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. Restoring the pre-morbid anatomy of the proximal humerus is a goal of anatomical shoulder arthroplasty, but reliance is placed on the surgeon’s experience and on anatomical estimations. The purpose of this study was to present a novel method, ‘Statistical Shape Modelling’, which accurately predicts the pre-morbid proximal humeral anatomy and calculates the 3D geometric parameters needed to restore normal anatomy in patients with severe degenerative osteoarthritis or a fracture of the proximal humerus. Materials and Methods. From a database of 57 humeral CT scans 3D humeral reconstructions were manually created. The reconstructions were used to construct a statistical shape model (SSM), which was then tested on a second set of 52 scans. For each humerus in the second set, 3D reconstructions of four diaphyseal segments of varying lengths were created. These reconstructions were chosen to mimic severe osteoarthritis, a fracture of the surgical neck of the humerus and a proximal humeral fracture with diaphyseal extension. The SSM was then applied to the diaphyseal segments to see how well it predicted proximal morphology, using the actual proximal humeral morphology for comparison. Results. With the metaphysis included, mimicking osteoarthritis, the errors of prediction for retroversion, inclination, height, radius of curvature and posterior and medial offset of the head of the humerus were 2.9° (± 2.3°), 4.0° (± 3.3°), 1.0 mm (± 0.8 mm), 0.8 mm (± 0.6 mm), 0.7 mm (± 0.5 mm) and 1.0 mm (± 0.7 mm), respectively. With the metaphysis excluded, mimicking a fracture of the surgical neck, the errors of prediction for retroversion, inclination, height, radius of curvature and posterior and medial offset of the head of the humerus were 3.8° (± 2.9°), 3.9° (± 3.4°), 2.4 mm (± 1.9 mm), 1.3 mm (± 0.9 mm), 0.8 mm (± 0.5 mm) and 0.9 mm (± 0.6 mm), respectively. Conclusion. This study reports a novel, computerised method that accurately predicts the pre-morbid proximal humeral anatomy even in challenging situations. This information can be used in the surgical planning and operative reconstruction of patients with severe degenerative osteoarthritis or with a fracture of the proximal humerus. Cite this article: Bone Joint J 2017;99-B:927–33

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 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. Methods. This retrospective cohort study included 469 patients, who underwent surgery for BM-E between January 2009 and March 2022 at a tertiary hospital in South Korea. Postoperative survival was calculated using the PATHFx3.0, SPRING13, OPTIModel, SORG, and IOR models. Model performance was assessed with area under the curve (AUC), calibration curve, Brier score, and decision curve analysis. Cox regression analyses were performed to evaluate the factors contributing to survival. Results. The SORG model demonstrated the highest discriminatory accuracy with AUC (0.80 (95% confidence interval (CI) 0.76 to 0.85)) at 12 months. In calibration analysis, the PATHfx3.0 and OPTIModel models underestimated survival, while the SPRING13 and IOR models overestimated survival. The SORG model exhibited excellent calibration with intercepts of 0.10 (95% CI -0.13 to 0.33) at 12 months. The SORG model also had lower Brier scores than the null score at three and 12 months, indicating good overall performance. Decision curve analysis showed that all five survival prediction models provided greater net benefit than the default strategy of operating on either all or no patients. Rapid growth cancer and low serum albumin levels were associated with three-, six-, and 12-month survival. Conclusion. State-of-art survival prediction models for BM-E (PATHFx3.0, SPRING13, OPTIModel, SORG, and IOR models) are useful clinical tools for orthopaedic surgeons in the decision-making process for the treatment in Asian patients, with SORG models offering the best predictive performance. Rapid growth cancer and serum albumin level are independent, statistically significant factors contributing to survival following surgery of BM-E. Further refinement of survival prediction models will bring about informed and patient-specific treatment of BM-E. Cite this article: Bone Joint J 2024;106-B(2):203–211

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

Abstract. Introduction. Precision health aims to develop personalised and proactive strategies for predicting, preventing, and treating complex diseases such as osteoarthritis (OA), a degenerative joint disease affecting over 300 million people worldwide. Due to OA heterogeneity, which makes developing effective treatments challenging, identifying patients at risk for accelerated disease progression is essential for efficient clinical trial design and new treatment target discovery and development. Objectives. This study aims to create a trustworthy and interpretable precision health tool that predicts rapid knee OA progression based on baseline patient characteristics using an advanced automated machine learning (autoML) framework, “Autoprognosis 2.0”. Methods. All available 2-year follow-up periods of 600 patients from the FNIH OA Biomarker Consortium were analysed using “Autoprognosis 2.0” in two separate approaches, with distinct definitions of clinical outcomes: multi-class predictions (categorising patients into non-progressors, pain-only progressors, radiographic-only progressors, and both pain and radiographic progressors) and binary predictions (categorising patients into non-progressors and progressors). Models were developed using a training set of 1352 instances and all available variables (including clinical, X-ray, MRI, and biochemical features), and validated through both stratified 10-fold cross-validation and hold-out validation on a testing set of 339 instances. Model performance was assessed using multiple evaluation metrics, such as AUC-ROC, AUC-PRC, F1-score, precision, and recall. Additionally, interpretability analyses were carried out to identify important predictors of rapid disease progression. Results. Our final models yielded high accuracy scores for both multi-class predictions (AUC-ROC: 0.858, 95% CI: 0.856–0.860; AUC-PRC: 0.675, 95% CI: 0.671–0.679; F1-score: 0.560, 95% CI: 0.554–0.566) and binary predictions (AUC-ROC: 0.717, 95% CI: 0.712–0.722; AUC-PRC: 0.620, 95% CI: 0.616–0.624; F1-score: 0.676, 95% CI: 0.673–0679). Important predictors of rapid disease progression included the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores and MRI features. Our models were further successfully validated using a hold-out dataset, which was previously omitted from model development and training (AUC-ROC: 0.877 for multi-class predictions; AUC-ROC: 0.746 for binary predictions). Additionally, accurate ML models were developed for predicting OA progression in a subgroup of patients aged 65 or younger (AUC-ROC: 0.862, 95% CI: 0.861–0.863 for multi-class predictions; AUC-ROC: 0.736, 95% CI: 0.734–0.738 for binary predictions). Conclusions. This study presents a reliable and interpretable precision health tool for predicting rapid knee OA progression using “Autoprognosis 2.0”. Our models provide accurate predictions and offer insights into important predictors of rapid disease progression. Furthermore, the transparency and interpretability of our methods may facilitate their acceptance by clinicians and patients, enabling effective utilisation in clinical practice. Future work should focus on refining these models by increasing the sample size, integrating additional features, and using independent datasets for external validation. Declaration of Interest. (b) declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported:I declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research project

Precision health aims to develop personalised and proactive strategies for predicting, preventing, and treating complex diseases such as osteoarthritis (OA). Due to OA heterogeneity, which makes developing effective treatments challenging, identifying patients at risk for accelerated disease progression is essential for efficient clinical trial design and new treatment target discovery and development. To create a reliable and interpretable precision health tool that predicts rapid knee OA progression over a 2-year period from baseline patient characteristics using an advanced automated machine learning (autoML) framework, “Autoprognosis 2.0”. All available 2-year follow-up periods of 600 patients from the FNIH OA Biomarker Consortium were analysed using “Autoprognosis 2.0” in two separate approaches, with distinct definitions of clinical outcomes: multi-class predictions (categorising disease progression into pain and/or radiographic progression) and binary predictions. Models were developed using a training set of 1352 instances and all available variables (including clinical, X-ray, MRI, and biochemical features), and validated through both stratified 10-fold cross-validation and hold-out validation on a testing set of 339 instances. Model performance was assessed using multiple evaluation metrics. Interpretability analyses were carried out to identify important predictors of progression. Our final models yielded higher accuracy scores for multi-class predictions (AUC-ROC: 0.858, 95% CI: 0.856-0.860) compared to binary predictions (AUC-ROC: 0.717, 95% CI: 0.712-0.722). Important predictors of rapid disease progression included WOMAC scores and MRI features. Additionally, accurate ML models were developed for predicting OA progression in a subgroup of patients aged 65 or younger. This study presents a reliable and interpretable precision health tool for predicting rapid knee OA progression. Our models provide accurate predictions and, importantly, allow specific predictors of rapid disease progression to be identified. Furthermore, the transparency and explainability of our methods may facilitate their acceptance by clinicians and patients, enabling effective translation to clinical practice

Total knee and hip arthroplasty (TKA and THA) are the most commonly performed surgical procedures, the costs of which constitute a significant healthcare burden. Improving access to care for THA/TKA requires better efficiency. It is hypothesized that this may be possible through a two-stage approach that utilizes prediction of surgical time to enable optimization of operating room (OR) schedules. Data from 499,432 elective unilateral arthroplasty procedures, including 302,490 TKAs, and 196,942 THAs, performed from 2014-2019 was extracted from the American College of Surgeons (ACS) National Surgical and Quality Improvement (NSQIP) database. A deep multilayer perceptron model was trained to predict duration of surgery (DOS) based on pre-operative clinical and biochemical patient factors. A two-stage approach, utilizing predicted DOS from a held out “test” dataset, was utilized to inform the daily OR schedule. The objective function of the optimization was the total OR utilization, with a penalty for overtime. The scheduling problem and constraints were simulated based on a high-volume elective arthroplasty centre in Canada. This approach was compared to current patient scheduling based on mean procedure DOS. Approaches were compared by performing 1000 simulated OR schedules. The predict then optimize approach achieved an 18% increase in OR utilization over the mean regressor. The two-stage approach reduced overtime by 25-minutes per OR day, however it created a 7-minute increase in underutilization. Better objective value was seen in 85.1% of the simulations. With deep learning prediction and mathematical optimization of patient scheduling it is possible to improve overall OR utilization compared to typical scheduling practices. Maximizing utilization of existing healthcare resources can, in limited resource environments, improve patient's access to arthritis care by increasing patient throughput, reducing surgical wait times and in the immediate future, help clear the backlog associated with the COVID-19 pandemic

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. Methods. Data were prospectively collected on all patients who underwent total or unicompartmental knee arthroplasty at a between July 2015 and June 2018. Cohort 1 (n = 5,958) was utilized to develop models for LOS and 90 day readmission. Cohort 2 (n = 2,391, surgery date 2015 to 2017) was utilized to develop models for one-year improvements in Knee Injury and Osteoarthritis Outcome Score (KOOS) pain score, KOOS function score, and KOOS quality of life (QOL) score. Model accuracies within the imputed data set were assessed through cross-validation with root mean square errors (RMSEs) and mean absolute errors (MAEs) for the LOS and PROMs models, and the index of prediction accuracy (IPA), and area under the curve (AUC) for the readmission models. Model accuracies in new patient data sets were assessed with AUC. Results. Within the imputed datasets, the LOS (RMSE 1.161) and PROMs models (RMSE 15.775, 11.056, 21.680 for KOOS pain, function, and QOL, respectively) demonstrated good accuracy. For all models, the accuracy of predicting outcomes in a new set of patients were consistent with the cross-validation accuracy overall. Upon validation with a new patient dataset, the LOS and readmission models demonstrated high accuracy (71.5% and 65.0%, respectively). Similarly, the one-year PROMs improvement models demonstrated high accuracy in predicting ten-point improvements in KOOS pain (72.1%), function (72.9%), and QOL (70.8%) scores. Conclusion. The data-driven models developed in this study offer scalable predictive tools that can accurately estimate the likelihood of improved pain, function, and quality of life one year after knee arthroplasty as well as LOS and 90 day readmission. Cite this article: Bone Joint J 2020;102-B(9):1183–1193

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

Numerous prediction tools are available for estimating postoperative risk following spine surgery. External validation studies have shown mixed results. We present the development, validation, and comparative evaluation of novel tool (NZSpine) for modelling risk of complications within 30 days of spine surgery. Data was gathered retrospectively from medical records of patients who underwent spine surgery at Waikato Hospital between January 2019 and December 2020 (n = 488). Variables were selected a priori based on previous evidence and clinical judgement. Postoperative adverse events were classified objectively using the Comprehensive Complication Index. Models were constructed for the occurrence of any complication and significant complications (based on CCI >26). Performance and clinical utility of the novel model was compared against SpineSage (. https://depts.washington.edu/spinersk/. ), an extant online tool which we have shown in unpublished work to be valid in our local population. Overall complication rate was 34%. In the multivariate model, higher age, increased surgical invasiveness and the presence of preoperative anemia were most strongly predictive of any postoperative complication (OR = 1.03, 1.09, 2.1 respectively, p <0.001), whereas the occurrence of a major postoperative complication (CCI >26) was most strongly associated with the presence of respiratory disease (OR = 2.82, p <0.001). Internal validation using the bootstrapped models showed the model was robust, with an AUC of 0.73. Using sensitivity analysis, 80% of the model's predictions were correct. By comparison SpineSage had an AUC of 0.71, and in decision curve analysis the novel model showed greater expected benefit at all thresholds of risk. NZSpine is a novel risk assessment tool for patients undergoing acute and elective spine surgery and may help inform clinicians and patients of their prognosis. Use of an objective tool may help to provide uniformity between DHBs when completing the “clinician assessment of risk” section of the national prioritization tool

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

Background. Although there are predictive equations that estimate the total fat mass obtained from multiple-site ultrasound (US) measurements, the predictive equation of total fat mass has not been investigated solely from abdominal subcutaneous fat thickness. Therefore, the aims of this study were; (1) to develop regression-based prediction equations based on abdominal subcutaneous fat thickness for predicting fat mass in young- and middle-aged adults, and (2) to investigate the validity of these equations to be developed. Methods. The study was approved by the Local Research Ethics Committee (Decision number: GO 19/788). Twenty-seven males (30.3 ± 8.7 years) and eighteen females (32.4 ± 9.5 years) were randomly divided into two groups as the model prediction group (19 males and 12 females) and the validation group (8 males and 6 females). Total body fat mass was determined by dual-energy X-ray absorptiometry (DXA). Abdominal subcutaneous fat thickness was measured by US. The predictive equations for total fat mass from US were determined as fat thickness (in mm) × standing height (in m). Statistical analyses were performed using R version 4.0.0. The association between the total fat mass and the abdominal subcutaneous fat thickness was interpreted using the Pearson test. The linear regression analysis was used to predict equations for total body fat mass from the abdominal subcutaneous fat thickness acquired by US. Then these predictive equations were applied to the validation group. The paired t-test was used to examine the difference between the measured and the predicted fat masses, and Lin's concordance correlation coefficient (CCC) was used as a further measure of agreement. Results. There was a significant positive moderate correlation between the total fat mass and the abdominal subcutaneous fat thickness × height in the model prediction group of males (r = 0.588, p = 0.008), whereas significant positive very strong correlation was observed in the model prediction group of females (r = 0.896, p < 0.001). Predictive equations for DXA-measured total body fat mass from abdominal subcutaneous fat thickness using US were as follows: for males “Fat mass-DXA = 0.276 × (Fat thickness-US × Height) + 17.221” (R. 2. = 0.35, SEE = 3.6, p = 0.008); for females “Fat mass-DXA = 0.694 x (Fat thickness-US × Height) + 7.085” (R. 2. = 0.80, SEE = 2.8, p < 0.001). When fat mass prediction equations were applied to the validation groups, measured- and estimated-total fat masses in males and females were found similar (p = 0.9, p = 0.5, respectively). A good level of agreement between measurements in males and females was attained (CCC = 0.84, CCC = 0.76, respectively). Conclusion. We developed and validated prediction equations that are convenient for determining total fat masses in young- and middle-aged adults using abdominal subcutaneous fat thickness obtained from the US. The abdominal subcutaneous fat thickness obtained from a single region by US might provide a noninvasive quick and easy evaluation not only in clinical settings but also on the field

