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. Methods. We conducted a retrospective cohort study of adult patients with traumatic distal femur fracture treated with lateral locking plate between 2009 and 2018. The patients who underwent secondary surgery due to fracture healing problem or plate failure were considered having nonunion. Background knowledge of risk factors of distal femur fracture nonunion based on previous literature was used to form an initial set of variables. A logistic regression model was used with previously identified patient- and injury-related variables (age, sex, BMI, diabetes, smoking, periprosthetic fracture, open fracture, trauma energy, fracture zone length, fracture comminution, medial side comminution) in the first analysis and with treatment-related variables (different surgeon-controlled factors, e.g. plate length, screw placement, and proximal fixation) in the second analysis to predict the nonunion leading to secondary surgery in distal femur fractures. Results. We were able to include 299 fractures in 291 patients. Altogether, 31/299 fractures (10%) developed nonunion. In the first analysis, pseudo-R. 2. was 0.27 and area under the receiver operating characteristic curve (AUC) was 0.81. BMI was the most important variable in the prediction. In the second analysis, pseudo-R. 2. was 0.06 and AUC was 0.67. Plate length was the most important variable in the prediction. Conclusion. The model including patient- and injury-related factors had moderate fit and predictive ability in the prediction of distal femur fracture nonunion leading to secondary surgery. BMI was the most important variable in prediction of nonunion. Surgeon-controlled factors had a minor role in prediction of nonunion. Cite this article: Bone Jt Open 2023;4(8):584–593

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.

Objectives. Our objective was to predict the knee extension strength and post-operative function in quadriceps resection for soft-tissue sarcoma of the thigh. Methods. A total of 18 patients (14 men, four women) underwent total or partial quadriceps resection for soft-tissue sarcoma of the thigh between 2002 and 2014. The number of resected quadriceps was surveyed, knee extension strength was measured with the Biodex isokinetic dynamometer system (affected side/unaffected side) and relationships between these were examined. The Musculoskeletal Tumor Society (MSTS) score, Toronto Extremity Salvage Score (TESS), European Quality of Life-5 Dimensions (EQ-5D) score and the Short Form 8 were used to evaluate post-operative function and examine correlations with extension strength. The cutoff value for extension strength to expect good post-operative function was also calculated using a receiver operating characteristic (ROC) curve and Fisher’s exact test. Results. Extension strength decreased when the number of resected quadriceps increased (p < 0.001), and was associated with lower MSTS score, TESS and EQ-5D (p = 0.004, p = 0.005, p = 0.006, respectively). Based on the functional evaluation scales, the cutoff value of extension strength was 56.2%, the equivalent to muscle strength with resection of up to two muscles. Conclusion. Good post-operative results can be expected if at least two quadriceps muscles are preserved. Cite this article: A. Tanaka, Y. Yoshimura, K. Aoki, M. Kito, M. Okamoto, S. Suzuki, T. Momose, H. Kato. Knee extension strength and post-operative functional prediction in quadriceps resection for soft-tissue sarcoma of the thigh. Bone Joint Res 2016;5:232–238. DOI: 10.1302/2046-3758.56.2000631

Aims. Accurate skeletal age and final adult height prediction methods in paediatric orthopaedics are crucial for determining optimal timing of growth-guiding interventions and minimizing complications in treatments of various conditions. This study aimed to evaluate the accuracy of final adult height predictions using the central peak height (CPH) method with long leg X-rays and four different multiplier tables. Methods. This study included 31 patients who underwent temporary hemiepiphysiodesis for varus or valgus deformity of the leg between 2014 and 2020. The skeletal age at surgical intervention was evaluated using the CPH method with long leg radiographs. The true final adult height (FH. TRUE. ) was determined when the growth plates were closed. The final height prediction accuracy of four different multiplier tables (1. Bayley and Pinneau; 2. Paley et al; 3. Sanders – Greulich and Pyle (SGP); and 4. Sanders – peak height velocity (PHV)) was then compared using either skeletal age or chronological age. Results. All final adult height predictions overestimated the FH. TRUE. , with the SGP multiplier table having the lowest overestimation and lowest absolute deviation when using both chronological age and skeletal age. There were no significant differences in final height prediction accuracy between using skeletal age and chronological age with PHV (p = 0.652) or SGP multiplier tables (p = 0.969). Adult height predictions with chronological age and SGP (r = 0.769; p ≤ 0.001), as well as chronological age and PHV (r = 0.822; p ≤ 0.001), showed higher correlations with FH. TRUE. than predictions with skeletal age and SGP (r = 0.657; p ≤ 0.001) or skeletal age and PHV (r = 0.707; p ≤ 0.001). Conclusion. There was no significant improvement in adult height prediction accuracy when using the CPH method compared to chronological age alone. The study concludes that there is no advantage in routinely using the CPH method for skeletal age determination over the simple use of chronological age. The findings highlight the need for more accurate methods to predict final adult height in contemporary patient populations. Cite this article: Bone Jt Open 2023;4(10):750–757

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. This systematic review aims to identify 3D predictors derived from biplanar reconstruction, and to describe current methods for improving curve prediction in patients with mild adolescent idiopathic scoliosis. Methods. A comprehensive search was conducted by three independent investigators on MEDLINE, PubMed, Web of Science, and Cochrane Library. Search terms included “adolescent idiopathic scoliosis”,“3D”, and “progression”. The inclusion and exclusion criteria were carefully defined to include clinical studies. Risk of bias was assessed with the Quality in Prognostic Studies tool (QUIPS) and Appraisal tool for Cross-Sectional Studies (AXIS), and level of evidence for each predictor was rated with the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. In all, 915 publications were identified, with 377 articles subjected to full-text screening; overall, 31 articles were included. Results. Torsion index (TI) and apical vertebral rotation (AVR) were identified as accurate predictors of curve progression in early visits. Initial TI > 3.7° and AVR > 5.8° were predictive of curve progression. Thoracic hypokyphosis was inconsistently observed in progressive curves with weak evidence. While sagittal wedging was observed in mild curves, there is insufficient evidence for its correlation with curve progression. In curves with initial Cobb angle < 25°, Cobb angle was a poor predictor for future curve progression. Prediction accuracy was improved by incorporating serial reconstructions in stepwise layers. However, a lack of post-hoc analysis was identified in studies involving geometrical models. Conclusion. For patients with mild curves, TI and AVR were identified as predictors of curve progression, with TI > 3.7° and AVR > 5.8° found to be important thresholds. Cobb angle acts as a poor predictor in mild curves, and more investigations are required to assess thoracic kyphosis and wedging as predictors. Cumulative reconstruction of radiographs improves prediction accuracy. Comprehensive analysis between progressive and non-progressive curves is recommended to extract meaningful thresholds for clinical prognostication. Cite this article: Bone Jt Open 2024;5(3):243–251

