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

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. 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. 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

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

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

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

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

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.

There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for various aspects of Trauma & Orthopaedic surgery. However, correct interpretation of these models is difficult for those without specific knowledge of computing or health data science methodology. Lack of current reporting standards leads to the potential for significant heterogeneity in the design and quality of published studies. We provide an overview of machine-learning techniques for the lay individual, including key terminology and best practice reporting guidelines.

Cite this article: Bone Joint J 2021;103-B(12):1754–1758.

Aims

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).

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.

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.

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.

Aims

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

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.

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.

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.

We measured the proximal migration of 265 acetabular cups over seven years and correlated the findings with clinical outcome and acetabular revision for aseptic loosening. Cups which eventually became aseptically loose were shown to migrate more rapidly than successful cups. The average proximal migration at two years postoperatively for four groups of cups showed a monotonic relationship to the acetabular revision rate for aseptic loosening at 6.5 years. We conclude that acetabular cups which develop aseptic loosening as evidenced by pain, revision or screw fracture show increased proximal migration by one year, and that the 'migration rate' at two years can be used to predict the acetabular revision rate from aseptic loosening at 6.5 years.

The uptake of 99mTc-MDP was studied in 73 patients after a tibial fracture. The image obtained five minutes after injection during a period between one and four weeks after fracture was found to be related to the incidence of non-union after six months. A ratio of 1.3 between the uptake at the fracture site and at normal bone adjacent to it predicted non-union in an individual patient with a sensitivity of about 70% and a specificity of 90%.

We have studied the correlation between the prevention of progressive collapse and the ratio of the intact articular surface of the femoral head, after transtrochanteric rotational osteotomy for osteonecrosis. We used probit analysis on 125 hips in order to assess the ratio necessary to prevent progressive radiological collapse over a ten-year period. The results show that a minimum postoperative intact ratio of 34% was required. This critical ratio may be useful for surgical planning and in assessing the natural history of the condition.

We report a prospective study of 232 consecutive patients with hip fractures. All were over 64 years of age and living independently before admission to a geriatric orthopaedic ward. We assessed the value, at admission, of predicting factors for independent living at one year after injury.

The most important factors were: (1) preinjury function in activities of daily living (grade A or B on the Katz et al (1963) scale); (2) absence of other medical conditions which would impair rehabilitation; and (3) cognitive function better than 7 on the Pfeiffer (1975) mental questionnaire. The odds ratios (95% CI) for these three predictors were 3.5 (1.3 to 9.1), 2.9 (1.3 to 6.1) and 2.4 (1.9 to 4.9), respectively. When all predictors were positive at admission, 92% were living independently at one year; with one, two or three negative predictors, the percentages living independently were 76, 61 and 27, respectively.

The median values of the total number of days in hospital, irrespective of diagnosis, during the first year were 12, 24, 29 and 149 days for the four groups. The mortality at one year was predictable on admission only by the number of medical conditions: with no other diagnosis than the fracture the mortality was 0%; with one or two additional conditions the mortality was 14%; and with three or more additional diagnoses it was 24%.

These simple and robust predictors can be used to optimise resources for rehabilitation.

We compared a group of 46 somatised patients with a control group of 41 non-somatised patients who had undergone elective surgery to the lumbar spine in an attempt to identify pre-operative factors which could predict the outcome. In a prospective single-centre study, the Distress and Risk Assessment method consisting of a modified somatic perception questionnaire and modified Zung depression index was used pre-operatively to identify somatised patients. The type and number of consultations were correlated with functional indicators of outcome, such as the Oswestry disability index and a visual analogue score for pain in the leg after follow-up for six and 12 months.

Similar improvements in the Oswestry disability index were found in the somatised and non-somatised groups. Somatised patients who had a good outcome on the Oswestry disability index had an increased number of orthopaedic consultations (50 of 83 patients (60%) vs 29 of 73 patients (39.7%); p = 0.16) and waited less time for their surgery (5.5 months) (sd 5.26) vs 10.1 months (sd 6.29); p = 0.026). No other identifiable factors were found. A shorter wait for surgery appeared to predict a good outcome. Early review by a spinal surgeon and a reduced waiting time to surgery appear to be of particular benefit to somatised patients.

In 139 patients with burst fractures of the thoracic, thoracolumbar or lumbar spine, the least sagittal diameter of the spinal canal at the level of injury was measured by computerised tomography. By multiple logistic regression we investigated the joint correlation of the level of the burst fracture and the percentage of spinal canal stenosis with the probability of an associated neurological deficit. There was a very significant correlation between neurological deficit and the percentage of spinal canal stenosis; the higher the level of injury the greater was the probability. The severity of neurological deficit could not be predicted.