Abstract. Introduction. Minimising postoperative complications and mortality in COVID-19 patients who were undergoing trauma and orthopaedic surgeries is an international priority. Aim was to develop a predictive nomogram for 30-day morbidity/mortality of COVID-19 infection in patients who underwent orthopaedic and trauma surgery during the coronavirus pandemic in the UK in 2020 compared to a similar period in 2019. Secondary objective was to compare between patients with positive PCR test and those with negative test. Methods. Retrospective multi-center study including 50 hospitals. Patients with suspicion of SARS-CoV-2 infection who had underwent orthopaedic or trauma surgery for any indication during the 2020 pandemic were enrolled in the study (2525 patients). We analysed cases performed on orthopaedic and trauma operative lists in 2019 for comparison (4417). Multivariable Logistic Regression analysis was performed to assess the possible predictors of a fatal outcome. A nomogram was developed with the possible predictors and total point were calculated. Results. Of the 2525 patients admitted for suspicion of COVID-19, 658 patients had negative preoperative test, 151 with positive test and 1716 with unknown preoperative COVID-19 status. Preoperative COVID-19 status, sex, ASA grade, urgency and indication of surgery, use of torniquet, grade of operating surgeon and some comorbidities were independent risk factors associated with 30-day complications/mortality. The 2020 nomogram model exhibited moderate prediction ability. In contrast, the prediction ability of total points of 2019 nomogram model was excellent. Conclusions. Nomograms can be used by orthopaedic and trauma surgeons as a practical and effective tool in postoperative complications and mortality risk estimation

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

Total knee arthroplasty (TKA) is the most commonly performed elective orthopaedic procedure. With an increasingly aging population, the number of TKAs performed is expected to be ∼2,900 per 100,000 by 2050. Surgical Site Infections (SSI) after TKA can have significant morbidity and mortality. The purpose of this study was to construct a risk prediction model for acute SSI (classified as either superficial, deep and overall) within 30 days of a TKA based on commonly ordered pre-operative blood markers and using audited administrative data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. All adult patients undergoing an elective unilateral TKA for osteoarthritis from 2011–2016 were identified from the NSQIP database using Current Procedural Terminology (CPT) codes. Patients with active or chronic, local or systemic infection/sepsis or disseminated cancer were excluded. Multivariate logistic regression was conducted to estimate coefficients, with manual stepwise reduction to construct models. Bootstrap estimation was administered to measure internal validity. The SSI prediction model included the following co-variates: body mass index (BMI) and sex, comorbidities such as congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), smoking, current/previous steroid use, as well as pre-operative blood markers, albumin, alkaline phosphatase, blood urea nitrogen (BUN), creatinine, hematocrit, international normalized ratio (INR), platelets, prothrombin time (PT), sodium and white blood cell (WBC) levels. To compare clinical models, areas under the receiver operating characteristic (ROC) curves and McFadden's R-squared values were reported. The total number of patients undergoing TKA were 210,524 with a median age of 67 years (mean age of 66.6 + 9.6 years) and the majority being females (61.9%, N=130,314). A total of 1,674 patients (0.8%) had a SSI within 30 days of the index TKA, of which N=546 patients (33.2%) had a deep SSI and N=1,128 patients (67.4%) had a superficial SSI. The annual incidence rate of overall SSI decreased from 1.60% in 2011 to 0.68% in 2016. The final risk prediction model for SSI contained, smoking (OR=1.69, 95% CI: 1.31 – 2.18), previous/current steroid use (OR=1.66, 95% CI: 1.23 – 2.23), as well as the pre-operative lab markers, albumin (OR=0.46, 95% CI: 0.37 – 0.56), blood urea nitrogen (BUN, OR=1.01, 95% CI: 1 – 1.02), international normalized ratio (INR, OR=1.22, 95% CI:1.05 – 1.41), and sodium levels (OR=0.94, 95% CI: 0.91 – 0.98;). Area under the ROC curve for the final model of overall SSI was 0.64. Models for deep and superficial SSI had ROC areas of 0.68 and 0.63, respectively. Albumin (OR=0.46, 95% CI: 0.37 – 0.56, OR=0.33, 95% CI: 0.27 – 0.40, OR=0.75, 95% CI: 0.59 – 0.95) and sodium levels (OR=0.94, 95% CI: 0.91 – 0.98, OR=0.96, 95% CI: 0.93 – 0.99, OR=0.97, 95% CI: 0.96 – 0.99) levels were consistently significant in all prediction models for superficial, deep and overall SSI, respectively. Overall, hypoalbuminemia and hyponatremia are both significant risk factors for superficial, deep and overall SSI. To our knowledge, this is the first prediction model for acute SSI post TKA whereby hyponatremia (and hypoalbuminemia) are predictive of SSI. This prediction model can help fill an important gap for predicting risk factors for SSI after TKA and can help physicians better optimize patients prior to TKA

Early and accurate prediction of hospital length-of-stay (LOS) in patients undergoing knee replacement is important for economic and operational reasons. Few studies have systematically developed a multivariable model to predict LOS. We performed a retrospective cohort study of 1609 patients aged ≥ 50 years who underwent elective, primary total or unicompartmental knee replacements. Pre-operative candidate predictors included patient demographics, knee function, self-reported measures, surgical factors and discharge plans. In order to develop the model, multivariable regression with bootstrap internal validation was used. The median LOS for the sample was four days (interquartile range 4 to 5). Statistically significant predictors of longer stay included older age, greater number of comorbidities, less knee flexion range of movement, frequent feelings of being down and depressed, greater walking aid support required, total (versus unicompartmental) knee replacement, bilateral surgery, low-volume surgeon, absence of carer at home, and expectation to receive step-down care. For ease of use, these ten variables were used to construct a nomogram-based prediction model which showed adequate predictive accuracy (optimism-corrected R. 2. = 0.32) and calibration. If externally validated, a prediction model using easily and routinely obtained pre-operative measures may be used to predict absolute LOS in patients following knee replacement and help to better manage these patients. . Cite this article: Bone Joint J 2013;95-B:1490–6

Introduction. Diaphyseal tibial fractures account for approximately 1.9% of adult fractures. Several studies demonstrate a high proportion of diaphyseal tibial fractures have ipsilateral occult posterior malleolus fractures, this ranges from 22–92.3%. Materials and Methods. A retrospective review of a prospectively collected database was performed at Liverpool University Hospitals NHS Foundation Trust between 1/1/2013 and 9/11/2020. The inclusion criteria were patients over 16, with a diaphyseal tibial fracture and who underwent a CT. The articular fracture extension was categorised into either posterior malleolar (PM) or other fracture. Results. 764 fractures were analysed, 300 had a CT. There were 127 intra-articular fractures. 83 (65.4%) cases were PM and 44 were other fractures. On univariate analysis for PM fractures, fibular spiral (p=.016) fractures, no fibular fracture(p=.003), lateral direction of the tibial fracture (p=.04), female gender (p=.002), AO 42B1 (p=.033) and an increasing angle of tibial fracture. On multivariate regression analysis a high angle of tibia fracture was significant. Other fracture extensions were associated with no fibular fracture (p=.002), medial direction of tibia fracture (p=.004), female gender (p=.000), and AO 42A1 (p=.004), 42A2 (p=.029), 42B3 (p=.035) and 42C2 (p=.032). On multivariate analysis, the lateral direction of tibia fracture, and AO classification 42A1 and 42A2 were significant. Conclusions. Articular extension happened in 42.3%. A number of factors were associated with the extension, however multivariate analysis did not create a suitable prediction model. Nevertheless, rotational tibia fractures with a high angle of fracture should have further investigation with a CT

Background. Dislocation is a common complication following total hip arthroplasty (THA), and accounts for a high percentage of subsequent revisions. The purpose of this study was to develop a convolutional neural network (CNN) model to identify patients at high risk for dislocation based on postoperative anteroposterior (AP) pelvis radiographs. Methods. We retrospectively evaluated radiographs for a cohort of 13,970 primary THAs with 374 dislocations over 5 years of follow-up. Overall, 1,490 radiographs from dislocated and 91,094 from non-dislocated THAs were included in the analysis. A CNN object detection model (YOLO-V3) was trained to crop the images by centering on the femoral head. A ResNet18 classifier was trained to predict subsequent hip dislocation from the cropped imaging. The ResNet18 classifier was initialized with ImageNet weights and trained using FastAI (V1.0) running on PyTorch. The training was run for 15 epochs using ten-fold cross validation, data oversampling and augmentation. Results. The hip dislocation prediction classifier achieved the following mean performance: accuracy= 49.5(±4.1)%, sensitivity= 89.0(±2.2)%, specificity= 48.8(±4.2)%, positive predictive value= 3.3(±0.3)%, negative predictive value= 99.5(±0.1)%, and area under the receiver operating characteristic curve= 76.7(±3.6)%. Saliency maps demonstrated that the model placed the greatest emphasis on the femoral head and acetabular component. Conclusions. Existing prediction methods fail to identify patients at high risk of dislocation following THA. Our prediction model has high sensitivity and negative predictive value. Therefore, it can be helpful in rapid assessment of risk for dislocation following THA. The model further suggests radiographic locations which may be important in understanding the etiology of prosthesis dislocation

Aims. We report the use of the distal radius and ulna (DRU) classification for the prediction of peak growth (PG) and growth cessation (GC) in 777 patients with idiopathic scoliosis. We compare this classification with other commonly used parameters of maturity. Patients and Methods. The following data were extracted from the patients’ records and radiographs: chronological age, body height (BH), arm span (AS), date of menarche, Risser sign, DRU grade and status of the phalangeal and metacarpal physes. The mean rates of growth were recorded according to each parameter of maturity. PG was defined as the summit of the curve and GC as the plateau in deceleration of growth. The rates of growth at PG and GC were used for analysis using receiver operating characteristic (ROC) curves to determine the strength and cutoff values of the parameters of growth. Results. The most specific grades for PG using the DRU classification were radial grade 6 and ulnar grade 5, and for GC were radial grade 9 and ulnar grade 7. The DRU classification spanned both PG and GC, enabling better prediction of these clinically relevant stages than other methods. The rate of PG (≥ 0.7 cm/month) and GC (≤ 0.15 cm/month) was the same for girls and boys, in BH and AS measurements. Conclusion. This is the first study to note that the DRU classification can predict both PG and GC, providing evidence that it may aid the management of patients with idiopathic scoliosis. Cite this article: Bone Joint J 2016;98-B:1689–96

Background. Postoperative recovery after routine total hip arthroplasty (THA) can lead to the development of prolonged opioid use but there are few tools for predicting this adverse outcome. The purpose of this study was to develop machine learning algorithms for preoperative prediction of prolonged post-operative opioid use after THA. Methods. A retrospective review of electronic health records was conducted at two academic medical centers and three community hospitals to identify adult patients who underwent THA for osteoarthritis between January 1. st. , 2000 and August 1. st. , 2018. Prolonged postoperative opioid prescriptions were defined as continuous opioid prescriptions after surgery to at least 90 days after surgery. Five machine learning algorithms were developed to predict this outcome and were assessed by discrimination, calibration, and decision curve analysis. Results. Overall, 5507 patients underwent THA, of which 345 (6.3%) had prolonged postoperative opioid prescriptions. The factors determined for prediction of prolonged postoperative opioid prescriptions were: age, duration of pre-operative opioid exposure, preoperative hemoglobin, and certain preoperative medications (anti-depressants, benzodiazepines, non-steroidal anti-inflammatory drugs, and beta-2-agonists). The elastic-net penalized logistic regression model achieved the best performance across discrimination (c-statistic = 0.77), calibration, and decision curve analysis. This model was incorporated into a digital application able to provide both predictions and explanations; available here: . https://sorg-apps.shinyapps.io/thaopioid/. Conclusion. If externally validated in independent populations, the algorithms developed in this study could improve preoperative screening and support for THA patients at high-risk for prolonged postoperative opioid use. Early identification and intervention in high-risk cases may mitigate the long-term adverse consequence of opioid dependence. For any tables or figures, please contact the authors directly

Although 3D-printed porous dental implants may possess improved osseointegration potential, they must exhibit appropriate fatigue strength. Finite element analysis (FEA) has the potential to predict the fatigue life of implants and accelerate their development. This work aimed at developing and validating an FEA-based tool to predict the fatigue behavior of porous dental implants. Test samples mimicking dental implants were designed as 4.5 mm-diameter cylinders with a fully porous section around bone level. Three porosity levels (50%, 60% and 70%) and two unit cell types (Schwarz Primitive (SP) and Schwarz W (SW)) were combined to generate six designs that were split between calibration (60SP, 70SP, 60SW, 70SW) and validation (50SP, 50SW) sets. Twenty-eight samples per design were additively manufactured from titanium powder (Ti6Al4V). The samples were tested under bending compression loading (ISO 14801) monotonically (N=4/design) to determine ultimate load (F. ult. ) (Instron 5866) and cyclically at six load levels between 50% and 10% of F. ult. (N=4/design/load level) (DYNA5dent). Failure force results were fitted to F/F. ult. = a(N. f. ). b. (Eq1) with N. f. being the number of cycles to failure, to identify parameters a and b. The endurance limit (F. e. ) was evaluated at N. f. = 5M cycles. Finite element models were built to predict the yield load (F. yield. ) of each design. Combining a linear correlation between FEA-based F. yield. and experimental F. ult. with equation Eq1 enabled FEA-based prediction of F. e. . For all designs, F. e. was comprised between 10% (all four samples surviving) and 15% (at least one failure) of F. ult. The FEA-based tool predicted F. e. values of 11.7% and 12.0% of F. ult. for the validation sets of 50SP and 50SW, respectively. Thus, the developed FEA-based workflow could accurately predict endurance limit for different implant designs and therefore could be used in future to aid the development of novel porous implants. Acknowledgements: This study was funded by EU's Horizon 2020 grant No. 953128 (I-SMarD). We gratefully acknowledge the expert advice of Prof. Philippe Zysset

Background. There is a 20% dissatisfaction rate with knee replacements. Calls for tools that can pre-operatively identify patients at risk of being dissatisfied postoperatively have been widespread. However, it is unclear what sort of information patients would want from such a tool, how it would affect their decision making process, and at what part of the pathway such a tool should be used. Methods. Using focus groups involving 12 participants and in-depth interviews with 10 participants, we examined the effect outcome prediction has by providing fictitious predictions to patients at different stages of treatment. A qualitative analysis of themes, based on a constant comparative method, is used to analyse the data. This study was approved by the Dyfed Powys Research Ethics Committee (13/WA/0140). Results. Our results demonstrate several interesting findings. Firstly, patients who have received information from friends and family are unwilling to adjust their expectation of outcome down (i.e. to a worse outcome), but highly willing to adjust it up (to a better outcome). This is an example of the optimism bias, and suggests the effect on expectation of any poor outcome prediction would be blunted. Secondly, patients generally wanted a “bottom line” outcome, rather than lots of detail. Thirdly, patients who were earlier in their treatment for osteoarthritis were more likely to find the information useful, and for it to affect their decision, than patients later in their pathway. Conclusion. An outcome prediction tool would have most effect targeted towards people at the start of their treatment pathway, with a “bottom line” prediction of outcome. However, any effect on expectation and decision making of a poor outcome prediction is likely to be blunted by the optimism bias. Level of Evidence. 4