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. The aim of this study was to evaluate the reliability and validity of a patient-specific algorithm which we developed for predicting changes in sagittal pelvic tilt after total hip arthroplasty (THA). Methods. This retrospective study included 143 patients who underwent 171 THAs between April 2019 and October 2020 and had full-body lateral radiographs preoperatively and at one year postoperatively. We measured the pelvic incidence (PI), the sagittal vertical axis (SVA), pelvic tilt, sacral slope (SS), lumbar lordosis (LL), and thoracic kyphosis to classify patients into types A, B1, B2, B3, and C. The change of pelvic tilt was predicted according to the normal range of SVA (0 mm to 50 mm) for types A, B1, B2, and B3, and based on the absolute value of one-third of the PI-LL mismatch for type C patients. The reliability of the classification of the patients and the prediction of the change of pelvic tilt were assessed using kappa values and intraclass correlation coefficients (ICCs), respectively. Validity was assessed using the overall mean error and mean absolute error (MAE) for the prediction of the change of pelvic tilt. Results. The kappa values were 0.927 (95% confidence interval (CI) 0.861 to 0.992) and 0.945 (95% CI 0.903 to 0.988) for the inter- and intraobserver reliabilities, respectively, and the ICCs ranged from 0.919 to 0.997. The overall mean error and MAE for the prediction of the change of pelvic tilt were -0.3° (SD 3.6°) and 2.8° (SD 2.4°), respectively. The overall absolute change of pelvic tilt was 5.0° (SD 4.1°). Pre- and postoperative values and changes in pelvic tilt, SVA, SS, and LL varied significantly among the five types of patient. Conclusion. We found that the proposed algorithm was reliable and valid for predicting the standing pelvic tilt after THA. Cite this article: Bone Joint J 2024;106-B(1):19–27

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. Methods. The study included 538 joints that underwent primary THA. The patients were divided into groups using unsupervised time series clustering for five-year BMD loss of Gruen zone 7 postoperatively, and a machine-learning model to predict the BMD loss was developed. Additionally, the predictor for BMD loss was extracted using SHapley Additive exPlanations (SHAP). The patient-specific efficacy of bisphosphonate, which is the most important categorical predictor for BMD loss, was examined by calculating the change in predictive probability when hypothetically switching between the inclusion and exclusion of bisphosphonate. Results. Time series clustering allowed us to divide the patients into two groups, and the predictive factors were identified including patient- and operation-related factors. The area under the receiver operating characteristic (ROC) curve (AUC) for the BMD loss prediction averaged 0.734. Virtual administration of bisphosphonate showed on average 14% efficacy in preventing BMD loss of zone 7. Additionally, stem types and preoperative triglyceride (TG), creatinine (Cr), estimated glomerular filtration rate (eGFR), and creatine kinase (CK) showed significant association with the estimated patient-specific efficacy of bisphosphonate. Conclusion. Periprosthetic BMD loss after THA is predictable based on patient- and operation-related factors, and optimal prescription of bisphosphonate based on the prediction may prevent BMD loss. Cite this article: Bone Joint Res 2024;13(4):184–192

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.

Objectives

Unicompartmental knee arthroplasty (UKA) is an alternative to total knee arthroplasty with isolated medial or lateral compartment osteoarthritis. However, polyethylene wear can significantly reduce the lifespan of UKA. Different bearing designs and materials for UKA have been developed to change the rate of polyethylene wear. Therefore, the objective of this study is to investigate the effect of insert conformity and material on the predicted wear in mobile-bearing UKA using a previously developed computational wear method.

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. Methods. A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias. Results. A total of 40 studies reported on training and internal validation; four studies performed both development and external validation, and one study performed only external validation. The most commonly reported outcomes were mortality (33%, 15/45) and length of hospital stay (9%, 4/45), and the majority of prediction models were developed in the hip fracture population (60%, 27/45). The overall median completeness for the TRIPOD statement was 62% (interquartile range 30 to 81%). The overall risk of bias in the PROBAST tool was low in 24% (11/45), high in 69% (31/45), and unclear in 7% (3/45) of the studies. High risk of bias was mainly due to analysis domain concerns including small datasets with low number of outcomes, complete-case analysis in case of missing data, and no reporting of performance measures. Conclusion. The results of this study showed that despite a myriad of potential clinically useful applications, a substantial part of ML studies in orthopaedic trauma lack transparent reporting, and are at high risk of bias. These problems must be resolved by following established guidelines to instil confidence in ML models among patients and clinicians. Otherwise, there will remain a sizeable gap between the development of ML prediction models and their clinical application in our day-to-day orthopaedic trauma practice. Cite this article: Bone Jt Open 2024;5(1):9–19

Aims. Precise implant positioning, tailored to individual spinopelvic biomechanics and phenotype, is paramount for stability in total hip arthroplasty (THA). Despite a few studies on instability prediction, there is a notable gap in research utilizing artificial intelligence (AI). The objective of our pilot study was to evaluate the feasibility of developing an AI algorithm tailored to individual spinopelvic mechanics and patient phenotype for predicting impingement. Methods. This international, multicentre prospective cohort study across two centres encompassed 157 adults undergoing primary robotic arm-assisted THA. Impingement during specific flexion and extension stances was identified using the virtual range of motion (ROM) tool of the robotic software. The primary AI model, the Light Gradient-Boosting Machine (LGBM), used tabular data to predict impingement presence, direction (flexion or extension), and type. A secondary model integrating tabular data with plain anteroposterior pelvis radiographs was evaluated to assess for any potential enhancement in prediction accuracy. Results. We identified nine predictors from an analysis of baseline spinopelvic characteristics and surgical planning parameters. Using fivefold cross-validation, the LGBM achieved 70.2% impingement prediction accuracy. With impingement data, the LGBM estimated direction with 85% accuracy, while the support vector machine (SVM) determined impingement type with 72.9% accuracy. After integrating imaging data with a multilayer perceptron (tabular) and a convolutional neural network (radiograph), the LGBM’s prediction was 68.1%. Both combined and LGBM-only had similar impingement direction prediction rates (around 84.5%). Conclusion. This study is a pioneering effort in leveraging AI for impingement prediction in THA, utilizing a comprehensive, real-world clinical dataset. Our machine-learning algorithm demonstrated promising accuracy in predicting impingement, its type, and direction. While the addition of imaging data to our deep-learning algorithm did not boost accuracy, the potential for refined annotations, such as landmark markings, offers avenues for future enhancement. Prior to clinical integration, external validation and larger-scale testing of this algorithm are essential. Cite this article: Bone Jt Open 2024;5(8):671–680