We report the ten-year results for three designs of stem in 240 total hip replacements, for which subsidence had been measured on plain radiographs at regular intervals. Accurate migration patterns could be determined by the method of Einzel-Bild-Roentgen-Analyse-femoral component analysis (EBRA-FCA) for 158 hips (66%).

Of these, 108 stems (68%) remained stable throughout, and five (3%) started to migrate after a median of 54 months. Initial migration of at least 1 mm was seen in 45 stems (29%) during the first two years, but these then became stable. We revised 17 stems for aseptic loosening, and 12 for other reasons. Revision for aseptic loosening could be predicted by EBRA-FCA with a sensitivity of 69%, a specificity of 80%, and an accuracy of 79% by the use of a threshold of subsidence of 1.5 mm during the first two years. Similar observations over a five-year period allowed the long-term outcome to be predicted with an accuracy of 91%.

We discuss the importance of four different patterns of subsidence and confirm that the early measurement of migration by a reasonably accurate method can help to predict long-term outcome. Such methods should be used to evaluate new and modified designs of prosthesis.

We report the theoretical basis of a method to measure axial migration of femoral components of total hip replacements (THR). The use of the top of the greater trochanter and a lateral point on the collar of the stem, allowing for variations of up to 10 degrees rotation of the femur in any direction between successive radiographs, gave a maximum error of 0.37 mm. At a more realistic 5 degrees rotational variation, the error was only 0.13 mm. These data were confirmed in an experimental study using digitisation of points and special software. We also showed that the centre of the femoral head, the stem tip, and the lesser trochanter provided less accurate landmarks. In a second study we digitised a series of radiographs of 51 Charnley and 57 Stanmore THRs; the mean migration rates were found to be identical. We then studied 46 successful stems with a minimum follow-up of eight years and 46 stems which had failed by aseptic loosening at different times. At two years, the successful stems had migrated by a mean of 1.45 +/- 0.68 mm, but the failed cases had a mean migration of 4.32 +/- 2.58 mm (p < 0.0001). Of the successful cases 76% had migrated less than 2 mm, while in the failed group 84% had migrated more than 2 mm. For any particular case migration of more than 2.6 mm at two years had only a 5% chance of continuing success and would therefore merit special follow-up. Only 24% of the eventually successful stems showed migration at the stem-cement interface, but this had happened in every failed stem. We conclude that it would be possible to evaluate a new cemented design of femoral stem over a two-year period by the use of our method and to compare its performance against the reported known standard of the Charnley and Stanmore designs.

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

Aims. Heterotopic ossification (HO) is a common complication after elbow trauma and can cause severe upper limb disability. Although multiple prognostic factors have been reported to be associated with the development of post-traumatic HO, no model has yet been able to combine these predictors more succinctly to convey prognostic information and medical measures to patients. Therefore, this study aimed to identify prognostic factors leading to the formation of HO after surgery for elbow trauma, and to establish and validate a nomogram to predict the probability of HO formation in such particular injuries. Methods. This multicentre case-control study comprised 200 patients with post-traumatic elbow HO and 229 patients who had elbow trauma but without HO formation between July 2019 and December 2020. Features possibly associated with HO formation were obtained. The least absolute shrinkage and selection operator regression model was used to optimize feature selection. Multivariable logistic regression analysis was applied to build the new nomogram: the Shanghai post-Traumatic Elbow Heterotopic Ossification Prediction model (STEHOP). STEHOP was validated by concordance index (C-index) and calibration plot. Internal validation was conducted using bootstrapping validation. Results. Male sex, obesity, open wound, dislocations, late definitive surgical treatment, and lack of use of non-steroidal anti-inflammatory drugs were identified as adverse predictors and incorporated to construct the STEHOP model. It displayed good discrimination with a C-index of 0.80 (95% confidence interval 0.75 to 0.84). A high C-index value of 0.77 could still be reached in the internal validation. The calibration plot showed good agreement between nomogram prediction and observed outcomes. Conclusion. The newly developed STEHOP model is a valid and convenient instrument to predict HO formation after surgery for elbow trauma. It could assist clinicians in counselling patients regarding treatment expectations and therapeutic choices. Cite this article: Bone Joint J 2022;104-B(8):963–971

Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered. Cite this article: Bone Joint J 2022;104-B(12):1292–1303