Background. Stability of total knee arthroplasty (TKA) is dependent on correct and precise rotation of the femoral component. Multiple differing surgical techniques are currently utilized to perform total knee arthroplasty. Accurate implant position have been cited as the most important factors of successful TKA. There are two techniques of achieving soft gap balancing in TKA; a measured resection technique and a balanced gap technique. Debate still exists on the choice of surgical technique to achieve the optimal soft tissue balance with opinions divided between the measured resection technique and the gap balance technique. In the measured resection technique, the bone resection depends on size of the prosthesis and is referenced to fixed anatomical landmarks. This technique however may have accompanying problems in imbalanced patients. Prediction of gap balancing technique, tries to overcome these fallacies. Our aim in this study was twofold: 1) To describe our methodology of ROBOTIC TKA using prediction of gap balancing technique. 2) To analyze the clinico-radiological outcome our technique comparison of meseaured resection ROBOTIC TKA after 1year. Methods. Patients that underwent primary TKA using a robotic system were included for this study. Only patients with a diagnosis of primary degenerative osteoarthritis with varus deformity and flexion deformity of were included in this study. Patients with valgus deformity, secondary arthritis, inflammatory arthritis, and severe varus/flexion deformity were excluded. Three hundred ten patients (319 knees) who underwent ROBOTIC TKA using measured resection technique from 2004 – 2009. Two hundred twenty (212 knees) who underwent ROBOTIC TKA using prediction of gap balancing technique from 2010 – 2012. Clinical outcomes including KS and WOMAC scores, and ranges of motion and radiological outcomes including mechanical axis, prosthesis alignments, flexion varus/valgus stabilities were compared after 1year. Results. Leg mechanical axes were significantly different at follow-up 1year versus preoperative values, the mean axes in the Robotic-TKA with measured resection technique and Robotic-TKA with prediction of gap balancing technique improved from 9.6±5.0° of varus to 0.5±1.9° of varus, and from 10.6±5.5° to 0.4±1.3° of varus (p<0.001), respectively. However, no significant intergroup differences were found between mechanical axis or coronal alignments of femoral or tibial prostheses (pï¼ï¿½0.05). Mean varus laxities at 90° of knee flexion in measured resection and gap prediction technique group were 6.4° and 5.3°, respectively, and valgus laxities were 6.2 and 5.2 degrees, respectively, with statistical significance (p=0.045 and 0.032, respectively). KS knee and function scores and WOMAC scores were significantly improved at follow-up 1year (pï¼ï¿½0.05). However, no significant difference was found between the Robotic-TKA with measured resection technique and Robotic-TKA with prediction of gap balancing technique for any clinical outcome parameter at follow-up 1year (pï¼ï¿½0.05). Conclusions. Robotic assisted TKA using measured resection or gap prediction technique provide adequate and practically identical levels of flexion stability at 90° of knee flexion with accurate leg and prosthesis alignment. But, Robotic TKA using measured resection technique have less than flexion stability compared with gap prediction technique with statistical significance after follow-up 1year

Background. Total knee arthroplasty (TKA) is a proven and cost-effective treatment for osteoarthritis. Despite the good to excellent long-term results, some patients remain dissatisfied. Our study aimed at establishing a predictive model to aid patient selection and decision-making in TKA. Methods. Using data from our prospective arthroplasty outcome database, 113 patients were included. Pre- and postoperatively, the patients completed 107 questions in 5 questionnaires: KOOS, OKS, PCS, EQ-5D and KSS. First, outcome parameters were compared between the satisfied and dissatisfied group. Secondly, we developed a new prediction tool using regression analysis. Each outcome score was analysed with simple regression. Subsequently, the predictive weight of individual questions was evaluated applying multiple linear regression. Finally, 10 questions were retained to construct a new prediction tool. Results. Overall satisfaction rate in this study was found to be 88%. We identified a significant difference between the satisfied and dissatisfied group when looking at the preoperative questionnaires. Dissatisfied patients had more preoperative symptoms (such as stiffness), less pain and a lower QOL. They were more likely to ruminate and had a lower preoperative KSS satisfaction score. The developed prediction tool consists of 10 simple, but robust questions. Sensitivity was 97% with a positive predictive value of 93%. Conclusions. Based upon preoperative parameters, we were able to partially predict satisfaction and dissatisfaction after TKA. After further validation this new prediction tool for patient satisfaction following TKA may allow surgeons and patients to evaluate the risks and benefits of surgery on an individual basis and help in patient selection

Introduction. Up to 60% of total hip arthroplasties (THA) in Asian populations arise from avascular necrosis (AVN), a bone disease that can lead to femoral head collapse. Current diagnostic methods to classify AVN have poor reproducibility and are not reliable in assessing the fracture risk. Femoral heads with an immediate fracture risk should be treated with a THA, conservative treatments are only successful in some cases and cause unnecessary patient suffering if used inappropriately. There is potential to improve the assessment of the fracture risk by using a combination of density-calibrated computed tomographic (QCT) imaging and engineering beam theory. The aim of this study was to validate the novel fracture prediction method against in-vitro compression tests on a series of six human femur specimens. Methods. Six femoral heads from six subjects were tested, a subset (n=3) included a hole drilled into the subchondral area of the femoral head via the femoral neck (University of Leeds, ethical approval MEEC13-002). The simulated lesions provided a method to validate the fracture prediction model with respect of AVN. The femoral heads were then modelled by a beam loaded with a single joint contact load. Material properties were assigned to the beam model from QCT-scans by using a density-modulus relationship. The maximum joint loading at which each bone cross-section was likely to fracture was calculated using a strain based failure criterion. Based on the predicted fracture loads, all six femoral heads (validation set) were classified into two groups, high fracture risk and low fracture risk (Figure 1). Beam theory did not allow for an accurate fracture load to be found because of the geometry of the femoral head. Therefore the predicted fracture loads of each of the six femoral heads was compared to the mean fracture load from twelve previously analysed human femoral heads (reference set) without lesions. The six cemented femurs were compression tested until failure. The subjects with a higher fracture risk were identified using both the experimental and beam tool outputs. Results. The computational tool correctly identified all femoral head samples which fractured at a significantly low load in-vitro (Figure 2). Both samples with a low experimental fracture load had an induced lesion in the subchondral area (Figure 3). Discussion. This study confirmed findings of a previous verification study on a disease models made from porcine femoral heads (Preutenborbeck et al. I-CORS2016). It demonstrated that fracture prediction based on beam theory is a viable tool to predict fracture. The tests confirmed that samples with a lesion in the weight bearing area were more likely to fracture at a low load however not all samples with a lesion fractured with a low load experimentally, indicating that a lesion alone is not a sufficient factor to predict fracture. The developed tool takes both structural and material properties into account when predicting the fracture risk. Therefore it might be superior to current diagnostic methods in this respect and it has the added advantage of being largely automated and therefore removing the majority of user bias. For any figures or tables, please contact the authors directly

Introduction and Aims: We propose a new, simple, and universal method to predict adult height: the Height Multiplier Method. Our aim was to calculate height multipliers from various databases and validate their use for height prediction. Method: Standard growth charts, based on a diverse population, were published by the Centres for Disease Control and Prevention (CDC) in 2000. Height multipliers (M) for boys and girls were calculated by dividing the height at skeletal maturity (Htm) by the present height (Ht) (M = Htm/Ht) for each age, gender, and height percentile using CDC data. These multipliers were compared with multipliers derived from various height databases of 28 boys and 24 girls. The accuracy of the multipliers was tested on individual longitudinal data sets from 52 normal children. Results: The average CDC-derived multipliers were significantly different at each age for boys and girls, but within gender, different percentiles at each age were very similar. These multipliers were very similar to multipliers derived from each of the databases. For predictions based on individual data sets from 52 children, the median, 90%, and standard deviation of absolute error prediction (AEP) were calculated. Boys’ median AEP ranged from 1.4–4.3cm; 90% AEP ranged from 1.8–8.3cm. Girls’ median AEP ranged from 0.68–4.38cm; 90% AEP ranged from 1.5–10.6cm. Conclusion: The Height Multiplier Method of stature prediction is as accurate as CDC growth charts when based on single-height measurements and is similar in accuracy to other methods. The Height Multiplier Method has the advantage of percentile, race, nationality, and generation independence. Growth charts have the advantage of showing trends over time

In conventional DXA (Dual-energy X-ray Absorptiometry) analysis, pixel bone mineral density (BMD) is often averaged at the femoral neck. Neck BMD constitutes the basis for osteoporosis diagnosis and fracture risk assessment. This data averaging, however, limits our understanding of localised spatial BMD patterns that could potentially enhance fracture prediction. DXA region free analysis (RFA) is a validated toolkit for pixel-level BMD analysis. We have previously deployed this toolkit to develop a spatio-temporal atlas of BMD ageing in the femur. This study aims first to introduce bone age to reflect the overall bone structural evolution with ageing, and second to quantify fracture-specific patterns in the femur. The study dataset comprised 4933 femoral DXA scans from White British women aged 75 years or older. The total number of fractures was 684, of which 178 were reported at the hip within a follow-up period of five years. BMD maps were computed using the RFA toolkit. For each BMD map, bone age was defined as the age for which the L2-norm between the map and the median atlas at that age is minimised. Next, bone maps were normalised for the estimated bone age. A t-test followed by false discovery rate (FDR) analysis was applied to compare between fracture and non-fracture groups. Excluding the ageing effect revealed subtle localised patterns of loss in BMD oriented in the same direction as principal tensile curves. A new score called f-score was defined by averaging the normalised pixel BMD values over the region with FDR q-value less than 1e–6. The area under the curve (AUC) was 0.731 (95% confidence interval (CI)=0.689–0.761) and 0.736 (95% CI=0.694–0.769) for neck BMD and f-score. Combining bone age and f-score improved the AUC significantly by 3% (AUC=0.761, 95% CI=0.756–0.768) over the neck BMD alone (AUC=0.731, 95% CI=0.726–0.737). This technique shows promise in characterizing spatially-complex BMD changes, for which the conventional region-based technique is insensitive. DXA RFA shows promise to further improve fracture prediction using spatial BMD distribution

During OA the homeostasis of healthy articular chondrocytes is dysregulated, which leads to a phenotypical transition of the cells, further influenced by external stimuli. Chondrocytes sense those stimuli, integrate them at the intracellular level and respond by modifying their secretory and molecular state. This process is controlled by a complex interplay of intracellular factors. Each factor is influenced by a myriad of feedback mechanisms, making the prediction of what will happen in case of external perturbation challenging. Hampering the hypertrophic phenotype has emerged as a potential therapeutic strategy to help OA patients (Ripmeester et al. 2018). Therefore, we developed a computational model of the chondrocyte's underlying regulatory network (RN) to identify key regulators as potential drug targets. A mechanistic mathematical model of articular chondrocyte differentiation was implemented with a semi-quantitative formalism. It is composed of a protein RN and a gene RN(GRN) and developed by combining two strategies. First, we established a mechanistic network based on accumulation of decades of biological knowledge. Second, we combined that mechanistic network with data-driven modelling by inferring an OA-GRN using an ensemble of machine learning methods. This required a large gene expression dataset, provided by distinct public microarrays merged through an in-house pipeline for cross-platform integration. We successfully merged various micro-array experiments into one single dataset where the biological variance was predominant over the batch effect from the different technical platforms. The gain of information provided by this merge enabled us to reconstruct an OA-GRN which subsequently served to complete our mechanistic model. With this model, we studied the system's multi-stability, equating the model's stable states to chondrocyte phenotypes. The network structure explained the occurrence of two biologically relevant phenotypes: a hypertrophic-like and a healthy-like phenotype, recognized based on known cell state markers. Second, we tested several hypotheses that could trigger the onset of OA to validate the model with relevant biological phenomena. For instance, forced inflammation pushed the chondrocyte towards hypertrophy but this was partly rescued by higher levels of TGF-β. However, we could annihilate this rescue by concomitantly mimicking an increase in the ALK1/ALK5 balance. Finally, we performed a screening of in-silico (combinatorial) perturbations (inhibitions and/or over-activations) to identify key molecular factors involved in the stability of the chondrocyte state. More precisely, we looked for the most potent conditions for decreasing hypertrophy. Preliminary validation experiments have confirmed that PKA activation could decrease the hypertrophic phenotype in primary chondrocytes. Importantly the in-silico results highlighted that targeting two factors at the same time would greatly help reducing hypertrophic changes. A priori testing of conditions with in-silico models may cut time and cost of experiments via target prioritization and opens new routes for OA combinatorial therapies

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

Total hip replacements (THRs) provide pain relief and improved function to thousands of patients suffering from end-stage osteoarthritis, every year. Over 800 different THR constructs were implanted in the UK in 2017. To ensure reliable implants are used, a NICE revision benchmark of 5% after 10 years exists. Given the 10-year cumulative mortality of patients under 55 years of age receiving THRs is only 5% and that a recent study suggests 25-year THR survival of 58%, we aim to produce revision estimates out to 30 years that may guide future long-term benchmarks. The local database of the Princess Elizabeth Orthopaedic Centre (PEOC), Exeter, holds data on over 20,000 patients with nearly 30-years follow-up with contemporary prostheses. A previous study suggests that the results of this centre are generalisable if comparisons restricted to the same prostheses. Via flexible parametric survival analysis, we created an algorithm using this database, for revision of any part of the construct for any reason, controlling for age and gender. This algorithm was applied to 664,761 patients in the NJR who have undergone THR, producing a revision prediction for patients with the same prostheses as those used at this centre. Using our algorithm, the 10-year predicted revision rate of THRs in the NJR was 2.2% (95% CI 1.8, 2.7) based on a 68-year-old female patient; well below the current NICE benchmark. Our predictions were validated by comparison to the maximum observed survival in the NJR (14.2 years) using restricted mean survival time (P=0.32). Our predicted cumulative revision estimate after 30 years is 6.5% (95% CI 4.5, 9.4). The low observed and predicted revision rate with the prosthesis combinations studied, suggest current benchmarks may be lowered and new ones introduced at 15 and 20 years to encourage the use of prostheses with high survival

Introduction. Integrating additively manufactured structures, such as porous lattices into implants has numerous potential benefits, such as custom mechanical properties, porosity for osseointegration/fluid flow as well as improved fixation features. Component anisotropic stiffness can be controlled through varying density and lattice orientation. This is useful due to the influence of load on bone remodelling. Matching implant and bone anisotropy/stiffness may help reduce problems such as stress shielding and prevent implant loosening. It is therefore beneficial to be able to design AM parts with a desired anisotropic stiffness. In this study we present a method that predicts the anisotropic stiffness of an additively manufactured lattice structure from its CAD data, and validate this model with experimental testing. The model predicts anisotropic stiffness in terms of density (ρ), fabric (M) and fabric eigen values (m) and is matched to stiffness data of the structure in 3 principal directions, based on an orthotropic assumption. This model was described in terms of 10 constants and had the form shown in Equation 1. Eq.1. S. =. ∑. i. ,. j. =. 1.  .  .  .  . i. ,. j. =. 3. λ. (. i. ,. j. ). ρ. k. m. (. i. ). 1. (. i. ). m. (. j. ). 1. (. i. ). |. M. i. M. j. '. |. 2. Methods. A stochastic line structure was formed in CAD by joining pseudo-random points generated using the Poisson-disk method Lines at an angle lower than 30° to the x-y plane removed to allow for AM manufacturing. Lines were converted to struts with 330 µm diameter. Second order fabric tensors were determined from CAD files of the AM specimens using the mean intercept length (MIL), the gold standard for determining a measure of the ‘average orientation’ of material within trabecular bone structures. 10 × 10 × 12 mm specimens of the CAD model were manufactured on a Renishaw AM250 powder bed fusion machine. The structure was built in 10 different orientations to enable stiffness measurement in 10 different directions (n=5 for each direction). Compression testing in a servohydraulic materials testing machine was performed according to ISO13314 with LVDTs used to measure displacement to remove compliance effects. Stress-strain curves were obtained and elastic moduli were estimated from a hysteresis loop in the load application, from 70% to 20% of the plateau stress. Specimen density and fabric data were fit to the observed stiffnesses using least squares linear regression. Experimental stiffnesses of the structure in 10 directions were compared to the model to evaluate the accuracy of model predictions. Results & Discussion. The model predicted the stiffness of the structure across all 10 orientations to within 13% absolute error compared to the observed stiffness data, with an R. 2. value of 0.969. The three dimensional stiffness plot formed by the model was similar to the experimental data, displaying an hourglass shape. Our model is the first to predict the anisotropic stiffness of stochastic structures and will be highly useful in predicting stiffness of lattice structures and could also be applied to bone to measure anisotropic stiffness. For any figures or tables, please contact authors directly

Previously, we showed that case-specific non-linear finite element (FE) models are better at predicting the load to failure of metastatic femora than experienced clinicians. In this study we improved our FE modelling and increased the number of femora and characteristics of the lesions. We retested the robustness of the FE predictions and assessed why clinicians have difficulty in estimating the load to failure of metastatic femora. A total of 20 femora with and without artificial metastases were mechanically loaded until failure. These experiments were simulated using case-specific FE models. Six clinicians ranked the femora on load to failure and reported their ranking strategies. The experimental load to failure for intact and metastatic femora was well predicted by the FE models (R. 2. = 0.90 and R. 2. = 0.93, respectively). Ranking metastatic femora on load to failure was well performed by the FE models (τ = 0.87), but not by the clinicians (0.11 < τ < 0.42). Both the FE models and the clinicians allowed for the characteristics of the lesions, but only the FE models incorporated the initial bone strength, which is essential for accurately predicting the risk of fracture. Accurate prediction of the risk of fracture should be made possible for clinicians by further developing FE models.

