patients with cemented THA due to osteoarthritis from the Swedish Hip Arthroplasty Registry for model derivation and internal validation, as well as 125,428 patients from England and Wales recorded in the National Joint Register for England, Wales, Northern Ireland, the Isle of Man, and the States of Guernsey (NJR) for external model validation. A model was developed using a bootstrap ranking procedure with a least absolute shrinkage and selection operator (LASSO) logistic regression model combined with piecewise linear regression. Discriminative ability was evaluated by the area under the receiver operating characteristic curve (AUC). Calibration belt plots were used to assess model calibration. Results. A main effects model combining age, sex, American Society for Anesthesiologists (ASA) class, the presence of cancer, diseases of the central nervous system, kidney disease, and diagnosed obesity had good discrimination, both internally (AUC = 0.78, 95% confidence interval (CI) 0.75 to 0.81) and externally (AUC = 0.75, 95% CI 0.73 to 0.76). This model was superior to traditional models based on the Charlson (AUC = 0.66, 95% CI 0.62 to 0.70) and Elixhauser (AUC = 0.64, 95% CI 0.59 to 0.68) comorbidity indices. The model was well calibrated for predicted probabilities up to 5%. Conclusion. We developed a parsimonious model that may facilitate individualized risk assessment prior to one of the most common surgical interventions. We have published a web calculator to aid clinical decision-making. Cite this article: Bone Joint J 2021;103-B(3):469–478

Aims

The purpose of this study was to develop a convolutional neural network (CNN) for fracture detection, classification, and identification of greater tuberosity displacement ≥ 1 cm, neck-shaft angle (NSA) ≤ 100°, shaft translation, and articular fracture involvement, on plain radiographs.

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. Methods. Data pre-processing and analyses were conducted according to the Artificial intelligence to Revolutionize the patient Care pathway in Hip and knEe aRthroplastY (ARCHERY) project protocol. This included use of de-identified Scottish regional clinical data of patients referred for consideration of THA/TKA, held in a secure data environment designed for artificial intelligence (AI) inference. Only preoperative radiology reports were included. NLP algorithms were based on the freely available GatorTron model, a LLM trained on over 82 billion words of de-identified clinical text. Two inference tasks were performed: assessment after model-fine tuning (50 Epochs and three cycles of k-fold cross validation), and external validation. Results. For THA, there were 5,558 patient radiology reports included, of which 4,137 were used for model training and testing, and 1,421 for external validation. Following training, model performance demonstrated average (mean across three folds) accuracy, F1 score, and area under the receiver operating curve (AUROC) values of 0.850 (95% confidence interval (CI) 0.833 to 0.867), 0.813 (95% CI 0.785 to 0.841), and 0.847 (95% CI 0.822 to 0.872), respectively. For TKA, 7,457 patient radiology reports were included, with 3,478 used for model training and testing, and 3,152 for external validation. Performance metrics included accuracy, F1 score, and AUROC values of 0.757 (95% CI 0.702 to 0.811), 0.543 (95% CI 0.479 to 0.607), and 0.717 (95% CI 0.657 to 0.778) respectively. There was a notable deterioration in performance on external validation in both cohorts. Conclusion. The use of routinely available preoperative radiology reports provides promising potential to help screen suitable candidates for THA, but not for TKA. The external validation results demonstrate the importance of further model testing and training when confronted with new clinical cohorts. Cite this article: Bone Joint J 2024;106-B(7):688–695

Aims. The aim of this study was to identify factors associated with five-year cancer-related mortality in patients with limb and trunk soft-tissue sarcoma (STS) and develop and validate machine learning algorithms in order to predict five-year cancer-related mortality in these patients. Methods. Demographic, clinicopathological, and treatment variables of limb and trunk STS patients in the Surveillance, Epidemiology, and End Results Program (SEER) database from 2004 to 2017 were analyzed. Multivariable logistic regression was used to determine factors significantly associated with five-year cancer-related mortality. Various machine learning models were developed and compared using area under the curve (AUC), calibration, and decision curve analysis. The model that performed best on the SEER testing data was further assessed to determine the variables most important in its predictive capacity. This model was externally validated using our institutional dataset. Results. A total of 13,646 patients with STS from the SEER database were included, of whom 35.9% experienced five-year cancer-related mortality. The random forest model performed the best overall and identified tumour size as the most important variable when predicting mortality in patients with STS, followed by M stage, histological subtype, age, and surgical excision. Each variable was significant in logistic regression. External validation yielded an AUC of 0.752. Conclusion. This study identified clinically important variables associated with five-year cancer-related mortality in patients with limb and trunk STS, and developed a predictive model that demonstrated good accuracy and predictability. Orthopaedic oncologists may use these findings to further risk-stratify their patients and recommend an optimal course of treatment. Cite this article: Bone Joint J 2023;105-B(6):702–710