Aims. No predictive model has been published to forecast operating time for total knee arthroplasty (TKA). The aims of this study were to design and validate a predictive model to estimate operating time for robotic-assisted TKA based on demographic data, and evaluate the added predictive power of CT scan-based predictors and their impact on the accuracy of the predictive model. Methods. A retrospective study was conducted on 1,061 TKAs performed from January 2016 to December 2019 with an image-based robotic-assisted system. Demographic data included age, sex, height, and weight. The femoral and tibial mechanical axis and the osteophyte volume were calculated from CT scans. These inputs were used to develop a predictive model aimed to predict operating time based on demographic data only, and demographic and 3D patient anatomy data. Results. The key factors for predicting operating time were the surgeon and patient weight, followed by 12 anatomical parameters derived from CT scans. The predictive model based only on demographic data showed that 90% of predictions were within 15 minutes of actual operating time, with 73% within ten minutes. The predictive model including demographic data and CT scans showed that 94% of predictions were within 15 minutes of actual operating time and 88% within ten minutes. Conclusion. The primary factors for predicting robotic-assisted TKA operating time were surgeon, patient weight, and osteophyte volume. This study demonstrates that incorporating 3D patient-specific data can improve operating time predictions models, which may lead to improved operating room planning and efficiency. Cite this article: Bone Jt Open 2022;3(5):383–389

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. 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 aims of this study were to assess mapping models to predict the three-level version of EuroQoL five-dimension utility index (EQ-5D-3L) from the Oxford Knee Score (OKS) and validate these before and after total knee arthroplasty (TKA). Methods. A retrospective cohort of 5,857 patients was used to create the prediction models, and a second cohort of 721 patients from a different centre was used to validate the models, all of whom underwent TKA. Patient characteristics, BMI, OKS, and EQ-5D-3L were collected preoperatively and one year postoperatively. Generalized linear regression was used to formulate the prediction models. Results. There were significant correlations between the OKS and EQ-5D-3L preoperatively (r = 0.68; p < 0.001) and postoperatively (r = 0.77; p < 0.001) and for the change in the scores (r = 0.61; p < 0.001). Three different models (preoperative, postoperative, and change) were created. There were no significant differences between the actual and predicted mean EQ-5D-3L utilities at any timepoint or for change in the scores (p > 0.090) in the validation cohort. There was a significant correlation between the actual and predicted EQ-5D-3L utilities preoperatively (r = 0.63; p < 0.001) and postoperatively (r = 0.77; p < 0.001) and for the change in the scores (r = 0.56; p < 0.001). Bland-Altman plots demonstrated that a lower utility was overestimated, and higher utility was underestimated. The individual predicted EQ-5D-3L that was within ± 0.05 and ± 0.010 (minimal clinically important difference (MCID)) of the actual EQ-5D-3L varied between 13% to 35% and 26% to 64%, respectively, according to timepoint assessed and change in the scores, but was not significantly different between the modelling and validation cohorts (p ≥ 0.148). Conclusion. The OKS can be used to estimate EQ-5D-3L. Predicted individual patient utility error beyond the MCID varied from one-third to two-thirds depending on timepoint assessed, but the mean for a cohort did not differ and could be employed for this purpose. Cite this article: Bone Jt Open 2022;3(7):573–581

Aims. Developmental cervical spinal stenosis (DcSS) is a well-known predisposing factor for degenerative cervical myelopathy (DCM) but there is a lack of consensus on its definition. This study aims to define DcSS based on MRI, and its multilevel characteristics, to assess the prevalence of DcSS in the general population, and to evaluate the presence of DcSS in the prediction of developing DCM. Methods. This cross-sectional study analyzed MRI spine morphological parameters at C3 to C7 (including anteroposterior (AP) diameter of spinal canal, spinal cord, and vertebral body) from DCM patients (n = 95) and individuals recruited from the general population (n = 2,019). Level-specific median AP spinal canal diameter from DCM patients was used to screen for stenotic levels in the population-based cohort. An individual with multilevel (≥ 3 vertebral levels) AP canal diameter smaller than the DCM median values was considered as having DcSS. The most optimal cut-off canal diameter per level for DcSS was determined by receiver operating characteristic analyses, and multivariable logistic regression was performed for the prediction of developing DCM that required surgery. Results. A total of 2,114 individuals aged 64.6 years (SD 11.9) who underwent surgery from March 2009 to December 2016 were studied. The most optimal cut-off canal diameters for DcSS are: C3 < 12.9 mm, C4 < 11.8 mm, C5 < 11.9 mm, C6 < 12.3 mm, and C7 < 13.3 mm. Overall, 13.0% (262 of 2,019) of the population-based cohort had multilevel DcSS. Multilevel DcSS (odds ratio (OR) 6.12 (95% CI 3.97 to 9.42); p < 0.001) and male sex (OR 4.06 (95% CI 2.55 to 6.45); p < 0.001) were predictors of developing DCM. Conclusion. This is the first MRI-based study for defining DcSS with multilevel canal narrowing. Level-specific cut-off canal diameters for DcSS can be used for early identification of individuals at risk of developing DCM. Individuals with DcSS at ≥ three levels and male sex are recommended for close monitoring or early intervention to avoid traumatic spinal cord injuries from stenosis. Cite this article: Bone Joint J 2024;106-B(11):1333–1341

Aims. A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three patient-reported outcome measures (PROMs), our aim was: 1) to assess machine learning (ML), the simple pre-surgery PROM score, and logistic-regression (LR)-derived performance in their prediction of whether patients undergoing HA or KA achieve an improvement as high or higher than a calculated MCID; and 2) to test whether ML is able to outperform LR or pre-surgery PROM scores in predictive performance. Methods. MCIDs were derived using the change difference method in a sample of 1,843 HA and 1,546 KA patients. An artificial neural network, a gradient boosting machine, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic net, random forest, LR, and pre-surgery PROM scores were applied to predict MCID for the following PROMs: EuroQol five-dimension, five-level questionnaire (EQ-5D-5L), EQ visual analogue scale (EQ-VAS), Hip disability and Osteoarthritis Outcome Score-Physical Function Short-form (HOOS-PS), and Knee injury and Osteoarthritis Outcome Score-Physical Function Short-form (KOOS-PS). Results. Predictive performance of the best models per outcome ranged from 0.71 for HOOS-PS to 0.84 for EQ-VAS (HA sample). ML statistically significantly outperformed LR and pre-surgery PROM scores in two out of six cases. Conclusion. MCIDs can be predicted with reasonable performance. ML was able to outperform traditional methods, although only in a minority of cases. Cite this article: Bone Joint Res 2023;12(9):512–521