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.

Introduction:. Primary stability is crucial for long-term fixation of cementless tibial trays. Micromotion less than 50 μm is associated with stable bone ingrowth and greater than 150 μm causes the formation of fibrous tissue around the implant [1, 2]. Finite element (FE) analysis of complete activities of daily living (ADL's) have been used to assess primary stability, but these are computationally expensive. There is an increasing need to account for both patient and surgical variability when assessing the performance of total joint replacement. As a consequence, an implant should be evaluated over a spectrum of load cases. An alternative approach to running multiple FE models, is to perform a series of analyses and train a surrogate model which can then be used to predict micromotion in a fraction of the time. Surrogate models have been used to predict single metrics, such as peak micromotion. The aim of this work is to train a surrogate model capable of predicting micromotion over the entire bone-implant interface. Methods:. A FE model of an implanted proximal tibia was analysed [3] (Fig. 1). A statistical model of knee kinetics, incorporating subject-specific variability in all 6-DOF joint loads [4], was used to randomly generate loading profiles for 50 gait cycles. A Latin Hypercube (LH) sampling method was applied to sample 6-DOF loads of the new population throughout the gait cycle. Kinetic data was sampled at 10, 50 and 100 instances and FE predictions of micromotion were calculated and used to train a surrogate model capable of describing micromotion over the entire bone-implant interface. The surrogate model was tested for an unseen gait cycle and the resulting micromotions were compared with FE predictions. Results and discussion:. Accuracy of the surrogate model increased with increasing sample size in the training set; with a LH sample of 10, 50 and 100 trials, the surrogate model predicted micromotion at the bone-implant interface during gait with RMS accuracy of 61, 44 and 33 μm, respectively (Fig. 2). Similar range in micromotion was measured in FE and surrogate models; although the surrogate model tended to over-predict micromotion early in the gait cycle (Fig. 2). There was good agreement in location and magnitude of micromotion at the interface surface through out the gait cycle (Fig. 3). Although encouraging, further work is required to optimize the number and distribution of the training samples to minimize the error in the surrogate model. Analysis time for the FE model was 15 hours, compared to 30 seconds for the surrogate model. The results suggest that surrogate models have significant potential to rapidly predict micromotion over the entire bone-implant interface, allowing for a greater range in loading conditions to be explored than would be possible through conventional methods

Introduction. From 2004 to 2015, elective lumbar fusions increased by 62% in the US. The largest increases were for among age 65 or older (139% in volume) and scoliosis (187%) [1]. Age is a well known factor of osteoporosis. The load-sharing may exceed the pedicular screws constructs in aging spine and lead to non-union and re-do. Surgical options may increase the screw purchase (e.g.: augmentation, extensions) at supplementary risks. Pedicular screw are known to cause vascular, nerve root or cord injuries. Facing these pitfalls, the surgeon's experience and rule of thumbs are the most deciding factors for the surgical planning. The aim of this study is to assess the accuracy of a patient specific tool, designed to plan a safe pedicular trajectory and to provide an intraoperative screw pullout strength estimate. Materials and Methods. Clinical QCT were taken for nine cadaveric spines (82 y. [61; 87], 6 females, 3 males). The experimental maximum axial pullout resistance (FMax) of twenty-seven pedicular screws inserted (nine T12, nine L4 and nine L5) was obtained as described in a previous study [2]. A custom 3D-WYSIWYG software simulated a medio-lateral surgical insertion technique in the QCTs coordinates reference, respecting the cortical walls. Repeatable density, morphometric and hardware parameters were recorded for each vertebrae. A statistical model was built to match predictive and experimental data. Preliminary results. Experimental FMax(N) were [104;953] (359 ±223). A further displacement of 1,81mm ±0,35 halved the experimental FMax. Predictive FMax(N) were [142;862] (359 ±220). A high positive correlation between experimental and predictive FMax was revealed (Pearson, ρ = 0.93, R2 = 0.87, p < .001, figure 1). Absolute differences ranged between 3N and 177N. Discussion. A high screw purchase in primary fixation is paramount to achieve spine surgical procedures (e.g.: kyphosis, scoliosis) and postoperative stability for vertebrae fusion. High losses of screw purchase by bone plastic deformation, begin with tiny pullouts. Theses unwanted intraoperative millimetric over-displacements are hard to avoid when monitoring at the same time tens of screws surrounded by bleedings. This advocates for including predictive FMax for each implantable pedicular screw in the surgical planning decision making process to prevent failures and assess risks. For the first time, this study presents an experimentally validated statistical model for FMax prediction with a safe trajectory definition tool, including patients’ vertebrae and hardware properties and referring to the patient's clinical 3D quantitative imagery. The model was able to differentiate between bone quality and vertebrae variations. More extensive model validation is currently ongoing to interface with robotics & navigation systems and to produce meshes for 3D printing of sterilizable insertion guides

The prospective evaluation of two hundred and seven symptomatic total knee arthroplasties presenting for revision total knee arthroplasty is reported. On univariate analysis patients who had infection differed significantly (p< .001) from those without infection with regards to: elevated ESR, CRP, positive aspiration, and history of; revision procedure less than two years since last surgery, early wound problems, ongoing pain since index procedure, and discharging wound. On multivariate analysis elevated ESR or CRP, positive aspiration, pain since index procedure and early wound complications were significant predictors of infection (p< .05). These variables were then used to formulate an evidence-based multivariate predictive algorithm to assist the clinician in decision making prior to surgery. Differentiating septic from aseptic failure of total knee arthroplasty on the basis of clinical features and diagnostic tests can be troublesome for the clinician. The purpose of this paper is to describe significant differences between cases of septic and aseptic failure of total knee arthroplasty. The incorporation of these variables into a practical multivariate clinical prediction algorithm can provide assistance in establishing the diagnosis of infection prior to revision knee arthroplasty. A simple clinical prediction algorithm can assist in the diagnosis of infection in patients with painful total knee arthroplasty. Patients with five of five criteria have a 99% probability of infection whereas patients with zero of five criteria have a 1% probability of infection. This is the first multivariate evidence-based clinical prediction algorithm presented for use in decision making prior to revision total knee arthroplasty. The surgeon can use the information derived from clinical and laboratory assessment to compute an approximate pre-operative probability of infection prior to surgery (see table). On multivariate analysis elevated ESR or CRP, positive aspiration, pain since index procedure and early wound complications were significant predictors of infection (p< .05). These variables were then used to formulate an evidence-based multivariate predictive algorithm to assist in clinical decision making. Prospective data was collected on two hundred and seven symptomatic knee arthroplasties presenting for revision arthroplasty. A multivariate logistic regression model was used to determine the probability of infection using five significant variables. Combinations of these five variables can provide the clinician with an estimate of the probability of infection prior to revision knee arthroplasty. Please contact author for tables and/or charts

Aims

Current levels of hip fracture morbidity contribute greatly to the overall burden on health and social care services. Given the anticipated ageing of the population over the coming decade, there is potential for this burden to increase further, although the exact scale of impact has not been identified in contemporary literature. We therefore set out to predict the future incidence of hip fracture and help inform appropriate service provision to maintain an adequate standard of care.

Abstract

Introduction. Preoperative templating of femoral and tibial components can assist in choosing the appropriate implant size prior to TKA. While weight bearing long limb roentograms have been shown to provide benefit to the surgeon in assessing alignment, disease state, and previous pathology or trauma, their accuracy in size prediction is continually debated due to scaling factors and rotated views. Further, they represent a static time point, accounting for boney anatomy only. A perceived benefit of robotic-assisted surgery is the ability to pre-operatively select component sizes with greater accuracy based on 3D information, however, to allow for flexibility in refining based on additional data only available at the time of surgery. Methods. The purpose of this study was to determine the difference of pre-operative plans in size prediction of the tibia, femur, and polyethylene insert. Eighty four cases were enrolled at three centers as part of an Investigational Device Exemption to evaluate a robotic-assisted TKA. All patients had a CT scan as part of a pre-operative planning protocol. Scans were segmented and implant sizes predicted based on the patients boney morphology and an estimated 2mm cartilage presence. Additional information such as actual cartilage presence and soft tissue effects on balance and kinematics were recorded intra-operatively. Utilizing this additional information, surgical plans were fine tuned if necessary to achieve minimal insert thickness and balance. Data from the Preoperative CT plan sizing and final size were compared to determine the percentage of size and within one size accuracy. Results. The pre-operative plan was able to determine the femoral and tibial components within one size for 100% of cases. Intra-operatively, surgeon upsized femoral 15 out of 85 (18%), downsized femoral 1 out of 85 (1%), baseplate 13 out of 85 (15%), and downsized baseplate 4 out of 85 (5%). Polyethylene exact size could be planned 93% of the time. Discussion/Conclusion. Robotic-assisted pre-operative CT based planning was accurate over 70% of the time for the femur and tibial components, and over 90% with respect to the insert thickness Additionally, intraoperative information allowed for adjustments to provide patients with ideal coverage of articular surfaces and for joint balancing providing optimal individualized component placement. Further research is needed to determine the potential cost savings in hospital and OR inventory management

The primary objectives of this study were to: 1) identify risk factors for subsequent surgery following initial treatment of proximal humerus fractures, stratified by initial treatment type; 2) generate risk prediction tools to predict subsequent shoulder surgery following initial treatment; and 3) internally validate the discriminative ability of each tool. We identified patients ≥ 50 years with a diagnosis of proximal humerus fracture from 2004 to 2015 using linkable health datasets in Ontario, Canada. We used procedural and fee codes within 30 days of the index fracture to classify patients into treatment groups: 1) surgical fixation; 2) shoulder replacement; and 3) conservative. We used intervention and diagnosis codes to identify all instances of complication-related subsequent shoulder surgery following initial treatment within two years post fracture. We developed logistic regression models for randomly selected two thirds of each treatment group to evaluate the association of patient, fracture, surgical, and hospital variables on the odds of subsequent shoulder surgery following initial treatment. We used regression coefficients to compute points associated with each of the variables within each category, and calculated the risk associated with each point total using the regression equation. We used the final third of each cohort to evaluate the discriminative ability of the developed risk tools (via the continuous point total and a dichotomous point cut-off value for “higher” vs. “lower” risk determined by Receiver Operating Curves) using c-statistics. We identified 20,897 patients with proximal humerus fractures that fit our inclusion criteria for analysis, 2,414 treated with fixation, 1,065 treated with replacement, and 17,418 treated conservatively. The proportions of patients who underwent subsequent shoulder surgery within two years were 13.8%, 5.1%, and 1.3%, for fixation, replacement, and conservative groups, respectively. Predictors of reoperation following fixation included the use of a bone graft, and fixation with a nail or wire vs. a plate. The only significant predictor of reoperation following replacement was poor bone quality. The only predictor of subsequent shoulder surgery following conservative treatment was more comorbidities while patients aged 70+, and those discharged home following initial presentation (vs. admitted or transferred to another facility) had lower odds of subsequent shoulder surgery. The risk tools developed were able to discriminate between patients who did or did not undergo subsequent shoulder surgery in the derivation cohorts with c-statistics of 0.75–0.88 (continuous point total), and 0.82–0.88 (dichotomous cut-off), and 0.53–0.78 (continuous point total) and 0.51–0.79 (dichotomous cut-off) in the validation cohorts. Our results present potential factors associated with subsequent shoulder surgery following initial treatment of proximal humerus fractures, stratified by treatment type. Our developed risk tools showed good to strong discriminative ability in both the derivation and validation cohorts for patients treated with fixation, and conservatively. This indicates that the tools may be useful for clinicians and researchers. Future research is required to develop risk tools that incorporate clinical variables such as functional demands

PURPOSE: To determine the capability of fellowship trained Orthopaedic Trauma surgeons to predict union or non-union of femoral and tibial shaft fractures. METHODS: A series of 50 patients with femur or tibia shaft fractures were evaluated. Patients were prospectively followed at 2,6,12, and 18 weeks after surgical intervention. At each interval surgeons evaluated factors related to fracture healing on AP and lateral radiographs and predicted the probability of union on a visual analog scale. Union was defined as radiographic evidence of healing three of four cortices, no tenderness with palpation of the fracture site, and full weight bearing without the use of assistive devices. RESULTS: Eight patients missed initial visits or were lost to follow-up, making for a total of 42 patients that were included in the results. Average patient age was 31 years. Eighty-one percent of the patients went onto union (N=34) and 19% went onto nonunion (N=8). Early clinical prediction for nonunion at 2 weeks had a sensitivity of 50%, a specificity of 91%, a positive predictive value (PPV) of 57%, and a negative predictive value (NPV) of 89%. At 6 weeks, there was a sensitivity of 75%, a specificity of 100%, a PPV of 100%, and a NPV of 94%. One patient treated with intramedullary nailing was 15 years old and despite minimal callous formation the physician incorrectly predicted future union given the young age. The other patient had a severely comminuted femur fracture and required a quad cane to ambulate and should perhaps have been predicted to go onto nonunion. At 12 and 18 weeks, sensitivity, specificity, PPV, and NPV were both 100%. CONCLUSIONS: Fellowship trained orthopaedic trauma surgeons at 6-week follow-up can predict union with a sensitivity of 75% and specificity of 100% and a PPV of 100%. Early clinical prediction at 6 weeks can be used to provide the patient with a secondary intervention such as a bone graft or bone stimulator and avoid months of delay