Aims. The aim of this study was to develop and evaluate a deep learning-based model for classification of hip fractures to enhance diagnostic accuracy. Methods. A retrospective study used 5,168 hip anteroposterior radiographs, with 4,493 radiographs from two institutes (internal dataset) for training and 675 radiographs from another institute for validation. A convolutional neural network (CNN)-based classification model was trained on four types of hip fractures (Displaced, Valgus-impacted, Stable, and Unstable), using DAMO-YOLO for data processing and augmentation. The model’s accuracy, sensitivity, specificity, Intersection over Union (IoU), and Dice coefficient were evaluated. Orthopaedic surgeons’ diagnoses served as the reference standard, with comparisons made before and after artificial intelligence assistance. Results. The accuracy, sensitivity, specificity, IoU, and Dice coefficients of the model for the four fracture categories in the internal dataset were as follows: Displaced (1.0, 0.79, 1.0, 0.70, 0.82), Valgus-impacted (1.0, 0.80, 1.0, 0.70, 0.82), Stable (0.99, 0.95, 0.99, 0.83, 0.89), and Unstable (1.0, 0.98, 0.99, 0.86, 0.92), respectively. For the external validation dataset, the sensitivity and specificity were as follows: Displaced (0.83, 0.94), Valgus-impacted (0.89, 0.90), Stable (0.88, 0.95), and Unstable (0.85, 0.99), respectively. The overall means (Micro AVG and Macro AVG) for the external dataset were Micro AVG (0.83 (SD 0.05), 0.96 (SD 0.01)) and Macro AVG (0.69 (SD 0.02), 0.95 (SD 0.02)), respectively. Conclusion. Compared to human diagnosis alone, our study demonstrates that the developed model significantly improves the accuracy of detecting and classifying hip fractures. Our model has shown great potential in assisting clinicians with the accurate diagnosis and classification of hip fractures. Cite this article: Bone Joint J 2025;107-B(2):213–220

Aims. Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials. Methods. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures. Results. Out of 1,160 studies initially identified, 39 were included. Most studies (85%) were published between 2020 and 2024, with 82% using publicly available datasets, primarily the Osteoarthritis Initiative. ML methods were predominantly supervised, with significant variability in the definitions of OA progression: most studies focused on structural changes (59%), while fewer addressed pain progression or both. Deep learning was used in 44% of studies, while automated ML was used in 5%. There was a lack of standardization in evaluation metrics and limited external validation. Interpretability was explored in 54% of studies, primarily using SHapley Additive exPlanations. Conclusion. Our systematic review demonstrates the feasibility of ML models in predicting OA progression, but also uncovers critical limitations that currently restrict their clinical applicability. Future priorities should include diversifying data sources, standardizing outcome measures, enforcing rigorous validation, and integrating more sophisticated algorithms. This paradigm shift from predictive modelling to actionable clinical tools has the potential to transform patient care and disease management in orthopaedic practice. Cite this article: Bone Joint J 2024;106-B(11):1216–1222