Aims. To determine the major risk factors for unplanned reoperations (UROs) following corrective surgery for adult spinal deformity (ASD) and their interactions, using machine learning-based prediction algorithms and game theory. Methods. Patients who underwent surgery for ASD, with a minimum of two-year follow-up, were retrospectively reviewed. In total, 210 patients were included and randomly allocated into training (70% of the sample size) and test (the remaining 30%) sets to develop the machine learning algorithm. Risk factors were included in the analysis, along with clinical characteristics and parameters acquired through diagnostic radiology. Results. Overall, 152 patients without and 58 with a history of surgical revision following surgery for ASD were observed; the mean age was 68.9 years (SD 8.7) and 66.9 years (SD 6.6), respectively. On implementing a random forest model, the classification of URO events resulted in a balanced accuracy of 86.8%. Among machine learning-extracted risk factors, URO, proximal junction failure (PJF), and postoperative distance from the posterosuperior corner of C7 and the vertical axis from the centroid of C2 (SVA) were significant upon Kaplan-Meier survival analysis. Conclusion. The major risk factors for URO following surgery for ASD, i.e. postoperative SVA and PJF, and their interactions were identified using a machine learning algorithm and game theory. Clinical benefits will depend on patient risk profiles. Cite this article: Bone Joint Res 2023;12(4):245–255

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

Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality

Aims. To investigate the optimal thresholds and diagnostic efficacy of commonly used serological and synovial fluid detection indexes for diagnosing periprosthetic joint infection (PJI) in patients who have rheumatoid arthritis (RA). Methods. The data from 348 patients who had RA or osteoarthritis (OA) and had previously undergone a total knee (TKA) and/or a total hip arthroplasty (THA) (including RA-PJI: 60 cases, RA-non-PJI: 80 cases; OA-PJI: 104 cases, OA-non-PJI: 104 cases) were retrospectively analyzed. A receiver operating characteristic curve was used to determine the optimal thresholds of the CRP, ESR, synovial fluid white blood cell count (WBC), and polymorphonuclear neutrophil percentage (PMN%) for diagnosing RA-PJI and OA-PJI. The diagnostic efficacy was evaluated by comparing the area under the curve (AUC) of each index and applying the results of the combined index diagnostic test. Results. For PJI prediction, the results of serological and synovial fluid indexes were different between the RA-PJI and OA-PJI groups. The optimal cutoff value of CRP for diagnosing RA-PJI was 12.5 mg/l, ESR was 39 mm/hour, synovial fluid WBC was 3,654/μl, and PMN% was 65.9%; and those of OA-PJI were 8.2 mg/l, 31 mm/hour, 2,673/μl, and 62.0%, respectively. In the RA-PJI group, the specificity (94.4%), positive predictive value (97.1%), and AUC (0.916) of synovial fluid WBC were higher than those of the other indexes. The optimal cutoff values of synovial fluid WBC and PMN% for diagnosing RA-PJI after THA were significantly higher than those of TKA. The specificity and positive predictive value of the combined index were 100%. Conclusion. Serum inflammatory and synovial fluid indexes can be used for diagnosing RA-PJI, for which synovial fluid WBC is the best detection index. Combining multiple detection indexes can provide a reference basis for the early and accurate diagnosis of RA-PJI. Cite this article: Bone Joint Res 2023;12(9):559–570

Aims. We assessed the value of the Clinical Frailty Scale (CFS) in the prediction of adverse outcome after hip fracture. Methods. Of 1,577 consecutive patients aged > 65 years with a fragility hip fracture admitted to one institution, for whom there were complete data, 1,255 (72%) were studied. Clinicians assigned CFS scores on admission. Audit personnel routinely prospectively completed the Standardised Audit of Hip Fracture in Europe form, including the following outcomes: 30-day survival; in-hospital complications; length of acute hospital stay; and new institutionalization. The relationship between the CFS scores and outcomes was examined graphically and the visual interpretations were tested statistically. The predictive values of the CFS and Nottingham Hip Fracture Score (NHFS) to predict 30-day mortality were compared using receiver operating characteristic area under the curve (AUC) analysis. Results. Significant non-linear associations between CFS and outcomes were observed. Risk of death within 30 days rose linearly for CFS 1 to 5, but plateaued for CFS > 5. The incidence of complications and length of stay rose linearly for CFS 1 to 4, but plateaued for CFS > 4. In contrast, the risk of new institutionalization rose linearly for CFS 1 to 8. The AUCs for 30-day mortality for the CFS and NHFS were very similar: CFS AUC 0.63 (95% CI 0.57 to 0.69) and NHFS AUC 0.63 (95% CI 0.57 to 0.69). Conclusion. Use of the CFS may provide useful information on outcomes for fitter patients presenting with hip fracture, but completion of the CFS by the admitting orthopaedic team does not appear successful in distinguishing between higher CFS categories, which define patients with frailty. This makes a strong case for the role of the orthogeriatrician in the early assessment of these patients. Further work is needed to understand why patients assessed as being of mild, moderate, and severe frailty do not result in different outcomes. Cite this article: Bone Joint J 2022;104-B(8):980–986

Aims. Advances in treatment have extended the life expectancy of patients with metastatic bone disease (MBD). Patients could experience more skeletal-related events (SREs) as a result of this progress. Those who have already experienced a SRE could encounter another local management for a subsequent SRE, which is not part of the treatment for the initial SRE. However, there is a noted gap in research on the rate and characteristics of subsequent SREs requiring further localized treatment, obligating clinicians to extrapolate from experiences with initial SREs when confronting subsequent ones. This study aimed to investigate the proportion of MBD patients developing subsequent SREs requiring local treatment, examine if there are prognostic differences at the initial treatment between those with single versus subsequent SREs, and determine if clinical, oncological, and prognostic features differ between initial and subsequent SRE treatments. Methods. This retrospective study included 3,814 adult patients who received local treatment – surgery and/or radiotherapy – for bone metastasis between 1 January 2010 and 31 December 2019. All included patients had at least one SRE requiring local treatment. A subsequent SRE was defined as a second SRE requiring local treatment. Clinical, oncological, and prognostic features were compared between single SREs and subsequent SREs using Mann-Whitney U test, Fisher’s exact test, and Kaplan–Meier curve. Results. Of the 3,814 patients with SREs, 3,159 (83%) patients had a single SRE and 655 (17%) patients developed a subsequent SRE. Patients who developed subsequent SREs generally had characteristics that favoured longer survival, such as higher BMI, higher albumin levels, fewer comorbidities, or lower neutrophil count. Once the patient got to the point of subsequent SRE, their clinical and oncological characteristics and one-year survival (28%) were not as good as those with only a single SRE (35%; p < 0.001), indicating that clinicians’ experiences when treating the initial SRE are not similar when treating a subsequent SRE. Conclusion. This study found that 17% of patients required treatments for a second, subsequent SRE, and the current clinical guideline did not provide a specific approach to this clinical condition. We observed that referencing the initial treatment, patients in the subsequent SRE group had longer six-week, 90-day, and one-year median survival than patients in the single SRE group. Once patients develop a subsequent SRE, they have a worse one-year survival rate than those who receive treatment for a single SRE. Future research should identify prognostic factors and assess the applicability of existing survival prediction models for better management of subsequent SREs. Cite this article: Bone Joint Res 2024;13(9):497–506