Adolescent idiopathic scoliosis (AIS), defined by an age at presentation of 11 to 18 years, has a prevalence of 0.47% and accounts for approximately 90% of all cases of idiopathic scoliosis. Despite decades of research, the exact aetiology of AIS remains unknown. It is becoming evident that it is the result of a complex interplay of genetic, internal, and environmental factors. It has been hypothesized that genetic variants act as the initial trigger that allow epigenetic factors to propagate AIS, which could also explain the wide phenotypic variation in the presentation of the disorder. A better understanding of the underlying aetiological mechanisms could help to establish the diagnosis earlier and allow a more accurate prediction of deformity progression. This, in turn, would prompt imaging and therapeutic intervention at the appropriate time, thereby achieving the best clinical outcome for this group of patients. Cite this article: Bone Joint J 2022;104-B(8):915–921

Acute bone and joint infections in children are serious, and misdiagnosis can threaten limb and life. Most young children who present acutely with pain, limping, and/or loss of function have transient synovitis, which will resolve spontaneously within a few days. A minority will have a bone or joint infection. Clinicians are faced with a diagnostic challenge: children with transient synovitis can safely be sent home, but children with bone and joint infection require urgent treatment to avoid complications. Clinicians often respond to this challenge by using a series of rudimentary decision support tools, based on clinical, haematological, and biochemical parameters, to differentiate childhood osteoarticular infection from other diagnoses. However, these tools were developed without methodological expertise in diagnostic accuracy and do not consider the importance of imaging (ultrasound scan and MRI). There is wide variation in clinical practice with regard to the indications, choice, sequence, and timing of imaging. This variation is most likely due to the lack of evidence concerning the role of imaging in acute bone and joint infection in children. We describe the first steps of a large UK multicentre study, funded by the National Institute for Health Research, which seeks to integrate definitively the role of imaging into a decision support tool, developed with the assistance of individuals with expertise in the development of clinical prediction tools. Cite this article: Bone Joint J 2023;105-B(3):227–229

Aims. The aims of this study were to validate the Forgotten Joint Score-12 (FJS-12) in the postoperative evaluation of periacetabular osteotomy (PAO), identify factors associated with joint awareness after PAO, and determine the FJS-12 threshold for patient-acceptable symptom state (PASS). Methods. Data from 686 patients (882 hips) with hip dysplasia who underwent transposition osteotomy of the acetabulum, a type of PAO, between 1998 and 2019 were reviewed. After screening the study included 442 patients (582 hips; response rate, 78%). Patients who completed a study questionnaire consisting of the visual analogue scale (VAS) for pain and satisfaction, FJS-12, and Hip disability and Osteoarthritis Outcome Score (HOOS) were included. The ceiling effects, internal consistency, convergent validity, and PASS thresholds of FJS-12 were investigated. Results. The median follow-up was 12 years (interquartile range 7 to 16). The ceiling effect of FJS-12 was 7.2%, the lowest of all the measures examined. FJS-12 correlated with all HOOS subscales (ρ = 0.72 to 0.77, p < 0.001) and pain and satisfaction-VAS (ρ = -0.63 and 0.56, p < 0.001), suggesting good convergent validity. Cronbach’s α was 0.95 for the FJS-12, which indicated excellent internal consistency. The median FJS-12 score for preoperative Tönnis grade 0 hips (60 points) was higher than that for grade 1 (51 points) or 2 (46 points). When PASS was defined as pain-VAS < 21 and satisfaction-VAS ≥ 77, the FJS-12 threshold that maximized the sensitivity and specificity for detecting PASS was 50 points (area under the curve (AUC) = 0.85). Conclusion. Our results suggest that FJS-12 is a valid and reliable assessment tool for patients undergoing PAO, and the threshold of 50 points may be useful to determine patient satisfaction following PAO in clinical settings. Further investigation of the factors influencing postoperative joint awareness may enable improved prediction of treatment efficacy and informed decision-making regarding the indication of PAO. Cite this article: Bone Joint J 2023;105-B(7):760–767

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

Aims. Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are common orthopaedic procedures requiring postoperative radiographs to confirm implant positioning and identify complications. Artificial intelligence (AI)-based image analysis has the potential to automate this postoperative surveillance. The aim of this study was to prepare a scoping review to investigate how AI is being used in the analysis of radiographs following THA and TKA, and how accurate these tools are. Methods. The Embase, MEDLINE, and PubMed libraries were systematically searched to identify relevant articles. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews and Arksey and O’Malley framework were followed. Study quality was assessed using a modified Methodological Index for Non-Randomized Studies tool. AI performance was reported using either the area under the curve (AUC) or accuracy. Results. Of the 455 studies identified, only 12 were suitable for inclusion. Nine reported implant identification and three described predicting risk of implant failure. Of the 12, three studies compared AI performance with orthopaedic surgeons. AI-based implant identification achieved AUC 0.992 to 1, and most algorithms reported an accuracy > 90%, using 550 to 320,000 training radiographs. AI prediction of dislocation risk post-THA, determined after five-year follow-up, was satisfactory (AUC 76.67; 8,500 training radiographs). Diagnosis of hip implant loosening was good (accuracy 88.3%; 420 training radiographs) and measurement of postoperative acetabular angles was comparable to humans (mean absolute difference 1.35° to 1.39°). However, 11 of the 12 studies had several methodological limitations introducing a high risk of bias. None of the studies were externally validated. Conclusion. These studies show that AI is promising. While it already has the ability to analyze images with significant precision, there is currently insufficient high-level evidence to support its widespread clinical use. Further research to design robust studies that follow standard reporting guidelines should be encouraged to develop AI models that could be easily translated into real-world conditions. Cite this article: Bone Joint J 2022;104-B(8):929–937