Aims. The primary aim of this study was to develop a reliable, effective radiological score to assess the healing of humeral shaft fractures, the Radiographic Union Score for HUmeral fractures (RUSHU). The secondary aim was to assess whether the six-week RUSHU was predictive of nonunion at six months after the injury. Patients and Methods. Initially, 20 patients with radiographs six weeks following a humeral shaft fracture were selected at random from a trauma database and scored by three observers, based on the Radiographic Union Scale for Tibial fractures system. After refinement of the RUSHU criteria, a second group of 60 patients with radiographs six weeks after injury, 40 with fractures that united and 20 with fractures that developed nonunion, were scored by two blinded observers. Results. After refinement, the interobserver intraclass correlation coefficient (ICC) was 0.79 (95% confidence interval (CI) 0.67 to 0.87), indicating substantial agreement. At six weeks after injury, patients whose fractures united had a significantly higher median score than those who developed nonunion (10 vs 7; p < 0.001). A receiver operating characteristic curve determined that a RUSHU cut-off of < 8 was predictive of nonunion (area under the curve = 0.84, 95% CI 0.74 to 0.94). The sensitivity was 75% and specificity 80% with a positive predictive value (PPV) of 65% and a negative predictive value of 86%. Patients with a RUSHU < 8 (n = 23) were more likely to develop nonunion than those with a RUSHU ≥ 8 (n = 37, odds ratio 12.0, 95% CI 3.4 to 42.9). Based on a PPV of 65%, if all patients with a RUSHU < 8 underwent fixation, the number of procedures needed to avoid one nonunion would be 1.5. Conclusion. The RUSHU is reliable and effective in identifying patients at risk of nonunion of a humeral shaft fracture at six weeks after injury. This tool requires external validation but could potentially reduce the morbidity associated with delayed treatment of an established nonunion. Cite this article: Bone Joint J 2019;101-B:1300–1306

Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered.

Cite this article: Bone Joint J 2022;104-B(12):1292–1303.

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

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.

Aims

This study aimed to compare the performance of survival prediction models for bone metastases of the extremities (BM-E) with pathological fractures in an Asian cohort, and investigate patient characteristics associated with survival.

Understanding spinopelvic mechanics is important for the success of total hip arthroplasty (THA). Despite significant advancements in appreciating spinopelvic balance, numerous challenges remain. It is crucial to recognize the individual variability and postoperative changes in spinopelvic parameters and their consequential impact on prosthetic component positioning to mitigate the risk of dislocation and enhance postoperative outcomes. This review describes the integration of advanced diagnostic approaches, enhanced technology, implant considerations, and surgical planning, all tailored to the unique anatomy and biomechanics of each patient. It underscores the importance of accurately predicting postoperative spinopelvic mechanics, selecting suitable imaging techniques, establishing a consistent nomenclature for spinopelvic stiffness, and considering implant-specific strategies. Furthermore, it highlights the potential of artificial intelligence to personalize care.

Cite this article: Bone Joint J 2024;106-B(11):1206–1215.

Economic evaluation provides a framework for assessing the costs and consequences of alternative programmes or interventions. One common vehicle for economic evaluations in the healthcare context is the decision-analytic model, which synthesizes information on parameter inputs (for example, probabilities or costs of clinical events or health states) from multiple sources and requires application of mathematical techniques, usually within a software program. A plethora of decision-analytic modelling-based economic evaluations of orthopaedic interventions have been published in recent years. This annotation outlines a number of issues that can help readers, reviewers, and decision-makers interpret evidence from decision-analytic modelling-based economic evaluations of orthopaedic interventions.

Cite this article: Bone Joint J 2023;105-B(1):17–20.

Artificial intelligence (AI) is, in essence, the concept of ‘computer thinking’, encompassing methods that train computers to perform and learn from executing certain tasks, called machine learning, and methods to build intricate computer models that both learn and adapt, called complex neural networks. Computer vision is a function of AI by which machine learning and complex neural networks can be applied to enable computers to capture, analyze, and interpret information from clinical images and visual inputs. This annotation summarizes key considerations and future perspectives concerning computer vision, questioning the need for this technology (the ‘why’), the current applications (the ‘what’), and the approach to unlocking its full potential (the ‘how’).

Cite this article: Bone Joint J 2022;104-B(8):911–914.

Aims

Obtaining solid implant fixation is crucial in revision total knee arthroplasty (rTKA) to avoid aseptic loosening, a major reason for re-revision. This study aims to validate a novel grading system that quantifies implant fixation across three anatomical zones (epiphysis, metaphysis, diaphysis).