Objectives. In this prospective cohort study, we investigated whether patient-specific finite element (FE) models can identify patients at risk of a pathological femoral fracture resulting from metastatic bone disease, and compared these FE predictions with clinical assessments by experienced clinicians. Methods. A total of 39 patients with non-fractured femoral metastatic lesions who were irradiated for pain were included from three radiotherapy institutes. During follow-up, nine pathological fractures occurred in seven patients. Quantitative CT-based FE models were generated for all patients. Femoral failure load was calculated and compared between the fractured and non-fractured femurs. Due to inter-scanner differences, patients were analyzed separately for the three institutes. In addition, the FE-based predictions were compared with fracture risk assessments by experienced clinicians. Results. In institute 1, median failure load was significantly lower for patients who sustained a fracture than for patients with no fractures. In institutes 2 and 3, the number of patients with a fracture was too low to make a clear distinction. Fracture locations were well predicted by the FE model when compared with post-fracture radiographs. The FE model was more accurate in identifying patients with a high fracture risk compared with experienced clinicians, with a sensitivity of 89% versus 0% to 33% for clinical assessments. Specificity was 79% for the FE models versus 84% to 95% for clinical assessments. Conclusion. FE models can be a valuable tool to improve clinical fracture risk predictions in metastatic bone disease. Future work in a larger patient population should confirm the higher predictive power of FE models compared with current clinical guidelines. Cite this article: F. Eggermont, L. C. Derikx, N. Verdonschot, I. C. M. van der Geest, M. A. A. de Jong, A. Snyers, Y. M. van der Linden, E. Tanck. Can patient-specific finite element models better predict fractures in metastatic bone disease than experienced clinicians? Towards computational modelling in daily clinical practice. Bone Joint Res 2018;7:430–439. DOI: 10.1302/2046-3758.76.BJR-2017-0325.R2

Aims. The lateral centre-edge angle (LCEA) is a plain radiological measure of superolateral cover of the femoral head. This study aims to establish the correlation between 2D radiological and 3D CT measurements of acetabular morphology, and to describe the relationship between LCEA and femoral head cover (FHC). Methods. This retrospective study included 353 periacetabular osteotomies (PAOs) performed between January 2014 and December 2017. Overall, 97 hips in 75 patients had 3D analysis by Clinical Graphics, giving measurements for LCEA, acetabular index (AI), and FHC. Roentgenographical LCEA, AI, posterior wall index (PWI), and anterior wall index (AWI) were measured from supine AP pelvis radiographs. The correlation between CT and roentgenographical measurements was calculated. Sequential multiple linear regression was performed to determine the relationship between roentgenographical measurements and CT FHC. Results. CT-measured LCEA and AI correlated strongly with roentgenographical LCEA (r = 0.92; p < 0.001) and AI (r = 0.83; p < 0.001). Radiological LCEA correlated very strongly with CT FHC (r = 0.92; p < 0.001). The sum of AWI and PWI also correlated strongly with CTFHC (r = 0.73; p < 0.001). CT measurements of LCEA and AI were 3.4° less and 2.3° greater than radiological LCEA and AI measures. There was a linear relation between radiological LCEA and CT FHC. The linear regression model statistically significantly predicted FHC from LCEA, F(1,96) = 545.1 (p < 0.001), adjusted R. 2. = 85.0%, with the prediction equation: CT FHC(%) = 42.1 + 0.77(XRLCEA). Conclusion. CT and roentgenographical measurement of acetabular parameters are comparable. Currently, a radiological LCEA greater than 25° is considered normal. This study demonstrates that those with hip pain and normal radiological acetabular parameters may still have deficiencies in FHC. More sophisticated imaging techniques such as 3D CT should be considered for those with hip pain to identify deficiencies in FHC. Cite this article: Bone Jt Open 2022;3(1):12–19

Aims. To devise a method to quantify and optimize tightness when inserting cortical screws, based on bone characterization and screw geometry. Methods. Cortical human cadaveric diaphyseal tibiae screw holes (n = 20) underwent destructive testing to firstly establish the relationship between cortical thickness and experimental stripping torque (T. str. ), and secondly to calibrate an equation to predict T. str. Using the equation’s predictions, 3.5 mm screws were inserted (n = 66) to targeted torques representing 40% to 100% of T. str. , with recording of compression generated during tightening. Once the target torque had been achieved, immediate pullout testing was performed. Results. Cortical thickness predicted T. str. (R. 2. = 0.862; p < 0.001) as did an equation based on tensile yield stress, bone-screw friction coefficient, and screw geometries (R. 2. = 0.894; p < 0.001). Compression increased with screw tightness up to 80% of the maximum (R. 2. = 0.495; p < 0.001). Beyond 80%, further tightening generated no increase in compression. Pullout force did not change with variations in submaximal tightness beyond 40% of T. str. (R. 2. = 0.014; p = 0.175). Conclusion. Screw tightening between 70% and 80% of the predicted maximum generated optimum compression and pullout forces. Further tightening did not considerably increase compression, made no difference to pullout, and increased the risk of the screw holes being stripped. While further work is needed for development of intraoperative methods for accurate and reliable prediction of the maximum tightness for a screw, this work justifies insertion torque being considerably below the maximum. Cite this article: Bone Joint Res 2020;9(8):493–500