This is quite an innovative study that should lead to a multicentre validation trial. We have developed an FDG-PET/MRI texture-based model for the prediction of lung metastases (LM) in newly diagnosed patients with soft-tissue sarcomas (STSs) using retrospective analysis. In this work, we assess the model performance using a new prospective STS cohort. We also investigate whether incorporating hypoxia and perfusion biomarkers derived from FMISO-PET and DCE-MRI scans can further enhance the predictive power of the model. A total of 66 patients with histologically confirmed STSs were used in this study and divided into two groups: a retrospective cohort of 51 patients (19 LM) used for training the model, and a prospective cohort of 15 patients (two patients with LM, one patient with bone metastases and suspicious lung nodules) for testing the model. In the training phase, a model of four texture features characterising tumour sub-region size and intensity heterogeneities was developed for LM prediction from pre-treatment FDG-PET and MRI scans (T1-weighted, T2-weighted with fat saturation) of the retrospective cohort, using imbalance-adjusted bootstrap statistical resampling and logistic regression multivariable modeling. In the testing phase, this multivariable model was applied to predict the distant metastasis status of the prospective cohort. The predictive power of the obtained model response was assessed using the area under the receiver-operating characteristic curve (AUC). In the exploratory phase of the study, we extracted two heterogeneity metrics from the prospective cohort: the area under the intensity-volume histogram of pre-treatment DCE-MRI volume transfer constant parametric maps and FMISO-PET hypoxia maps (AU-IVH-Ktrans, AU-IVH-FMISO). The impact of the addition of these two individual metrics to the texture-based model response obtained in the testing phase was first investigated using Spearman's correlation (rs), and lastly using logistic regression and leave-one-out cross-validation (LOO-CV) to account for overfitting bias. First, the texture-based model reached an AUC of 0.94, a sensitivity of 1, a specificity of 0.83 and an accuracy of 0.87 when tested in the prospective cohort. In the exploratory phase, the addition of AU-IVH-FMISO did not improve predictive power, yielding a correlation of rs = −0.42 (p = 0.12) with lung metastases, and a relative change in validation AUC of 0% in comparison with the texture-based model response alone in LOO-CV experiments. In contrast, the addition of AU-IVH-Ktrans improved predictive power, yielding a correlation of rs = −0.54 (p = 0.04) with lung metastases, and a change in validation AUC of +10%. Our results demonstrate that texture-based models extracted from pre-treatment FDG-PET and MRI anatomical scans could be successfully used to predict distant metastases in STS cancer. Our results also suggest that the addition of perfusion heterogeneity metrics may contribute to improving model prediction performance

Introduction. Despite generally excellent patient outcomes for Total Knee Arthroplasty (TKA), there remains a contingent of patients, up to 20%, who are not satisfied with the outcome of their procedure. (Beswick, 2012) There has been a large amount of research into identifying the factors driving these poor patient outcomes, with increasing recognition of the role of non-surgical factors in predicting achieved outcomes. However, most of this research has been based on single database or registry sources and so has inherited the limitations of its source data. The aim of this work is to develop a predictive model that uses expert knowledge modelling in conjunction with data sources to build a predictive model of TKR patient outcomes. Method. The preliminary Bayesian Belief Network (BBN) developed and presented here uses data from the Osteoarthritis Initiative, a National Institute of Health funded observational study targeting improved diagnosis and monitoring of osteoarthritis. From this data set, a pared down subset of patient outcome relevant preoperative questionnaire sets has been extracted. The BBN structure provides a flexible platform that handles missing data and varying data collection preferences between surgeons, in addition to temporally updating its predictions as the patient progresses through pre and postoperative milestones in their recovery. In addition, data collected using wearable activity monitoring devices has been integrated. An expert knowledge modelling process relying on the experience of the practicing surgical authors has been used to handle missing cross-correlation observations between the two sources of data. Results. The model presented here has been internally cross validated and has some interesting facets, including the strongest single predictive question of bad outcome for the patient being the presence of lower back pain. Clinical implementation and long term predictive accuracy result collection is ongoing. Discussion. Unsatisfied patients represent a significant minority of TKR recipients, with multiple, multifaceted causal factors both in surgery and out implicated. Historically, focus has been on the role of management and improvement of the surgical factors, which is linked to the fact that surgical factors can often lead to far more disastrous consequences for the patient and the basic principle that “you only improve what you measure.” Growing collection of Patient Reported Outcome Measures by registries around the world has exposed the fact that management of patient factors has lagged behind. (Judge, 2012) Increasingly, the pivotal role of unmet expectations in determining patient satisfaction (Noble, 2006) and the “expectation gap” (Ghomrawi, 2012) between surgeons and patients has been exposed as an opportunity to improve patient outcomes. By developing a model that uses existing surgical expert knowledge to integrate research identified preoperative factors that can be accurately and practically gathered in a clinical setting, a workflow that manages patient expectations in order to optimize outcomes could reduce dissatisfaction rates in TKR recipients. Future work should focus on improving clinical integration and, in the absence of sufficiently wide, deep and complete patient response and predictor datasets, ways of harnessing existing expert knowledge into an evolving predictive tool of patient outcomes

A predictive model for final kyphosis was tested by evaluating the radiographs of forty-three patients with traumatic burst fractures. Since clinical outcomes are related to final kyphosis in the ambulatory patient rather than on the initial supine injury radiograph, the ability to predict final kyphosis is beneficial in determining treatment. This study demonstrated that in the appropriately selected patient for conservative care, the limit of final-kyphosis(Kf) can be predicted from the intial-kyphosis(KI) , such that Kf= < KI+.5KI . Outliers from this equation were patients who had unrecognized posterior column fractures, superior and inferior end-plate fractures, and/or multiple level of injury. The purpose of this study was to define a prediction model that afforded clinicians the ability to define final kyphosis from initial supine films in order to guide the management of stable burst fractures. This study has demonstrated that as a rule of thumb, the final absolute kyphosis for stable burst fractures can be expected to be up to Ki (initial absolute kyphosis) + 1.5Ki. Outliers were found to be fractures with unrecognized posterior element injury, both superior and inferior endplate fractures and multiple level injuries. The final kyphosis is clinically more relevant than the initial kyphosis in terms of functional outcome after conservative management. A prediction model for final kyphosis based on initial injury films can help guide the clinician for optimal management. Retrospective radiographic analysis was performed on forty-three patients with a minimum follow up six months. All patients suffered traumatic burst fractures, which were deemed stable as to be satisfactorily managed in a brace. Serial radiographs were used to determine initial (Ki) and final (Kf), Kyphosis angles. Predicted Kf was determined using the equation Kf =Ki + 1.5 Ki. The initial absolute kyphosis was the measured kyphosis using the Cobb technique and including the loss of the expected normal lordosis of that spinal segment. Inclusion criteria included burst fractures at between levels T10 – L3 in the neurologically intact patient. The equation accurately predicted the final outcome , Kf, in 70 % of the cases. In 20% of the cases, the Kf was less than expected. (Acceptable clinical result). In 10% of the cases, Kf was greater than predicted or achieved a clinically unacceptable kyphotic angulation requiring secondary surgery. In this group of outliers, post-hoc analysis identified unrecognized posterior element injury, both superior and inferior endplate fractures and multiple level injuries. In traumatic burst fractures, the goal of management is to protect the spine during healing while maintaining an acceptable alignment, which will not lead to late pain and deformity. A final absolute kyphosis angle, Kf, from twenty to thirty degrees has been variably regarded as a threshold to obtain a good clinical outcome. Criteria for stability have been previously documented, however variables are based on initial presentation. Aside from careful classification of the fracture type, the current “rule of thumb” prediction model for Kf may further help the clinician with management decisions

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.

Bone remodeling effects is a significant issue in predicting long term stability of hip arthroplasty. It has been frequently observed around the femoral components especially with the implantation of prosthesis stem. Presence of the stiffer materials into the femur has altering the stress distribution and induces changes in the architecture of the bone. Phenomenon of bone resorption and bone thickening are the common reaction in total hip arthroplasty (THA) which leading to stem loosening and instability. The objectives of this study are (i) to develop inhomogeneous model of lower limbs with hip osteoarthritis and THA and (ii) to predict the bone resorption behavior of lower limbs for both cases. Biomechanical evaluations of lower limbs are established using the finite element method in predicting bone remodeling process. Lower limbs CT-based data of 79 years old female with hip osteoarthritis (OA) are used in constructing three dimensional inhomogenous models. The FE model of lower limbs was consisted of sacrum, left and right ilium and both femur shaft. Bond between cartilage, acetabulum and femoral head, sacrum and ilium were assumed to be rigidly connected. The inhomogeneous material properties of the bone are determined from the Hounsfield unit of the CT image using commercial biomedical software. A load case of 60kg body weight was considered and fixed at the distal cut of femoral shaft. For THA lower limbs model, the left femur which suffering for hip OA was cut off and implanted with prosthesis stem. THA implant is designed to be Titanium alloy and Alumina for stem and femoral ball, respectively. Distribution of young modulus of cross-sectional inhomogeneous model is presented in Fig. 2 while model of THA lower limbs also shown in Fig. 2. Higher values of young modulus at the outer part indicate hard or cortical bone. Prediction of bone resorption is discussed with the respect of bone mineral density (BMD). Changes in BMD at initial age to 5 years projection were simulated for hip OA and THA lower limbs models. The results show different pattern of stress distribution and bone mineral density between hip OA lower limbs and THA lower limbs. Stress is defined to be dominant at prosthesis stem while femur experienced less stress and leading to bone resorption. Projection for 5 years follow up shows that the density around the greater tronchanter appears to decrease significantly

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

Computational modelling has the potential of becoming a useful tool in assessing revision risk on a patient-specific basis. However, there are many difficulties encountered in generating subject-specific computational models that have unknown influences on such predictions, e.g. accuracy of the anatomical geometry and material properties of the patient. This study compares the influence of these two patient-specific parameters on predictions of revision risk due to aseptic loosening. First, X-rays from seventeen patients were processed using previously developed technique utilising rigid scaling of a generic femur to match selected dimensions from each patient’s post-operative X-ray and, then, the same set of 3D models was obtained by using an automated technique that generates 3D extra-cortical geometries from planar X-rays using a combination of 2D contour extraction and 3D warping of a generic model to match the extracted contour. A cement and cement-metal interfacial damage accumulation algorithm developed previously was used. For each geometric set two types of simulations were performed. First, constant cortical and cancellous bone apparent Young’s moduli were assumed. A second set of simulations used age-dependent Young’s moduli for each bone type. Walking and stair-climbing activities were simulated. Resultant migration of the prostheses was used to indicate revision risk. Factorial analysis has shown that the geometry has a larger influence on resultant migration magnitude for each case; however, unexpectedly, using more realistic geometry weakened the strength of predictions. This is most likely to be due ongoing mesh-induced contact problems

Introduction. Previous studies have shown that third body damage to the femoral head in metal-on-polyethylene hip replacement bearings can lead to accelerated wear of the polyethylene liners. The resulting damage patterns observed on retrieved metal heads are typically scratches and scrapes. The damage created in vitro must represent the third body damage that occurs clinically. A computational model was developed to predict the acceleration of wear of polyethylene articulating against in vitro damaged femoral heads. This involved using a damage registry from retrieval femoral heads to develop standardized templates of femoral head scratches statistically representative of retrieval damage. The aim of this study was to determine the wear rates of polyethylene liners articulating against retrievals and artificially damaged metal heads for the purpose of validating a computational wear prediction model; and to develop and validate an in vitro standardised femoral head damage protocol for pre-clinical testing of hip replacements. Materials and Methods. Twenty nine, 32mm diameter, metal-on-moderately cross-linked polyethylene bearings (Marathon. TM. ) inserted into Ti-6Al-4V shells (Pinnacle. ®). were tested in this study. All products were manufactured by DePuy Synthes, Warsaw, Indiana, USA. Following a retrieval study seven different damage patterns were defined, and these were applied to the femoral heads using a four-degree-of-freedom CNC milling machine (Figure 1). The ProSim 10-station pneumatic hip joint simulator (Simulation Solutions, UK) was used for experimental wear simulation using standard gait cycles and testing each experimental group for 3 million cycles. The acetabular cups were inclined at 35° on the simulator (equivalent to 45° in vivo). The wear volumes were determined using a microbalance (Mettler-Toledo XP205, Switzerland) at one million cycle intervals. Statistical analysis used was one way ANOVA followed by a post hoc analysis with significance taken at p<0.05. Results. Different damage patterns accelerated the wear of polyethylene at different rates (Figure 2). The moderately scratched and severely scratched heads caused a 2 fold (p<0.01) and 5.5 fold (p<0.01) increase when compared to the wear rate of the undamaged head group. However, the scraped damage caused a lower increase than the scratched heads, with a 1.4 fold (p=0.2) increase for the moderately scraped heads and 2.6 fold (p<0.01) increase for the severely scraped heads. The moderate hybrid and severe hybrid groups resulted in a similar increase to the scraped heads with 1.8 fold (p<0.01) increase with the moderate hybrid and 3 fold (p<0.01) increase with the severe hybrid. The wear of polyethylene against the mild hybrid and retrieved heads was not significantly different (p= 0.9) to the wear against undamaged heads. Discussion. A standardised protocol for generating in vitro damage representative of clinically occurring damage on femoral heads for preclinical testing purposes is needed. The wear rates of polyethylene liners articulating against the retrieval heads were similar to those articulating against the undamaged femoral heads. This study has shown the variations in wear rate of polyethylene bearing under different damage patterns generated in vitro. The wear prediction computational model predict similar trends of the wear acceleration reported in the experimental study

Background: Distinguishing septic arthritis from transient synovitis of the hip in children can be both crucial and challenging. In 1999, Kocher et al suggested four clinical predictors, fever > 38.5°C, inability to weight bear, WBC count > 12.0x109/L, and ESR> 40mm/hr; that, when combined were highly predictive of septic arthritis in children (99.6%). This figure was challenged by Luhmann et al, stating that the clinical prediction did not exceed 59%. In 2006, Caird et al recommended adding CRP of > 20mg/L as a fifth predictor. Aims: To assess the value and accuracy of clinical prediction algorithms in distinguishing septic arthritis from transient synovitis of the hip in children in our hospital. Methods: A retrospective review of all children admitted to our institution with painful hips was carried out over a period of four years (Feb 2003 to Mar 2007). One-hundred and twenty-two admissions (115 patients, 7 re-admissions) were reviewed. Results: 79 patients (64.8%) were males. The mean age was 6 years (9 months to 15 years). 86 patients (70.5%) were diagnosed with transient synovitis. All the 7 re-admissions were from this group. Only one of the re-admissions was diagnosed with confirmed septic arthritis. 4 patients (3.3%) were diagnosed with definite septic arthritis with positive cultures from the hip, and 1 (0.8%) with probable septic arthritis (negative culture). The presence of the clinical predictors was compared between the transient synovitis and septic arthritis groups, using Fisher’s exact test. Only the raised temperature and CRP were found to be significantly different (p< 0.05). Only two children (40%) with confirmed septic arthritis had four or more predictors (one had all five, and the other was able to partially weight bear). The third child had a raised temperature and CRP, and the fourth had a raised temperature only. The fifth patient (20%) was diagnosed with probable septic arthritis. His cultures were negative, but he was already on intravenous antibiotics. This patient did not have any of the predictors on admission (temperature was 38.3°C, CRP 10.7). However, he spiked a temperature of 40°C 24 hours post admission despite being on antibiotics, and his CRP increased to 34.5mg/L. In the transient synovitis group, two patients (2.2%) had positive five predictors, but were proven to have transient synovitis secondary to a urinary tract infection and gastroenteritis. 47 patients (51.6%) did not have any of the predictors, and 6 patients (6.6%) had three or more positive predictors. Conclusion: Although clinical predictors are helpful in distinguishing septic arthritis from transient synovitis, there were false negative and false positive findings in the study. The predictors cannot be considered alone, and ultimately clinical judgement must be exercised to ensure that cases of septic arthritis are not missed