Aims. Surgical site infection (SSI) after soft-tissue sarcoma (STS) resection is a serious complication. The purpose of this retrospective study was to investigate the risk factors for SSI after STS resection, and to develop a nomogram that allows patient-specific risk assessment. Methods. A total of 547 patients with STS who underwent tumour resection between 2005 and 2021 were divided into a development cohort and a validation cohort. In the development cohort of 402 patients, the least absolute shrinkage and selection operator (LASSO) regression model was used to screen possible risk factors of SSI. To select risk factors and construct the prediction nomogram, multivariate logistic regression was used. The predictive power of the nomogram was evaluated by receiver operating curve (ROC) analysis in the validation cohort of 145 patients. Results. LASSO regression analysis selected possible risk factors for SSI, including age, diabetes, operating time, skin graft or flap, resected tumour size, smoking, and radiation therapy. Multivariate analysis revealed that age, diabetes, smoking during the previous year, operating time, and radiation therapy were independent risk factors for SSI. A nomogram was developed based on the results of multivariate logistic regression analysis. In the development cohort, the incidence of SSI was 4.5% in the low-risk group (risk score < 6.89) and 26.6% in the high-risk group (risk score ≥ 6.89; p < 0.001). In the validation cohort, the incidence of SSI was 2.0% in the low-risk group and 15.9% in the high-risk group (p = 0.004). Conclusion. Our nomogram will enable surgeons to assess the risk of SSI in patients with STS. In patients with high risk of SSI, frequent monitoring and aggressive interventions should be considered to prevent this. Cite this article: Bone Joint J 2024;106-B(5):492–500

Aims. The aim of this study was to compare any differences in the primary outcome (biphasic flexion knee moment during gait) of robotic arm-assisted bi-unicompartmental knee arthroplasty (bi-UKA) with conventional mechanically aligned total knee arthroplasty (TKA) at one year post-surgery. Methods. A total of 76 patients (34 bi-UKA and 42 TKA patients) were analyzed in a prospective, single-centre, randomized controlled trial. Flat ground shod gait analysis was performed preoperatively and one year postoperatively. Knee flexion moment was calculated from motion capture markers and force plates. The same setup determined proprioception outcomes during a joint position sense test and one-leg standing. Surgery allocation, surgeon, and secondary outcomes were analyzed for prediction of the primary outcome from a binary regression model. Results. Both interventions were shown to be effective treatment options, with no significant differences shown between interventions for the primary outcome of this study (18/35 (51.4%) biphasic TKA patients vs 20/31 (64.5%) biphasic bi-UKA patients; p = 0.558). All outcomes were compared to an age-matched, healthy cohort that outperformed both groups, indicating residual deficits exists following surgery. Logistic regression analysis of primary outcome with secondary outcomes indicated that the most significant predictor of postoperative biphasic knee moments was preoperative knee moment profile and trochlear degradation (Outerbridge) (R. 2. = 0.381; p = 0.002, p = 0.046). A separate regression of alignment against primary outcome indicated significant bi-UKA femoral and tibial axial alignment (R. 2. = 0.352; p = 0.029), and TKA femoral sagittal alignment (R. 2. = 0.252; p = 0.016). The bi-UKA group showed a significant increased ability in the proprioceptive joint position test, but no difference was found in more dynamic testing of proprioception. Conclusion. Robotic arm-assisted bi-UKA demonstrated equivalence to TKA in achieving a biphasic gait pattern after surgery for osteoarthritis of the knee. Both treatments are successful at improving gait, but both leave the patients with a functional limitation that is not present in healthy age-matched controls. Cite this article: Bone Joint J 2022;103-B(4):433–443