Aims. The number of convolutional neural networks (CNN) available for fracture detection and classification is rapidly increasing. External validation of a CNN on a temporally separate (separated by time) or geographically separate (separated by location) dataset is crucial to assess generalizability of the CNN before application to clinical practice in other institutions. We aimed to answer the following questions: are current CNNs for fracture recognition externally valid?; which methods are applied for external validation (EV)?; and, what are reported performances of the EV sets compared to the internal validation (IV) sets of these CNNs?. Methods. The PubMed and Embase databases were systematically searched from January 2010 to October 2020 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The type of EV, characteristics of the external dataset, and diagnostic performance characteristics on the IV and EV datasets were collected and compared. Quality assessment was conducted using a seven-item checklist based on a modified Methodologic Index for NOn-Randomized Studies instrument (MINORS). Results. Out of 1,349 studies, 36 reported development of a CNN for fracture detection and/or classification. Of these, only four (11%) reported a form of EV. One study used temporal EV, one conducted both temporal and geographical EV, and two used geographical EV. When comparing the CNN’s performance on the IV set versus the EV set, the following were found: AUCs of 0.967 (IV) versus 0.975 (EV), 0.976 (IV) versus 0.985 to 0.992 (EV), 0.93 to 0.96 (IV) versus 0.80 to 0.89 (EV), and F1-scores of 0.856 to 0.863 (IV) versus 0.757 to 0.840 (EV). Conclusion. The number of externally validated CNNs in orthopaedic trauma for fracture recognition is still scarce. This greatly limits the potential for transfer of these CNNs from the developing institute to another hospital to achieve similar diagnostic performance. We recommend the use of geographical EV and statements such as the Consolidated Standards of Reporting Trials–Artificial Intelligence (CONSORT-AI), the Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence (SPIRIT-AI) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis–Machine Learning (TRIPOD-ML) to critically appraise performance of CNNs and improve methodological rigor, quality of future models, and facilitate eventual implementation in clinical practice. Cite this article: Bone Jt Open 2021;2(10):879–885

Aims. The use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments. Methods. Based on a clinical series of 479 primary total knees and 1,305 associated surgical decisions, various ML models were developed. These models identified the indicated surgical decision based on available, intra-operative alignment, and tibiofemoral load data. Results. With an associated area under the receiver-operator curve ranging between 0.75 and 0.98, the optimized ML models resulted in good to excellent predictions. The best performing model used a random forest approach while considering both alignment and intra-articular load readings. Conclusion. The presented model has the potential to make experience available to surgeons adopting new technology, bringing expert opinion in their operating theatre, but also provides insight in the surgical decision process. More specifically, these promising outcomes indicated the relevance of considering the overall limb alignment in the coronal and sagittal plane to identify the appropriate surgical decision

Aims. In computer simulations, the shape of the range of motion (ROM) of a stem with a cylindrical neck design will be a perfect cone. However, many modern stems have rectangular/oval-shaped necks. We hypothesized that the rectangular/oval stem neck will affect the shape of the ROM and the prosthetic impingement. Methods. Total hip arthroplasty (THA) motion while standing and sitting was simulated using a MATLAB model (one stem with a cylindrical neck and one stem with a rectangular neck). The primary predictor was the geometry of the neck (cylindrical vs rectangular) and the main outcome was the shape of ROM based on the prosthetic impingement between the neck and the liner. The secondary outcome was the difference in the ROM provided by each neck geometry and the effect of the pelvic tilt on this ROM. Multiple regression was used to analyze the data. Results. The stem with a rectangular neck has increased internal and external rotation with a quatrefoil cross-section compared to a cone in a cylindrical neck. Modification of the cup orientation and pelvic tilt affected the direction of projection of the cone or quatrefoil shape. The mean increase in internal rotation with a rectangular neck was 3.4° (0° to 7.9°; p < 0.001); for external rotation, it was 2.8° (0.5° to 7.8°; p < 0.001). Conclusion. Our study shows the importance of attention to femoral implant design for the assessment of prosthetic impingement. Any universal mathematical model or computer simulation that ignores each stem’s unique neck geometry will provide inaccurate predictions of prosthetic impingement. Cite this article: Bone Joint Res 2021;10(12):780–789

Aims. A comprehensive classification for coronal lower limb alignment with predictive capabilities for knee balance would be beneficial in total knee arthroplasty (TKA). This paper describes the Coronal Plane Alignment of the Knee (CPAK) classification and examines its utility in preoperative soft tissue balance prediction, comparing kinematic alignment (KA) to mechanical alignment (MA). Methods. A radiological analysis of 500 healthy and 500 osteoarthritic (OA) knees was used to assess the applicability of the CPAK classification. CPAK comprises nine phenotypes based on the arithmetic HKA (aHKA) that estimates constitutional limb alignment and joint line obliquity (JLO). Intraoperative balance was compared within each phenotype in a cohort of 138 computer-assisted TKAs randomized to KA or MA. Primary outcomes included descriptive analyses of healthy and OA groups per CPAK type, and comparison of balance at 10° of flexion within each type. Secondary outcomes assessed balance at 45° and 90° and bone recuts required to achieve final knee balance within each CPAK type. Results. There was similar frequency distribution between healthy and arthritic groups across all CPAK types. The most common categories were Type II (39.2% healthy vs 32.2% OA), Type I (26.4% healthy vs 19.4% OA) and Type V (15.4% healthy vs 14.6% OA). CPAK Types VII, VIII, and IX were rare in both populations. Across all CPAK types, a greater proportion of KA TKAs achieved optimal balance compared to MA. This effect was largest, and statistically significant, in CPAK Types I (100% KA vs 15% MA; p < 0.001), Type II (78% KA vs 46% MA; p = 0.018). and Type IV (89% KA vs 0% MA; p < 0.001). Conclusion. CPAK is a pragmatic, comprehensive classification for coronal knee alignment, based on constitutional alignment and JLO, that can be used in healthy and arthritic knees. CPAK identifies which knee phenotypes may benefit most from KA when optimization of soft tissue balance is prioritized. Further, it will allow for consistency of reporting in future studies. Cite this article: Bone Joint J 2021;103-B(2):329–337

Aims. An algorithm to determine the constitutional alignment of the lower limb once arthritic deformity has occurred would be of value when undertaking kinematically aligned total knee arthroplasty (TKA). The purpose of this study was to determine if the arithmetic hip-knee-ankle angle (aHKA) algorithm could estimate the constitutional alignment of the lower limb following development of significant arthritis. Methods. A matched-pairs radiological study was undertaken comparing the aHKA of an osteoarthritic knee (aHKA-OA) with the mechanical HKA of the contralateral normal knee (mHKA-N). Patients with Grade 3 or 4 Kellgren-Lawrence tibiofemoral osteoarthritis in an arthritic knee undergoing TKA and Grade 0 or 1 osteoarthritis in the contralateral normal knee were included. The aHKA algorithm subtracts the lateral distal femoral angle (LDFA) from the medial proximal tibial angle (MPTA) measured on standing long leg radiographs. The primary outcome was the mean of the paired differences in the aHKA-OA and mHKA-N. Secondary outcomes included comparison of sex-based differences and capacity of the aHKA to determine the constitutional alignment based on degree of deformity. Results. A total of 51 radiographs met the inclusion criteria. There was no significant difference between aHKA-OA and mHKA-N, with a mean angular difference of −0.4° (95% SE −0.8° to 0.1°; p = 0.16). There was no significant sex-based difference when comparing aHKA-OA and mHKA-N (mean difference 0.8°; p = 0.11). Knees with deformities of more than 8° had a greater mean difference between aHKA-OA and mHKA-N (1.3°) than those with lesser deformities (-0.1°; p = 0.009). Conclusion. This study supports the arithmetic HKA algorithm for prediction of the constitutional alignment once arthritis has developed. The algorithm has similar accuracy between sexes and greater accuracy with lesser degrees of deformity. Cite this article: Bone Joint Open 2020;1-7:339–345