The pathogenesis of scoliosis progression remains poorly understood. Seventy-two subject data sets, consisting of four successive values of Cobb-angle and lateral deviations at apices for six and twelve-months intervals in the coronal plane, were used to train and test an artificial neural network (ANN) to predict spinal deformity progression. The accuracies of the trained ANN (3-4-1) for training and testing data were within 3.64° (±2.58°) and 4.40° (±1.86°) of Cobb angles, and within 3.59 (±3.96) mm and 3.98 (±3.41) mm of lateral deviations, respectively. The adapted technique for predicting the scoliosis deformity progression has promising clinical applications. Scoliosis is a common and poorly understood three-dimensional spinal deformity. The study purpose is to predict scoliosis progression at six and twelve months intervals in the future using successive spinal indices with an artificial neural network (ANN). The adapted ANN technique enables earlier detection of scoliosis progression with high accuracy. Improved prediction of scoliosis progression will impact bracing or surgical treatment decisions, and may decrease hazardous X-ray exposure. Seventy-two data sets from adolescent idiopathic scoliosis subjects recruited at the Alberta Children’s Hospital were used in this study. Data sets composed of four successive values of Cobb angles and lateral deviations at apices for six and twelvemonth intervals (coronal plane) were extracted to train and test a specific ANN for predicting scoliosis progression. Progression patterns in Cobb angles (n = 10) and lateral deviations (n = 8) were successfully identified. The accuracies of the trained ANN (3-4-1) with the training and testing data sets were 3.64° (±2.58°) and 4.40° (±1.86°) of Cobb angles, 3.59 (±3.96) mm and 3.98 (±3.41) mm of lateral deviations, respectively. These results are in close agreement with those using cubic spline extrapolation techniques (3.49° ± 1.85° and 3.31 ± 4.22 mm) and adaptive neuro-fuzzy inference system (3.92° ±3.53° and 3.37 ±3.95 mm) for the same testing data. ANN can be a promising technique for prediction of scoliosis progression with substantial improvements in accuracy over current techniques, leading to potentially important implications for scoliosis monitoring and treatment decisions. Funding: AHFMR, CIHR, Fraternal Order of Eagles, NSERC, GEOIDE

Purpose. Incidence of malrotation of femoral fractures after intramedullary nailing is as high as 28%. Prevention of malrotation is superior to late derotation osteotomy. The lesser trochanter (LT) profile has been in use for some time as a radiographic landmark of femoral rotation. One of the authors has previously described a linear regression model that describes the relationship of the LT to rotation. This paper aims to validate the use of this equation in predicting femoral rotation. Method. A survey was created and circulated online. Twenty images of cadaveric femurs of known rotation were chosen randomly from a large series. Thirty individuals with varying degrees of orthopaedic experience were invited to participate. Participants were asked to take measurements of the LT in a standardized fashion. Inter-observer variation for predicted rotation and the precision of predicted rotation was calculated. Results were grouped into those with the LT readily visible and those with the LT hidden by the femoral shaft. Results. A pilot study found the standard deviation for films with the LT hidden was 10.8 degrees, and only 6.0 degrees for films with the LT visible. The mean difference between the predicted and actual rotation was equally high in both groups (18.3 and 17.3 degrees respectively). Conclusion. Preliminary results found that the LT must be clearly visible to predict femoral rotation. This suggests that the surgeon should place the femur in a neutral or externally rotated position. In a favourable position most predictions were within a 6.0 degree spread, which would be sufficient to prevent a fifteen degree malrotation. Predicted rotation was however not precise enough to prevent a fifteen degree malrotation, regardless of LT visibility. The precision of predicted rotation may be improved by using a non-linear model. Such a model has recently been designed by a group of engineers at the University of Manitoba. The r squared value of the non-linear model was 0.88, in comparison to 0.78 for the linear equation. Precision may be further improved by using the contra-lateral LT for comparison

Introduction. High tibial osteotomy (HTO) is a commonly used surgical technique for treating moderate osteoarthritis (OA) of the medial compartment of the knee by shifting the center of force towards the lateral compartment. The amount of alignment correction to be performed is usually calculated prior to surgery and it's based on the patient's lower limb alignment using long-leg radiographs. While the procedure is generally effective at relieving symptoms, an accurate estimation of change in intraarticular contact pressures and contact surface area has not been developed. Using electromyography (EMG), Meyer et al. attempted to predict intraarticular contact pressures during gait patterns in a patient who had received a cruciate retaining force-measuring tibial prosthesis. Lundberg et al. used data from the Third Grand Challenge Competition to improve contact force predictions in total knee replacement. Mina et al. performed high tibial osteotomy on eight human cadaveric knees with osteochondral defects in the medial compartment. They determined that complete unloading of the medial compartment occurred at between 6° and 10° of valgus, and that contact pressure was similarly distributed between the medial and lateral compartments at alignments of 0° to 4° of valgus. In the current study, we hypothesised that it would be possible to predict the change in intra-articular pressures based on extra-articular data acquisition. Methods. Seven cadavers underwent an HTO procedure with sequential 5º valgus realignment of the leg up to 15º of correction. A previously developed stainless-steel device with integrated load cell was used to axially load the leg. Pressure-sensitive sensors were used to measure intra-articular contact pressures. Intraoperative changes in alignment were monitored in real time using computer navigation. An axial loading force was applied to the leg in the caudal-craneal direction and gradually ramped up from 0 to 550 N. Intra-articular contact pressure (kg) and contact area (mm2) data were collected. Generalised linear models were constructed to estimate the change in contact pressure based on extra-articular force and alignment data. Results. The application of an axial load results in axial angle changes and load distribution changes inside the knee joint. Preliminary analysis has shown that it is possible to predict lateral and medial compartment pressures using externally acquired data. For lateral compartment pressure estimation, the following equation had an R of 0.86: Lateral compartment pressure = −1.26*axial_force + 37.08*horizontal_force − 2.40*vertical_force − 271.66*axial_torque − 32.64*horizontal_torque + 18.98*vertical_torque − 24.97*varusvalgus_angle_change + 86.68*anterecurvature_angle_change − 17.33*axial_angle_change − 26.14. For medial compartment pressure estimation, the following equation had an R2 of 0.86: Medial compartment pressure = −2.95*axial_force −22.93*horizontal_force − 9.48*vertical_force − 34.53*axial_torque + 6.18*horizontal_torque − 127.00*vertical_torque − 110.10*varusvalgus_angle_change − 15.10*anterecurvature_angle_change + 55.00*axial_angle_change + 193.91. Discussion. The most important finding of this study was that intra-articular pressure changes in the knee could be accurately estimated given a set of extra-articular parameters. The results from this study could be helpful in developing more accurate lower limb realignment procedures. This work complements and expands on previous research by other groups aimed at predicting intra-articular pressures and identifying optimal alignment for unloading arthritic defects. A possible clinical application of these findings may involve the application of a predetermined axial force to the leg intra-operatively. Given the estimated output from the predictive equation, one could then perform the opening wedge until the desired estimated intra-articular pressure is achieved. With this method, an arthrotomy and placement of intra-articular pressure sensors would not be needed. This work is not without its limitations. This experiment was performed on cadaveric specimens. Therefore, we cannot directly predict what the pressures would be in a de-ambulating patient. However, these sort of experiments do help us understand the complex biomechanics of the knee in response to alterations in multi-planar alignment. Further in vivo research would be warranted to validate these results. Additionally, given our current experimental setup, only axial loading could be performed for testing. Further experiments involving dynamic motion of the lower limb under load would further help us understand the changes in pressure at difference flexion angles. Continued experiments would help us gather additional data to better understand the relationship between these variables and to construct a more accurate predictive model. In summary, we have established a framework for estimating the change in intra-articular contact pressures based on extra-articular, computer-navigated measurements. Quantifying the resulting changes in load distribution, alignment changes, torque generation and deflection will be essential for generating appropriate algorithms able to estimate joint alignment changes based on applied loads

Distal radius fractures are commonest injury managed by junior doctors in accident and emergency department. Technique of manipulation is very well described and doctors are prepared from the days of medical school. Though manipulation is done in good position at initial management many patients require re-manipulation and surgical stabilisation due to loss of position on subsequent examination. Many Senior surgeon thinks this is due to inadequate plastering and moulding technique. Material and methods: We retrospectively, randomly selected 50 patients from 210 manipulations done in one year at District General Hospital. All these patients x-rays were reviewed and data collected for classification of fracture (Frykmann’s classification), radial height, ulnar varience, radial angulation, and Radial inclination measurements. Three Senior Orthopaedic Surgeons reviewed pre and post manipulation x-rays and asked for acceptability of initial reduction, plaster position and moulding signs on x-rays and asked to predict those requiring re-manipulation or loss of position. Results: 70% of the fractures were frykmann I or II as intra articular fractures Prediction of senior surgeon was right for more than 60 percent of the cases. Average radial angulation was 14 degree on post manipulation films. Radial height and inclination was average 6 mm and 18 degrees respectively. Discussion: Post manipulation is very important factor for maintaining reduction and poor moulding can lead to loss of position and require unnecessary additional operative procedure for initially well reduced fracture. Teaching of Plastering and moulding technique is very important skill development for junior doctors to improve outcome of these simple injuries

Quantitative ultrasound (QUS) is a promising tool to estimate bone structure characteristics and predict fragile fracture. The aim of this pilot cross-sectional study was to evaluate the performance of a multi-channel residual network (MResNet) based on ultrasonic radiofrequency (RF) signal to discriminate fragile fractures retrospectively in postmenopausal women.

Lag screw cut-out is a serious complication of dynamic hip screw fixation in trochanteric hip fractures. Lag screw position is recognised as a crucial factor influencing the occurrence of lag screw cut-out. We propose a modification of the Tip Apex Distance (TAD) and hypothesize that it could enhance the reliability of predicting lag screw cut-out in these injuries.

A retrospective study of hip fracture cases was conducted from January 2018 to July 2022. A total of 109 patients were eligible for the final analysis. The modified TAD was measured in millimetres, based on the sum of the traditional TAD in the lateral view and the net value of two distances in the anteroposterior (AP) view. The first distance is from the lag screw tip to the opposite point on the femoral head along the lag screw axis, while the second distance is from that point to the femoral head apex. The first distance is a positive value, whereas the second distance is positive if the lag screw is superior and negative if it is inferior. Receiver operating characteristic (ROC) curve analysis was used to assess the reliability of various parameters for evaluating the lag screw position within the femoral head.

Factors such as reduction quality, fracture pattern according to the AO/OTA classification, TAD, Calcar-Referenced TAD, Axis Blade Angle, Parker’s ratio in the AP view, Cleveland Zone 1, and modified TAD were statistically associated with lag screw cut-out. Among the tested parameters, the novel parameter exhibited 90.1% sensitivity and 90.9% specificity for predicting lag screw cut-out at a cut-off value of 25 mm, with a p-value < 0.001.

The modified TAD demonstrated the highest reliability in predicting lag screw cut-out. A value of 25 mm may potentially reduce the risk of lag screw cut-out in trochanteric hip fractures.

Aims

This study used an artificial neural network (ANN) model to determine the most important pre- and perioperative variables to predict same-day discharge in patients undergoing total knee arthroplasty (TKA).

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.

Abstract

The aim was to predict the number and incidence of distal radius fractures in Scotland over the next two decades according to age group, categorised into under 65yrs(<65) and 65yrs and older (≥65), and estimate the potential increased operative burden of this.

The number of distal radius fracture in Scotland was isolated from the Global Burden of Disease database and this was used, in addition to historic population data and population estimates, to create a multivariable model allowing incorporation of age group, sex and time. A Negative Binomial distribution was used to predict incidence in 2030 and 2040 and calculate projected number of fractures according to the population at risk. A 20.4% operative intervention rate was assumed in the ≥65 group (local data).

In terms of number of fractures, there was a projected 61% rise in the ≥65 group with an overall increase of 2099 fractures per year from 3417 in 2020 (95% confidence interval (CI) 2960 – 3463) to 5516 in 2040 (95% CI 4155–5675). This was associated with 428 additional operative interventions per year for those ≥65yrs. The projected increase between 2020 and 2040 was similar in both sexes (60% in females, 63% in males), however the absolute increase in fracture number was higher in females (2256 in 2020 [95% CI 1954–2287] to 3620 in 2040 [95% CI 2727–3721]) compared to males (1160 [95% CI 1005–1176] to 1895 [95% CI 1427–1950]). There was a 4% projected fall in the number of fractures in those <65.

Incidence of distal radius fractures is expected to considerably increase over the next two decades due to a projected increase in the number of fractures in the elderly. This has implications for the associated morbidity and healthcare resource use.

Abstract

Objective: To identify institution specific risk factors for developing MRSA surgical site infection (SSI) and develop an objective mechanism to estimate the probability of MRSA infection in a given patient admitted to the orthopaedic unit. Design: A cohort study was performed to identify risk factors in all patients who had MRSA infection during admission on the orthopaedic unit between January 2002 and December 2004. Logistic regression was used to model the likelihood of MRSA. A stepwise approach was employed to derive a model. The MRSA prediction tool was developed from the final model. Results: Of the 11 characteristics included in the logistic regression, the features that strongly predicted a MRSA infection were ASA grade, patient’s residence and reason for admission. 110 had MRSA infection in their surgical wound. 83 of 110 (75.5%) patients were non-elective admissions, of which 49 (60%) were proximal femur fractures. 20% of proximal femur fractures admitted from nursing home and 7.8% from their own homes developed SSI with MRSA. This cohort of SSI with MRSA had an average of 5.7(1–18) previous admissions. 25 (23%) had been previously colonised with MRSA. Majority of them (76%) were between 70–90 years old and were ASA grade 3–4. Conclusion: Through multivariate modelling technique we were able to identify the most important determinants of patients developing SSI with MRSA in our institute and develop a tool to predict the probability of MRSA in a given patient. This knowledge can be used to guide the use of appropriate prophylactic antibiotic and to take other required measures to avoid the SSI with MRSA

The preoperative prediction of gap balance after robotic total knee arthroplasty (TKA) is difficult. The purpose of this study was to evaluate the effectiveness of a new method of achieving balanced flexion-extension gaps during robotic TKA. Fifty one osteoarthritic patients undergoing cruciate retaining TKA using robotic system were included in this prospective study. Preoperative planning was based on the amount of lateral laxity in extension and flexion using varus stress radiograph. After complete milling by the robot and soft tissue balancing, intra-operative extension and flexion gaps were measured using a tensioning device. Knees were subdivided into three groups based on lateral laxities in 0° and 90° of flexion, as follows; the tight extension group (≥ 2mm smaller in extension than flexion laxity), the tight flexion group (≥ 2mm smaller in flexion than extension laxity), and the balanced group (< 2mm difference between laxities). In addition, intra-operative gap balance results were classified as acceptable (0–3mm larger in flexion than in extension), tight (larger in extension than in flexion) or loose (> 3mm larger in flexion than in extension) based on differences between extension and flexion gaps. During preoperative planning, 34 cases were allocated to the balanced group, 16 to the tight extension group and 1 case was allocated to the tight flexion group. Intra-operative gap balance was acceptable in 46 cases, 4 cases had a tight result, and one case had a loose flexion gap. We concluded that preoperative planning based on the amount of lateral laxity determined using varus stress radiographs may be useful for predicting intraoperative gap balance and help to achieve precise gap balance during robotic TKA

Introduction: Current evidence suggests that CES should be operated within 48 hours from onset of sphincteric symptoms in order to maximise chances of recovery. Measurement reproducibility of large disc prolapses and clinical correlations have not previously been studied. Objectives: (1) Determine whether initial MRI findings correlate with clinical outcome (2) Study the reproducibility of MRI measurements of large disc prolapses (3) Estimate the ability to predict CES based on MRI alone. Study Design: 31 patients with CES were identified, the case notes reviewed and the patients invited to attend clinic. Outcome consisted of history and examination, and several validated questionnaire assessments. 19 patients who underwent discectomy for persistent radiculopathy were identified. None had sphincteric symptoms. All had a significant surgical target. Digital photographs of all 50 MRIs were obtained showing the T2 mid-sagittal image and the axial image with the greatest disc protrusion. The Observers: 1 Consultant Radiologist, 2 Consultant Spinal Surgeons and 1 SHO did not know the number of patients in each group. Observers estimated the percentage spinal canal compromise on each view and indicated whether they thought the scan findings could produce CES. Measurements were repeated after 2 weeks. Results: 26 patients attended clinic mean follow up 51 months (25 to 97). As expected, the % canal compromise differed significantly between the two groups (p0.001). 12 of the 26 patients with CES had, on average, over75% canal compromise. No significant correlations were found between MRI canal compromise and clinical outcome. Canal compromise did predict whether the patient would fail their Trial Without Catheter (p0.05). Based on MRI alone, the correct identification of CES has sensitivity 68%, specificity 78%, positive predictive value 84% and negative predictive value 58%. Kappa values for intra-observer reproducibility ranged from 0.4 to 0.85 for sagittal compromise, axial compromise and correct prediction of CES. All three interobserver kappa values for these measurements were 0.64. Conclusions: This is the largest radiological case series of CES with 4 years clinical follow up. Canal compromise on MRI does not appear to directly predict clinical outcome. Reproducibility of MRI measurements of large disc protrusions has substantial agreement. MRI could be of help in equivocal cases if the scan shows a large disc