Aims

Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are common orthopaedic procedures requiring postoperative radiographs to confirm implant positioning and identify complications. Artificial intelligence (AI)-based image analysis has the potential to automate this postoperative surveillance. The aim of this study was to prepare a scoping review to investigate how AI is being used in the analysis of radiographs following THA and TKA, and how accurate these tools are.

We obtained information from the Elective Orthopaedic Centre on 1523 patients with baseline and six-month Oxford hip scores (OHS) after undergoing primary hip replacement (THR) and 1784 patients with Oxford knee scores (OKS) for primary knee replacement (TKR) who completed a six-month satisfaction questionnaire. Receiver operating characteristic curves identified an absolute change in OHS of 14 points or more as the point that discriminates best between patients’ satisfaction levels and an 11-point change for the OKS. Satisfaction is highest (97.6%) in patients with an absolute change in OHS of 14 points or more, compared with lower levels of satisfaction (81.8%) below this threshold. Similarly, an 11-point absolute change in OKS was associated with 95.4% satisfaction compared with 76.5% below this threshold. For the six-month OHS a score of 35 points or more distinguished patients with the highest satisfaction level, and for the six-month OKS 30 points or more identified the highest level of satisfaction. The thresholds varied according to patients’ pre-operative score, where those with severe pre-operative pain/function required a lower six-month score to achieve the highest levels of satisfaction. Our data suggest that the choice of a six-month follow-up to assess patient-reported outcomes of THR/TKR is acceptable. The thresholds help to differentiate between patients with different levels of satisfaction, but external validation will be required prior to general implementation in clinical practice

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.

Aims

Adverse spinal motion or balance (spine mobility) and adverse pelvic mobility, in combination, are often referred to as adverse spinopelvic mobility (SPM). A stiff lumbar spine, large posterior standing pelvic tilt, and severe sagittal spinal deformity have been identified as risk factors for increased hip instability. Adverse SPM can create functional malposition of the acetabular components and hence is an instability risk. Adverse pelvic mobility is often, but not always, associated with abnormal spinal motion parameters. Dislocation rates for dual-mobility articulations (DMAs) have been reported to be between 0% and 1.1%. The aim of this study was to determine the early survivorship from the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) of patients with adverse SPM who received a DMA.

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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.

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The aim of this study was to review the current evidence surrounding curve type and morphology on curve progression risk in adolescent idiopathic scoliosis (AIS).

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.

In recent years, machine learning (ML) and artificial neural networks (ANNs), a particular subset of ML, have been adopted by various areas of healthcare. A number of diagnostic and prognostic algorithms have been designed and implemented across a range of orthopaedic sub-specialties to date, with many positive results. However, the methodology of many of these studies is flawed, and few compare the use of ML with the current approach in clinical practice. Spinal surgery has advanced rapidly over the past three decades, particularly in the areas of implant technology, advanced surgical techniques, biologics, and enhanced recovery protocols. It is therefore regarded an innovative field. Inevitably, spinal surgeons will wish to incorporate ML into their practice should models prove effective in diagnostic or prognostic terms. The purpose of this article is to review published studies that describe the application of neural networks to spinal surgery and which actively compare ANN models to contemporary clinical standards allowing evaluation of their efficacy, accuracy, and relatability. It also explores some of the limitations of the technology, which act to constrain the widespread adoption of neural networks for diagnostic and prognostic use in spinal care. Finally, it describes the necessary considerations should institutions wish to incorporate ANNs into their practices. In doing so, the aim of this review is to provide a practical approach for spinal surgeons to understand the relevant aspects of neural networks.

Cite this article: Bone Joint J 2021;103-B(9):1442–1448.

Aims

The purpose of this study was to develop a personalized outcome prediction tool, to be used with knee arthroplasty patients, that predicts outcomes (lengths of stay (LOS), 90 day readmission, and one-year patient-reported outcome measures (PROMs) on an individual basis and allows for dynamic modifiable risk factors.

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Failure of irrigation and debridement (I&D) for prosthetic joint infection (PJI) is influenced by numerous host, surgical, and pathogen-related factors. We aimed to develop and validate a practical, easy-to-use tool based on machine learning that may accurately predict outcome following I&D surgery taking into account the influence of numerous factors.