Objectives. The aim of this study was to provide a comprehensive understanding of alterations in messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs) in cartilage affected by osteoarthritis (OA). Methods. The expression profiles of mRNAs, lncRNAs, and circRNAs in OA cartilage were assessed using whole-transcriptome sequencing. Bioinformatics analyses included prediction and reannotation of novel lncRNAs and circRNAs, their classification, and their placement into subgroups. Gene ontology and pathway analysis were performed to identify differentially expressed genes (DEGs), differentially expressed lncRNAs (DELs), and differentially expressed circRNAs (DECs). We focused on the overlap of DEGs and targets of DELs previously identified in seven high-throughput studies. The top ten DELs were verified by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) in articular chondrocytes, both in vitro and in vivo. Results. In total, 739 mRNAs, 1152 lncRNAs, and 42 circRNAs were found to be differentially expressed in OA cartilage tissue. Among these, we identified 18 overlapping DEGs and targets of DELs, and the top ten DELs were screened by expression profile analysis as candidate OA-related genes. WISP2, ATF3, and CHI3L1 were significantly increased in both normal versus OA tissues and normal versus interleukin (IL)-1β-induced OA-like cell models, while ADAM12, PRELP, and ASPN were shown to be significantly decreased. Among the identified DELs, we observed higher expression of ENST00000453554 and MSTRG.99593.3, and lower expression of MSTRG.44186.2 and NONHSAT186094.1 in normal versus OA cells and tissues. Conclusion. This study revealed expression patterns of coding and noncoding RNAs in OA cartilage, which added sets of genes and noncoding RNAs to the list of candidate diagnostic biomarkers and therapeutic agents for OA patients. Cite this article: H. Li, H. H. Yang, Z. G. Sun, H. B. Tang, J. K. Min. Whole-transcriptome sequencing of knee joint cartilage from osteoarthritis patients. Bone Joint Res 2019;8:290–303. DOI: 10.1302/2046-3758.87.BJR-2018-0297.R1

Objectives. Unicompartmental knee arthroplasty (UKA) is an alternative to total knee arthroplasty for patients who require treatment of single-compartment osteoarthritis, especially for young patients. To satisfy this requirement, new patient-specific prosthetic designs have been introduced. The patient-specific UKA is designed on the basis of data from preoperative medical images. In general, knee implant design with increased conformity has been developed to provide lower contact stress and reduced wear on the tibial insert compared with flat knee designs. The different tibiofemoral conformity may provide designers the opportunity to address both wear and kinematic design goals simultaneously. The aim of this study was to evaluate wear prediction with respect to tibiofemoral conformity design in patient-specific UKA under gait loading conditions by using a previously validated computational wear method. Methods. Three designs with different conformities were developed with the same femoral component: a flat design normally used in fixed-bearing UKA, a tibia plateau anatomy mimetic (AM) design, and an increased conforming design. We investigated the kinematics, contact stress, contact area, wear rate, and volumetric wear of the three different tibial insert designs. Results. Conforming increased design showed a lower contact stress and increased contact area. In addition, increased conformity resulted in a reduction of the wear rate and volumetric wear. However, the increased conformity design showed limited kinematics. Conclusion. Our results indicated that increased conformity provided improvements in wear but resulted in limited kinematics. Therefore, increased conformity should be avoided in fixed-bearing patient-specific UKA design. We recommend a flat or plateau AM tibial insert design in patient-specific UKA. Cite this article: Y-G. Koh, K-M. Park, H-Y. Lee, K-T. Kang. Influence of tibiofemoral congruency design on the wear of patient-specific unicompartmental knee arthroplasty using finite element analysis. Bone Joint Res 2019;8:156–164. DOI: 10.1302/2046-3758.83.BJR-2018-0193.R1

Objectives. Opening wedge high tibial osteotomy (HTO) is an established surgical procedure for the treatment of early-stage knee arthritis. Other than infection, the majority of complications are related to mechanical factors – in particular, stimulation of healing at the osteotomy site. This study used finite element (FE) analysis to investigate the effect of plate design and bridging span on interfragmentary movement (IFM) and the influence of fracture healing on plate stress and potential failure. Materials and Methods. A 10° opening wedge HTO was created in a composite tibia. Imaging and strain gauge data were used to create and validate FE models. Models of an intact tibia and a tibia implanted with a custom HTO plate using two different bridging spans were validated against experimental data. Physiological muscle forces and different stages of osteotomy gap healing simulating up to six weeks postoperatively were then incorporated. Predictions of plate stress and IFM for the custom plate were compared against predictions for an industry standard plate (TomoFix). Results. For both plate types, long spans increased IFM but did not substantially alter peak plate stress. The custom plate increased axial and shear IFM values by up to 24% and 47%, respectively, compared with the TomoFix. In all cases, a callus stiffness of 528 MPa was required to reduce plate stress below the fatigue strength of titanium alloy. Conclusion. We demonstrate that larger bridging spans in opening wedge HTO increase IFM without substantially increasing plate stress. The results indicate, however, that callus healing is required to prevent fatigue failure. Cite this article: A. R. MacLeod, G. Serrancoli, B. J. Fregly, A. D. Toms, H. S. Gill. The effect of plate design, bridging span, and fracture healing on the performance of high tibial osteotomy plates: An experimental and finite element study. Bone Joint Res 2018;7:639–649. DOI: 10.1302/2046-3758.712.BJR-2018-0035.R1

Objectives. The aim of this study was to review the current evidence and future application for the role of diagnostic and therapeutic ultrasound in fracture management. Methods. A review of relevant literature was undertaken, including articles indexed in PubMed with keywords “ultrasound” or “sonography” combined with “diagnosis”, “fracture healing”, “impaired fracture healing”, “nonunion”, “microbiology”, and “fracture-related infection”. Results. The use of ultrasound in musculoskeletal medicine has expanded rapidly over the last two decades, but the diagnostic use in fracture management is not routinely practised. Early studies have shown the potential of ultrasound as a valid alternative to radiographs to diagnose common paediatric fractures, to detect occult injuries in adults, and for rapid detection of long bone fractures in the resuscitation setting. Ultrasound has also been shown to be advantageous in the early identification of impaired fracture healing; with the advent of 3D image processing, there is potential for wider adoption. Detection of implant-related infection can be improved by ultrasound mediated sonication of microbiology samples. The use of therapeutic ultrasound to promote union in the management of acute fractures is currently a controversial topic. However, there is strong in vitro evidence that ultrasound can stimulate a biological effect with potential clinical benefit in established nonunions, which supports the need for further investigation. Conclusion. Modern ultrasound image processing has the potential to replace traditional imaging modalities in several areas of trauma practice, particularly in the early prediction of impaired fracture healing. Further understanding of the therapeutic application of ultrasound is required to understand and identify the use in promoting fracture healing. Cite this article: J. A. Nicholson, S. T. J. Tsang, T. J. MacGillivray, F. Perks, A. H. R. W. Simpson. What is the role of ultrasound in fracture management? Diagnosis and therapeutic potential for fractures, delayed unions, and fracture-related infection. Bone Joint Res 2019;8:304–312. DOI: 10.1302/2046-3758.87.BJR-2018-0215.R2