Abstract

Study Design: Prospective Cohort Study. Summary of Background Data: It has previously been suggested that fulcrum bending radiographs (Cheung et al Luk 1997) and traction radiography under anaesthetic (Davis et al 2003) predict the flexibility and correction obtained following surgery better than conventional supine bending radiographs. Objective: To compare fulcrum bending radiographs and traction radiographs for the prediction of surgical correction of idiopathic scoliosis. Subjects: The study was based on 16 patients with a diagnosis of idiopathic scoliosis who underwent corrective surgery. Outcome measures: The Cobb angle of the major curve was compared on the standing AP and fulcrum bending radiograph taken in the pre-op assessment clinic, the traction film undertaken under anaesthetic immediately prior to surgery and the first post operative standing radiograph taken. The post operative correction of the major curve was analysed using regression techniques and adjusted for the base line curve angle of the major curve. Results: The results were presented as an estimate of the parameter coefficient in the model associated with 95% confidence intervals. The median pre-operative Cobb angle of the major curve was 69 degrees, on the fulcrum bending film was 47 degrees, on the traction film was 30 degrees, and on the first post operative film was 30 degrees. There was no evidence to suggest that the fulcrum Cobb had an effect on the post operative correction of the major curve. There was however evidence to suggest that the traction Cobb angle had an effect on the post operative correction of the major curve (parameter estimate 0.87) 95% CI (0.174, 1.399), T value = 2.83, P = 0.016. Conclusion: Traction radiographs under anaesthetic better predict the surgical correction obtained in adolescent idiopathic scoliosis compared to fulcrum bending radiographs. These two techniques have not been directly compared before

Aims

The purpose of this study was to develop a prognostic model for predicting survival of patients undergoing surgery owing to metastatic bone disease (MBD) in the appendicular skeleton.

Introduction

Total Hip Arthroplasty (THA) devices are now increasingly subjected to a progressively greater range of kinematic and loading regimes from substantially younger and more active patients. In the interest of ensuring adequate THA solutions for all patient groups, THA polyethylene acetabular liner (PE Liner) wear representative of younger, heavier, and more active patients (referred to as HA in this study) warrants further understanding.

Previous studies have investigated HA joint related morbidity [1]. Current or past rugby players are more likely to report osteoarthritis, osteoporosis, and joint replacement than a general population.

This investigation aimed to provide a preliminary understanding of HA patient specific PE liner tribological performance during Standard Walking (SW) gait in comparison to IS0:14242-1:2014 standardized testing.

Hip displacement, defined in this study as a migration percentage (MP) of more than 40%, is a common, debilitating complication of cerebral palsy (CP). In this prospective study we analysed the risk of developing hip displacement within five years of the first pelvic radiograph.

All children with CP in southern and western Sweden are invited to register in the hip surveillance programme CPUP. Inclusion criteria for the two groups in this study were children from the CPUP database born between 1994 and 2009 with Gross Motor Function Classification System (GMFCS) III to V. Group 1 included children who developed hip displacement, group 2 included children who did not develop hip displacement over a minimum follow-up of five years. A total of 145 children were included with a mean age at their initial pelvic radiograph of 3.5 years (0.6 to 9.7).

The odds ratio for hip displacement was calculated for GMFCS-level, age and initial MP and head-shaft angle. A risk score was constructed with these variables using multiple logistic regression analysis. The predictive ability of the risk score was evaluated using the area under the receiver operating characteristics curve (AUC).

All variables had a significant effect on the risk of a MP > 40%. The discriminatory accuracy of the CPUP hip score is high (AUC = 0.87), indicating a high ability to differentiate between high- and low-risk individuals for hip displacement. The CPUP hip score may be useful in deciding on further follow-up and treatment in children with CP.

Cite this article: Bone Joint J 2015;97-B:1441–4.

The study is to evaluate mid-term follow-up clinical results and navigation prediction of the first 106 TKAs, which was performed based on the soft tissue balancing technique using the OrthoPilot navigation system (B.Braun Aesculap, Tuttlingen, Germany). All the 106 cases were diagnosed as osteoarthritis with varus deformity. After anatomical and kinematic registration, the mechanical axis was restored to neutral (±2°) at full extension with step by step meticulous medial soft tissue release and osteophyte removal. Proximal tibial bone cutting was performed under real-time navigation system control. Flexion and extension gaps were measured at full extension and at 90° of flexion using a tensioning device (V-STAT tensor, Zimmer) and a special torque wrench set at 50lb/inch before femoral bone cutting. The flexion and extension gap was evaluated and it’s difference was classified into 3 kinds; balanced, tight flexion gap and tight extension gap. Sixty-one (57.5%) knees were classified as having a ‘balanced gap’ (meaning that flexion and extension gaps were within 2 mm), 20 (18.9%) knees as having a ‘tight flexion gap’ (an extension gap at least 3mm more that the corresponding flexion gap), and 25 (23.6%) knees as having a ‘tight extension gap’ (a flexion gap at least 3mm more that the corresponding extension gap). Depending extension/flexion, and medial/lateral gap difference, the level of distal femoral cut and the rotation of femoral component was determined. Following the final bone cuts and completion of soft tissue release, assessment of the flexion and extension gap was repeated. Balanced flexion and extension gap (difference between flexion and extension gap ≤ 3mm) was confirmed in 99 cases (94%). A mobile bearing prosthesis (e motion FP, B.Braun Aesculap) was used. One patient (bilateral TKAs) died of unrelated causes at postoperative 2 year. One knee was revised due to infection. One hundred three cases were followed up at least more than 4 years, 53 months in average. Overall survival rate is 97%. Average preoperative HHS scores and range of motion (ROM) were 65.4 points (range, 33~82) and 126.8 degrees (80~140). At the last follow-up, HHS score and ROM were 95.0 points (78~100) and 131.4 degrees (110~140). Statistically significant improvement in HHS score and ROM were observed (p< 0.05). The mean mechanical axis was 179.44±1.83° (175~184°) with 8 cases of outliers (more than ±3° of optimum). There was no radiolucency, osteolysis, subsidence, or loosening at the last follow-up. In conclusion, navigation is an excellent predictor for achieving balanced soft tissue & flexion-extension gap in primary total knee arthroplasty. Navigated TKAs using soft tissue balancing technique showed excellent clinical results and is effective methods achieving accurate mechanical axis and reducing prosthetic alignment outlier

Background: Many studies have investigated predictors of outcome in low back pain (LBP) patients, including the role of coping. However, the relative importance of different coping strategies is unclear. Objective: To systematically review prospective cohort studies to determine the role of specific coping strategies in the prediction of LBP outcome in primary care settings. Methods: Medline, PsychINFO, and Embase electronic databases were searched from inception to April 2008. Prospective cohort studies focusing on aspects of coping with LBP in settings relevant to primary care were included. Studies were excluded if they focused on specific populations (e.g. nursing staff) or patients aged under 18 years, or if they were not published in English. Prior to data extraction, studies were quality assessed and coping strategies were conceptualised as either cognitive or behavioural. Results: 782 potentially relevant articles were identified, of which 34 were included with an average follow-up of 10 months. There was considerable variability in the prognostic risk factors measured. Fear avoidance beliefs were most frequently associated with outcome. Negative affect, anxiety and depression, use of active or passive coping strategies, and catastrophizing were also commonly associated with outcome. Behavioural coping strategies were measured by only 5 of the 34 studies. Conclusion: Despite considerable heterogeneity, most studies were of acceptable quality, enabling the identification of several key coping strategies predictive of LBP outcome. However, the majority of studies focused on cognitive coping factors only. Therefore further research is needed, particularly to investigate the influence of behavioural coping strategies on LBP outcome

We suggest that different mechanisms underlie joint pain at rest and on movement in osteoarthritis and that separate assessment of these two features with a visual analogue scale (VAS) offers better information about the likely effect of a total knee replacement (TKR) on pain. The risk of persistent pain after TKR may relate to the degree of central sensitisation before surgery, which might be assessed by determining the pain threshold to an electrical stimulus created by a special tool, the Pain Matcher. Assessments were performed in 69 patients scheduled for TKR. At 18 months after operation, separate assessment of pain at rest and with movement was again carried out using a VAS in order to enable comparison of pre- and post-operative measurements. A less favourable outcome in terms of pain relief was observed for patients with a high pre-operative VAS score for pain at rest and a low pain threshold, both features which may reflect a central sensitisation mechanism.

The development of an algorithm that provides accurate individualised estimates of revision risk could help patients make informed surgical treatment choices. This requires building a survival model based on fixed and modifiable risk factors that predict outcome at the individual level. Here we compare different survival models for predicting prosthesis survivorship after hip replacement for osteoarthritis using data from the National Joint Registry for England, Wales, Northern Ireland and the Isle of Man.

In this comparative study we implemented parametric and flexible parametric (FP) methods and random survival forests (RSF). The overall performance of the parametric models was compared using Akaike information criterion (AIC). The preferred parametric model and the RSF algorithm were further compared in terms of the Brier score, concordance index (C index) and calibration.

The dataset contains 327 238 hip replacements for osteoarthritis carried out in England and Wales between 2003 and 2015. The AIC value for the FP model was the lowest. The averages of survival probability estimates were in good agreement with the observed values for the FP model and the RSF algorithm. The integrated Brier score of the FP model and the RSF approach over 10 years were similar: 0.011 (95% confidence interval: 0.011–0.011). The C index of the FP model at 10 years was 59.4% (95% confidence interval: 59.4%–59.4%). This was 56.2% (56.1%–56.3%) for the RSF method.

The FP model outperformed other commonly used survival models across chosen validation criteria. However, it does not provide high discriminatory power at the individual level. Models with more comprehensive risk adjustment may provide additional insights for individual risk.

Aims

Our aim was to investigate the predictive factors for the development of a rebound phenomenon after temporary hemiepiphysiodesis in children with genu valgum.

INTRODUCTION

There is great potential for the use of computational tools within the design and test cycle for joint replacement devices.

The increasing need for stratified treatments that are more relevant to specific patients, and implant testing under more realistic, less idealised, conditions, will progressively increase the pre-clinical experimental testing work load. If the outcomes of experimental tests can be predicted using low cost computational tools, then these tools can be embedded early in the design cycle, e.g. benchmarking various design concepts, optimising component geometrical features and virtually predicting factors affecting the implant performance. Rapid, predictive tools could also allow population-stratified scenario testing at an early design stage, resulting in devices which are better suited to a patient-specific approach to treatment.

The aim of the current study was to demonstrate the ability of a rapid computational analysis tool to predict the behaviour of a total hip replacement (THR) device, specifically the risk of edge loading due to separation under experimental conditions.

Introduction

The development of an algorithm that provides accurate individualised estimates of revision risk could help patients make informed surgical treatment choices. This requires building a survival model based on fixed and modifiable risk factors that predict outcome at the individual level. Here we compare different survival models for predicting prosthesis survivorship after hip replacement for osteoarthritis using data from the National Joint Registry for England, Wales, Northern Ireland and the Isle of Man (NJR).

Aims

Our aim was to examine the Elixhauser and Charlson comorbidity indices, based on administrative data available before surgery, and to establish their predictive value for mortality for patients who underwent hip arthroplasty in the management of a femoral neck fracture.

Cervical spinal arthrodesis is the standard of care for the treatment of spinal diseases induced neck pain. However, adjacent segment disease (ASD) is the primary postoperative complication, which draws great concerns. At present, controversy still exists for the etiology of ASD. Knowledge of cervical spinal loading pattern after cervical spinal arthrodesis is proposed to be the key to answer these questions. Musculoskeletal (MSK) multi-body dynamics (MBD) models have an opportunity to obtain spinal loading that is very difficult to directly measure in vivo.

In present study, a previously validated cervical spine MSK MBD model was developed for simulating cervical spine after single-level anterior arthrodesis at C5-C6 disc level. In this cervical spine model, postoperative sagittal alignment and spine rhythms of each disc level, different from normal healthy subject, were both taken into account. Moreover, the biomechanical properties of facet joints of adjacent levels after anterior arthrodesis were modified according to the experimental results. Dynamic full range of motion (ROM) flexion/extension simulation was performed, where the motion data after arthrodesis was derived from published in-vivo kinematic observations. Meanwhile, the full ROM flexion/extension of normal subject was also simulated by the generic cervical spine model for comparative purpose. The intervertebral compressive and shear forces and loading-sharing distribution (the proportions of intervertebral compressive and shear force and facet joint force) at adjacent levels (C3-C4, C4-C5 and C6-C7 disc levels) were then predicted.

By comparison, arthrodesis led to a significant increase of adjacent intervertebral compressive force during the head extension movement. Postoperative intervertebral compressive forces at adjacent levels increased by approximate 20% at the later stage of the head extension movement. However, there was no obvious alteration in adjacent intervertebral compressive force, during the head flexion movement. For the intervertebral shear forces in the anterior-posterior direction, no significant differences were found between the arthrodesis subject and normal subject, during the head flexion/extension movement. Meanwhile, cervical spinal loading-sharing distribution after anterior arthrodesis was altered compared with the normal subject's distribution, during the head extension movement. In the postoperative loading-sharing distribution, the percentage of intervertebral disc forces was further increased as the motion angle increased, compared with normal subject.

In conclusion, cervical spinal loading after anterior arthrodesis was significantly increased at adjacent levels, during the head extension movement. Cervical spine musculoskeletal MBD model provides an attempt to comprehend postoperative ASD after anterior arthrodesis from a biomechanical perspective.

Background

It is not always clear why some patients experience recurrent dislocation following total hip arthroplasty (THA). In order to plan appropriate revision surgery for such patients, however, it is important to understand the specific biomechanical basis for the dislocation. We have developed a novel method to analyse the biomechanical profile of the THA, specifically to identify edge loading and prosthetic impingement, taking into account spinopelvic mobility. In this study we compare the results of this analysis in THA patients with and without recurrent dislocation.

Introduction

Pseudotumors have been reported following metal-on-metal total hip arthroplasty (THA); however, the natural history and longitudinal imaging findings of pseudotumors have yet to be fully analyzed. Our hypothesis was that pseudotumor size might change over time following metal-on-metal THA. This hypothesis was studied longitudinally using magnetic resonance imaging (MRI).

A durable biological fixation between implant and bone depends largely on the micro-motions [Pilliar et al., 1986]. Finite element analysis (FEA) is a numerical tool to calculate micro-motions during physiological loading. However, micromotions can be simulated and calculated in various ways. Generally, only a single peak force of an activity is applied, but it is also possible to apply discretized loads occurring during a continuous activity, offering the opportunity to analyze incremental micro-motions as well. Moreover, micro-motions are affected by the initial press-fit. We therefore aimed to evaluate the effect of different loading conditions and calculation methods on the micro-motions of an uncemented femoral knee component, while varying the interference-fit.