Aims

The aim of this study was to determine if the Oxford Knee and Hip Score (OKHS) can accurately predict when a primary knee or hip referral is deemed nonsurgical versus surgical by the surgeon during their first consultation, and to identify nonsurgical OKHS screening thresholds.

Aims

We investigated whether blood metal ion levels could effectively identify patients with bilateral Birmingham Hip Resurfacing (BHR) implants who have adverse reactions to metal debris (ARMD).

Graft-tunnel mismatch of the bone-patellar tendon-bone (BPTB) graft is a major concern during anatomical anterior cruciate ligament (ACL) reconstruction if the femoral tunnel is positioned using a far medial portal technique, as the femoral tunnel tends to be shorter compared with that positioned using a transtibial portal technique. This study describes an accurate method of calculating the ideal length of bone plugs of a BPTB graft required to avoid graft–tunnel mismatch during anatomical ACL reconstruction using a far medial portal technique of femoral tunnel positioning.

Based on data obtained intra-operatively from 60 anatomical ACL reconstruction procedures, we calculated the length of bone plugs required in the BPTB graft to avoid graft–tunnel mismatch. When this was prevented in all the 60 cases, we found that the mean length of femoral bone plug that remained in contact with the interference screw within the femoral tunnel was 14 mm (12 to 22) and the mean length of tibial bone plug that remained in contact with the interference screw within the tibial tunnel was 23 mm (18 to 28). These results were used to validate theoretical formulae developed to predict the required length of bone plugs in BPTB graft during anatomical ACL reconstruction using a far medial portal technique.

Cite this article: Bone Joint J 2015;97-B:324–8.

Clinical prediction algorithms are used to differentiate transient synovitis from septic arthritis. These algorithms typically include the erythrocyte sedimentation rate (ESR), although in clinical practice measurement of the C-reactive protein (CRP) has largely replaced the ESR. We evaluated the use of CRP in a predictive algorithm.

The records of 311 children with an effusion of the hip, which was confirmed on ultrasound, were reviewed (mean age 5.3 years (0.2 to 15.1)). Of these, 269 resolved without intervention and without long-term sequelae and were considered to have had transient synovitis. The remaining 42 underwent arthrotomy because of suspicion of septic arthritis. Infection was confirmed in 29 (18 had micro-organisms isolated and 11 had a high synovial fluid white cell count). In the remaining 13 no evidence of infection was found and they were also considered to have had transient synovitis. In total 29 hips were categorised as septic arthritis and 282 as transient synovitis. The temperature, weight-bearing status, peripheral white blood cell count and CRP was reviewed in each patient.

A CRP > 20 mg/l was the strongest independent risk factor for septic arthritis (odds ratio 81.9, p < 0.001). A multivariable prediction model revealed that only two determinants (weight-bearing status and CRP > 20 mg/l) were independent in differentiating septic arthritis from transient synovitis. Individuals with neither predictor had a < 1% probability of septic arthritis, but those with both had a 74% probability of septic arthritis. A two-variable algorithm can therefore quantify the risk of septic arthritis, and is an excellent negative predictor.

The revised Tokuhashi, Tomita and modified Bauer scores are commonly used to make difficult decisions in the management of patients presenting with spinal metastases. A prospective cohort study of 199 consecutive patients presenting with spinal metastases, treated with either surgery and/or radiotherapy, was used to compare the three systems. Cox regression, Nagelkerke’s R² and Harrell’s concordance were used to compare the systems and find their best predictive items. The three systems were equally good in terms of overall prognostic performance. Their most predictive items were used to develop the Oswestry Spinal Risk Index (OSRI), which has a similar concordance, but a larger coefficient of determination than any of these three scores. A bootstrap procedure was used to internally validate this score and determine its prediction optimism.

The OSRI is a simple summation of two elements: primary tumour pathology (PTP) and general condition (GC): OSRI = PTP + (2 – GC).

This simple score can predict life expectancy accurately in patients presenting with spinal metastases. It will be helpful in making difficult clinical decisions without the delay of extensive investigations.

Cite this article: Bone Joint J 2013;95-B:210–16.