Aims

Disorders of bone integrity carry a high global disease burden, frequently requiring intervention, but there is a paucity of methods capable of noninvasive real-time assessment. Here we show that miniaturized handheld near-infrared spectroscopy (NIRS) scans, operated via a smartphone, can assess structural human bone properties in under three seconds.

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An objective technological solution for tracking adherence to at-home shoulder physiotherapy is important for improving patient engagement and rehabilitation outcomes, but remains a significant challenge. The aim of this research was to evaluate performance of machine-learning (ML) methodologies for detecting and classifying inertial data collected during in-clinic and at-home shoulder physiotherapy exercise.

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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.

Objectives. Prosthetic joint infection (PJI) diagnosis is a major challenge in orthopaedics, and no reliable parameters have been established for accurate, preoperative predictions in the differential diagnosis of aseptic loosening or PJI. This study surveyed factors in synovial fluid (SF) for improving PJI diagnosis. Methods. We enrolled 48 patients (including 39 PJI and nine aseptic loosening cases) who required knee/hip revision surgery between January 2016 and December 2017. The PJI diagnosis was established according to the Musculoskeletal Infection Society (MSIS) criteria. SF was used to survey factors by protein array and enzyme-linked immunosorbent assay to compare protein expression patterns in SF among three groups (aseptic loosening and first- and second-stage surgery). We compared routine clinical test data, such as C-reactive protein level and leucocyte number, with potential biomarker data to assess the diagnostic ability for PJI within the same patient groups. Results. Cut-off values of 1473 pg/ml, 359 pg/ml, and 8.45 pg/ml were established for interleukin (IL)-16, IL-18, and cysteine-rich with EGF-like domains 2 (CRELD2), respectively. Receiver operating characteristic curve analysis showed that these factors exhibited an accuracy of 1 as predictors of PJI. These factors represent potential biomarkers for decisions associated with prosthesis reimplantation based on their ability to return to baseline values following the completion of debridement. Conclusion. IL-16, IL-18, and CRELD2 were found to be potential biomarkers for PJI diagnosis, with SF tests outperforming blood tests in accuracy. These factors could be useful for assessing successful debridement based on their ability to return to baseline values following the completion of debridement. Cite this article: M-F. Chen, C-H. Chang, L-Y. Yang, P-H. Hsieh, H-N. Shih, S. W. N. Ueng, Y. Chang. Synovial fluid interleukin-16, interleukin-18, and CRELD2 as novel biomarkers of prosthetic joint infections. Bone Joint Res 2019;8:179–188. DOI: 10.1302/2046-3758.84.BJR-2018-0291.R1

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Micromotion of the polyethylene (PE) inlay may contribute to backside PE wear in addition to articulate wear of total knee arthroplasty (TKA). Using radiostereometric analysis (RSA) with tantalum beads in the PE inlay, we evaluated PE micromotion and its relationship to PE wear.

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.

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The aim of this study was to create artificial intelligence (AI) software with the purpose of providing a second opinion to physicians to support distal radius fracture (DRF) detection, and to compare the accuracy of fracture detection of physicians with and without software support.

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Distal femoral resection in conventional total knee arthroplasty (TKA) utilizes an intramedullary guide to determine coronal alignment, commonly planned for 5° of valgus. However, a standard 5° resection angle may contribute to malalignment in patients with variability in the femoral anatomical and mechanical axis angle. The purpose of the study was to leverage deep learning (DL) to measure the femoral mechanical-anatomical axis angle (FMAA) in a heterogeneous cohort.

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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.

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Accurate identification of the ankle joint centre is critical for estimating tibial coronal alignment in total knee arthroplasty (TKA). The purpose of the current study was to leverage artificial intelligence (AI) to determine the accuracy and effect of using different radiological anatomical landmarks to quantify mechanical alignment in relation to a traditionally defined radiological ankle centre.

Aims

The surgical target for optimal implant positioning in robotic-assisted total knee arthroplasty remains the subject of ongoing discussion. One of the proposed targets is to recreate the knee’s functional behaviour as per its pre-diseased state. The aim of this study was to optimize implant positioning, starting from mechanical alignment (MA), toward restoring the pre-diseased status, including ligament strain and kinematic patterns, in a patient population.

Objectives. Researchers continue to seek easier ways to evaluate the quality of bone and screen for osteoporosis and osteopenia. Until recently, radiographic images of various parts of the body, except the distal femur, have been reappraised in the light of dual-energy X-ray absorptiometry (DXA) findings. The incidence of osteoporotic fractures around the knee joint in the elderly continues to increase. The aim of this study was to propose two new radiographic parameters of the distal femur for the assessment of bone quality. Methods. Anteroposterior radiographs of the knee and bone mineral density (BMD) and T-scores from DXA scans of 361 healthy patients were prospectively analyzed. The mean cortical bone thickness (CBTavg) and the distal femoral cortex index (DFCI) were the two parameters that were proposed and measured. Intra- and interobserver reliabilities were assessed. Correlations between the BMD and T-score and these parameters were investigated and their value in the diagnosis of osteoporosis and osteopenia was evaluated. Results. The DFCI, as a ratio, had higher reliability than the CBTavg. Both showed significant correlation with BMD and T-score. When compared with DFCI, CBTavg showed better correlation and was better for predicting osteoporosis and osteopenia. Conclusion. The CBTavg and DFCI are simple and reliable screening tools for the prediction of osteoporosis and osteopenia. The CBTavg is more accurate but the DFCI is easier to use in clinical practice. Cite this article: Q-F. He, H. Sun, L-Y. Shu, Y. Zhu, X-T. Xie, Y. Zhan, C-F. Luo. Radiographic predictors for bone mineral loss: Cortical thickness and index of the distal femur. Bone Joint Res 2018;7:468–475. DOI: 10.1302/2046-3758.77.BJR-2017-0332.R1

Aims

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.