We created an FE model of a distal femur based on calibrated CT-scans. A Sigma® Cruciate-Retaining Porocoat® (DePuy Synthes, Leeds, UK) was placed following the surgical instructions. A range of interference-fits (0–100 µm) was applied, while other contact parameters were kept unchanged. Micro-motions were calculated by tracking the projection of implant nodes onto the bone surface. We defined three different micro-motions measures: micro-motions between consecutive increments of a full loading cycle (incremental), micro-motions for each increment relative to the initial position (reference), and the largest distance between projected displacements, occurring during a discretized full cycle (resulting) (Fig. 1A). Four consecutive cycles of normal gait and squat movements were applied, in different configurations. In the first configuration, incremental tibiofemoral and patellofemoral contact forces were applied, which were derived from Orthoload database using inverse dynamics [Fitzpatrick et al., 2012]. Secondly, we applied the same loads without the patellofemoral force, which is often used in experimental set-ups. Finally, only the peak tibiofemoral force was applied, as a single loading instance. We calculated the average of micro-motions of all nodes per increment to compare different calculation techniques. The percentage of area with resulting micro-motions less than 5 µm was also calculated.

The percentage of surface area was increased non-linearly when the interference fit changed from 0 to 100 µm particularly for squat movement. Tracking nodes over multiple cycles showed implant migration with interference-fits lower than 30µm (Fig. 1A). Loading configurations without the patellofemoral force, and with only the peak tibiofemoral force slightly overestimated and underestimated the resulting micro-motions of squat movement, respectively; although, the effect was less obvious for the gait simulation when no patella force was applied. Both incremental and reference micro-motions underestimated the resulting micro-motions (Fig. 1B). Interestingly, the reference micro-motions followed the pattern of the tibiofemoral contact force (Fig. 1B).

The calculation technique has a substantial effect on the micro-motions, which means there is a room for interpretation of micro-motions analyses. This furthermore stresses the importance of validation of the predicted micro-motions against experimental set-ups. In addition, the minor effect of loading configurations indicates that a simplified loading condition using only the peak tibiofemoral force is suitable for experimental studies. From a clinical perspective, the migration pattern of femoral components implanted with a low interference fit stresses the role of an adequate surgical technique, to obtain a good initial stability.

At the 2010 Effort congress Prof Hernigou of France stated that you never need to template knee x-rays as there is an absolute association with patients height and implant size. Templating of the knee for size is seldom done in clinical practice but could be handy when doing revision surgery where normal anatomy has been lost. This is however difficult with digital x-rays due to enlargement problems.

With this in mind we retrospectively looked at the size of knee implants inserted to see if there was any relation with patient's height and also to see if this differs in male and female patients.

This study demonstrates a significant correlation between the American Knee Society (AKS) Clinical Rating System and the Oxford Knee Score (OKS) and provides a validated prediction tool to estimate score conversion.

A total of 1022 patients were prospectively clinically assessed five years after TKR and completed AKS assessments and an OKS questionnaire. Multivariate regression analysis demonstrated significant correlations between OKS and the AKS knee and function scores but a stronger correlation (r = 0.68, p < 0.001) when using the sum of the AKS knee and function scores. Addition of body mass index and age (other statistically significant predictors of OKS) to the algorithm did not significantly increase the predictive value.

The simple regression model was used to predict the OKS in a group of 236 patients who were clinically assessed nine to ten years after TKR using the AKS system. The predicted OKS was compared with actual OKS in the second group. Intra-class correlation demonstrated excellent reliability (r = 0.81, 95% confidence intervals 0.75 to 0.85) for the combined knee and function score when used to predict OKS.

Our findings will facilitate comparison of outcome data from studies and registries using either the OKS or the AKS scores and may also be of value for those undertaking meta-analyses and systematic reviews.

Cite this article: Bone Joint J 2015;97-B:503–9.

Introduction: Pain is one of the most important outcome measures that contributes to patient dissatisfaction following total knee arthroplasty (TKA) and unexplained pain poses a difficult problem to manage. This paper focuses on a group of patients with unexplained knee pain post arthroplasty to identify any predictors of a poor pain outcome.

Methods: A prospective study of 622 primary TKAs performed on 512 patients using cemented press fit condylar prosthesis was the basis to examine a group of patients that reported moderate or severe pain at 5 years. Demographic and operative variables as well as American Knee Society Scores were collected prospectively. Data was available for 462 knees at 5 years. After exclusion of patients with mild pain, two groups were generated; 374 with no pain and 28 with moderate or severe unexplained pain. Univariate linear analysis was performed to identify possible predictors of poor outcome and this was further refined using multiple regression analysis to remove the effect of confounding factors.

Results: Comparison of the pain and no pain group found the following to be significant predictors of poor outcome: Staged approach to bilateral disease when compared to simultaneous bilateral surgery (13% vs 2%, P< 0.01), age below 60 (17% vs 7%, P< 0.01) and performing lateral release (13% vs 5%, P< 0.01). Other factors which had no predictive effect were gender, body mass index, operating surgeon, patella component, instability and range of motion.

Conclusions: Avoiding surgery in patients aged below 60 and performing simultaneous bilateral TKA instead of a staged approach to bilateral disease, should aid selection of patients for improved outcome in terms of pain. Good surgical technique to avoid lateral release is also recommended to improve outcome.

The management of displaced forearm diaphyseal fractures in adults is predominantly operative. Anatomical reduction is necessary to infer optimal motion and strength. The authors have observed an intraoperative technique where passive pronosupination is examined to assess quality of reduction as a surrogate marker for active movement.

We aimed to assess the value of this technique, but intentionally malreducing a simulated diaphyseal fracture of a radius in a cadaveric model, and measuring the effect on pronosupination.

A single cadaveric arm was prepared and pronation/supination was examined according to American Academy of Orthopaedic Surgeons guidance. A Henry approach was then performed and a transverse osteotomy achieved in the radial diaphysis. A volar locking plate was used to hold the radius in progressive amounts of translation and rotation, with pronosupaintion measured with a goniometer.

The radius could be grossly malreduced with no effect on pronation and supination until the extremes of deformity. The forearm showed more tolerance with rotational malreduction than translation. Passive pronation was more sensitive for malreduction than supination.

The use of passive pronosupination to assess quality of reduction is misleading.

The Mangled Extremity Severity Score was applied to 152 patients with severely injured lower limbs. All cases with a score of seven or more required amputation; some with scores of less than seven eventually came to amputation. These observations are discussed.

A prospective study of factors which might help to predict mortality in patients with intracapsular fractures of the femoral neck has been undertaken. A multivariate analysis technique was used to analyse the collected data, and it was found that mental ability was the most significant variable; this factor had the greatest effect on outcome.

Background: Calcaneonavicular coalitions (CNC) have been reported to be associated with anatomical aberrations of either the calcaneus and/or navicular bones. These morphological abnormalities may complicate accurate surgical resection. Three-dimensional analysis of spatial orientation and morphological characteristics may help in preoperative planning of resection.

Materials and Methods: Sixteen feet diagnosed with CNC were evaluated by means of 3D CT modeling. Three angles were defined that were expressed in relation to one reproducible landmark (lateral border of the calcaneus): the dorsoplantar inclination, anteroposterior inclination and socket angle. The contact surface area was determined from the depth and width of the coalitions. Three-dimensional reconstructions of the calcanei evaluated the presence and morphology of the anterior calcaneal facet and of a navicular beak. The inter-observer correlations were assessed for the accuracy of the measurement methods. Sixteen normal feet were used as controls for comparison of the socket angle and anatomy of the anterior calcaneal facet and of the navicular beak.

Results: The dorsoplantar inclination angle averaged 50° (±17), the anteroposterior inclination angle 64° (±15), and the pathologic socket angle 98° (±11). The average contact area was 156mm2. Ninety-four percent of all patients in the CNC group revealed a plantar navicular beak. In 50% of those patients the anterior calcaneal facet was replaced by the navicular portion and in 44% the facet was totally missing. In contrast, the socket angle in the control group averaged 77° (± 18), which was found to be statistically different than the CNC group (p=0.0004). Only 25% of the patients in the control group had a plantar navicular beak. Statistically significant inter-observer correlations were found for all measured angles.

Conclusions: Computer aided CT analysis and reconstructions help to determine the spatial orientations of CNC and provide useful information in order to anticipate morphological abnormalities of the calcaneus and navicular.

Background

Wear and fatigue damage to polyethylene components remain major factors leading to complications after total knee and unicompartmental arthroplasty. A number of wear simulations have been reported using mechanical test equipment as well as computer models. Computational models of knee wear have generally not replicated experimental wear under diverse conditions. This is partly because of the complexity of quantifying the effect of cross-shear at the articular interface and partly because the results of pin-on-disk experiments cannot be extrapolated to total knee arthroplasty wear. Our premise is that diverse experimental knee wear simulation studies are needed to generate validated computational models. We combined five experimental wear simulation studies to develop and validate a finite-element model that accurately predicted polyethylene wear in high and low crosslinked polyethylene, mobile and fixed bearing, and unicompartmental (UKA) and tricompartmental knee arthroplasty (TKA).

A prospective study was carried out to determine if recognised histological features seen at surgery could help predict those rotator cuff tendon repairs which re-ruptured. 40 rotator cuff tendon edge specimens from 40 patients’ shoulders were analysed histologically following routine mini-open rotator cuff repair. 32/40 underwent Ultrasonography, at a mean time of 35 months post-operatively, to determine repair integrity. The histological features seen at surgery were then compared to the repair integrity of the tendon from which it had been taken. Rotator cuff repairs that remained intact demonstrated a greater reparative response, in terms of increased fibrobast cellularity, cell proliferation and a thickened synovial membrane, than those repairs which reruptured. Larger tears which remained intact showed a higher degree of vasacularity and a significant inflammatory component than those that re-ruptured. Good tissue quality at the time of surgery allows the repair the best chance of remaining intact despite the size of the lesion. Routine histological analysis of the tissue biopsy, preformed in the post-operatively, can now aid the clinician in terms of early management and repair prognosis.

Modern hip implants feature a modular design, whereby the individual components of the implant are assembled during the surgery. Increased reported failure rates associated with the utilization of modular junctions have raised many clinical concerns about the increased release of metal ions/debris leading to adverse local tissue reactions. Implant materials are subject to a myriad of mechanical motion and forces, and varying electrochemical conditions and pH changes from the surrounding environment. To date, no studies have attempted to model the collected data in order to predict the performance of the materials so that precautions can be taken before the problem reaches the critical stage. This study reports the effects of pH variation, displacement variation, and load variation on the mechanical and corrosion behavior of the hip implant modular junction system, tested with a custom-built fretting-corrosion apparatus. The main objective of this study is to combine the complete data set of the in-vitro experiments to create fretting-corrosion wear maps that can predict the dangerous domains of the hip implant modular system.

For each test, the flat portions of two CoCrMo pins were loaded perpendicularly against a Ti6Al4V Rod (Ti alloy) in a Flat-on-flat configuration in a simulated synovial fluid in order to simulate the modular hip implant system. A schematic diagram of contact conditions is presented in Figure 1. A sinusoidal displacement was applied onto the rod, which articulated against the CoCrMo alloy pins, at a frequency of 1Hz. The experiential data from the fretting-corrosion tests has been used to create fretting-corrosion maps. The variables incorporated into the maps include: total mass loss, electrochemical destabilization, pH variation, load variation, displacement variation, and visual examination of the wear features of the contact zone. Total mass loss has been estimated via measurement of the simulator fluid by ICP-MS technique. Electrochemical destabilization was evaluated by a single parameter (V_Drop). The electrochemical destabilization of the tribosystem was evaluated by measuring the drop in potential, V_Drop (V vs. SCE), resultant from the initiation of the fretting phase. The V_Drop refers to the initial cathodic drop in potential in response to the initial onset of fretting motion.

The data from the in vitro fretting-corrosion experiments has been combined to create four fretting-corrosion maps (Figures 2A–3D). Partial slip wear features and mechanical behavior was observed at 25µm displacement. 25–150µm displacement amplitudes showed gross slip behavior. Anything larger than 150µm displayed wear features that were indistinguishable from sliding wear. In general, total mass loss and V_Drop increased with increasing displacement. Samples that were tested at pH 6.0 or higher showed signs of material transfer and higher V_Drop. Finally, there was a general decrease in V_Dropwith increased applied load and pH.

In general, the wears maps were able to offer some predictive validity, however, there were some discrepancies between visual observations and the observed damage parameters. It is possible that other parameters could offer better correlation. Future studies will be conducted to measure other parameters.

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Purpose: To evaluate the effect of the presence of femur fracture on mortality, pulmonary complications, and ARDS in trauma patients. In addition, we aim to compare the effects of other major musculoskeletal injuries to femur fractures on these outcomes.

Method: We retrospectively reviewed the trauma registry of two tertiary level trauma centers for a period of 12 years (1995–2007). We evaluated data points on all patients: gender, age, AIS scores, GCS, SBP, and ICD-9 codes for femur fractures and other major orthopaedic injuries. Outcome measures were death in hospital and occurrence of a pulmonary complication (Adult respiratory distress syndrome, fat embolism syndrome, pneumonia and respiratory failure) and ARDS as a sub-group. Logistic regression was used to evaluate the effect of these variables and the presence of femur fracture on the three outcomes (death, pulmonary complications, and ARDS). The effect of other major orthopaedic injuries in these models was also compared to the effect of femur fractures.

Results: There were 83, 349 patients, with 3, 433 deaths, evaluated in the initial regression models. Gender, GCS < 8, age> 60, blood pressure < 90, 4 AIS scores and femur fracture were all independent predictors of mortality. The strongest predictors of mortality were GCS < 8 (OR 16.976, 95% CI 15.176–18.990) and SBP < 90 (OR 6.835, 95% CI 6.046– 7.726). Femur fracture was an independent predictor of mortality (OR 1.480 95% CI 1.135 – 1.929). The presence of femur fracture was not a statistically significant independent predictor of pulmonary complication (OR 1.29, 95% CI 0.911–1.766) while gender, GCS, and 5 of 6 AIS scores were. Other musculoskeletal injuries were significant predictors, including pelvic ring fractures and spinal fractures. In the ARDS regression model, femur fractures were not an independent predictor (OR 1.127, 95% 0.636–1.999).

Conclusion: The risk of mortality and pulmonary complications is multifactorial; most affected by age, GCS at presentation, SBP at presentation, gender and injury severity. In this study, the presence of a femur fracture does independently increase the risk of death, but not ARDS or other pulmonary complications. There are other musculoskeletal injuries that have a greater effect on mortality and pulmonary complications.

Introduction: Prophylactic pinning of an asymptomatic hip in SCFE is controversial. Bone age has been used as evidence of future contralateral slip risk and used as an indication for such intervention. The efficacy of bone age assessment at predicting contralateral slip was tested in this study.

Patients and Methods: 18 Caucasian children prospectively had bone age assessment using wrist and hand x-rays when presenting with a unilateral SCFE. Patients and parents were informed about the chance of contralateral slip and risks of prophylactic fixation, and advised to attend hospital immediately on development of symptoms in contralateral hip. After in-situ fixation of the affected side prospective monitoring in outpatient department was performed. Surgical intervention was undertaken if the contralateral hip was symptomatic.

Results: Three children (2 boys) went on to develop to a contralateral slip at a mean of 20 months from initial presentation. 6 children (5 boys) were deemed at risk of contralateral slip due to a bone age below 12.5 years for boys and 10.5 for girls. Only one from this group developed a contralateral slip. The relative risk of proceeding to contralateral slip when the bone age is below the designated values was 1 (95% confidence interval of 0.1118 to 8.95).

Conclusion: Delayed bone age by itself is not a good predictor of future contralateral slip at initial presentation. Routine prophylactic pinning is not justified based on bone age alone, with the risks of surgical fixation it carries. Prospective long term longitudinal study is required.