Aims. The aim of this study was to assess whether supine flexibility predicts the likelihood of curve progression in patients with adolescent idiopathic scoliosis (AIS) undergoing brace treatment. Methods. This was a retrospective analysis of patients with AIS prescribed with an underarm brace between September 2008 to April 2013 and followed up until 18 years of age or required surgery. Patients with structural proximal curves that preclude underarm bracing, those who were lost to follow-up, and those who had poor compliance to bracing (<16 hours a day) were excluded. The major curve Cobb angle, curve type, and location were measured on the pre-brace standing posteroanterior (PA) radiograph, supine whole spine radiograph, initial in-brace standing PA radiograph, and the post-brace weaning standing PA radiograph. Validation of the previous in-brace Cobb angle regression model was performed. The outcome of curve progression post-bracing was tested using a logistic regression model. The supine flexibility cut-off for curve progression was analyzed with receiver operating characteristic curve. Results. A total of 586 patients with mean age of 12.6 years (SD 1.2) remained for analysis after exclusion. The baseline Cobb angle was similar for thoracic major curves (31.6° (SD 3.8°)) and lumbar major curves (30.3° (SD 3.7°)). Curve progression was more common in the thoracic curves than lumbar curves with mean final Cobb angles of 40.5° (SD 12.5°) and 31.8° (SD 9.8°) respectively. This dataset matched the prediction model for in-brace Cobb angle with less mean absolute error in thoracic curves (0.61) as compared to lumbar curves (1.04). Reduced age and Risser stage, thoracic curves, increased pre-brace Cobb angle, and reduced correction and flexibility rates predicted increased likelihood of curve progression. Flexibility rate of more than 28% has likelihood of preventing curve progression with bracing. Conclusion. Supine radiographs provide satisfactory prediction for in-brace correction and post-bracing curve magnitude. The flexibility of the curve is a guide to determine the likelihood for brace success. Cite this article: Bone Joint J 2020;102-B(2):254–260

Aims. Intra-articular (IA) tumours around the knee are treated with extra-articular (EA) resection, which is associated with poor functional outcomes. We aim to evaluate the accuracy of MRI in predicting IA involvement around the knee. Methods. We identified 63 cases of high-grade sarcomas in or around the distal femur that underwent an EA resection from a prospectively maintained database (January 1996 to April 2020). Suspicion of IA disease was noted in 52 cases, six had IA pathological fracture, two had an effusion, two had prior surgical intervention (curettage/IA intervention), and one had an osseous metastasis in the proximal tibia. To ascertain validity, two musculoskeletal radiologists (R1, R2) reviewed the preoperative imaging (MRI) of 63 consecutive cases on two occasions six weeks apart. The radiological criteria for IA disease comprised evidence of tumour extension within the suprapatellar pouch, intercondylar notch, extension along medial/lateral retinaculum, and presence of IA fracture. The radiological predictions were then confirmed with the final histopathology of the resected specimens. Results. The resection histology revealed 23 cases (36.5%) showing IA disease involvement compared with 40 cases without (62%). The intraobserver variability of R1 was 0.85 (p < 0.001) compared to R2 with κ = 0.21 (p = 0.007). The interobserver variability was κ = 0.264 (p = 0.003). Knee effusion was found to be the most sensitive indicator of IA involvement, with a sensitivity of 91.3% but specificity of only 35%. However, when combined with a pathological fracture, this rose to 97.5% and 100% when disease was visible in Hoffa’s fat pad. Conclusion. MRI imaging can sometimes overestimate IA joint involvement and needs to be correlated with clinical signs. In the light of our findings, we would recommend EA resections when imaging shows effusion combined with either disease in Hoffa’s fat pad or retinaculum, or pathological fractures. Cite this article: Bone Joint J 2023;105-B(6):696–701

Aims. Excessive posterior pelvic tilt (PT) may increase the risk of anterior instability after total hip arthroplasty (THA). The aim of this study was to investigate the changes in PT occurring from the preoperative supine to postoperative standing position following THA, and identify factors associated with significant changes in PT. Methods. Supine PT was measured on preoperative CT scans and standing PT was measured on preoperative and one-year postoperative standing lateral radiographs in 933 patients who underwent primary THA. Negative values indicate posterior PT. Patients with > 13° of posterior PT from preoperative supine to postoperative standing (ΔPT ≤ -13°) radiographs, which corresponds to approximately a 10° increase in functional anteversion of the acetabular component, were compared with patients with less change (ΔPT > -13°). Logistic regression analysis was used to assess preoperative demographic and spinopelvic parameters predictive of PT changes of ≤ -13°. The area under receiver operating characteristic curve (AUC) determined the diagnostic accuracy of the predictive factors. Results. PT changed from a mean of 3.8° (SD 6.0°)) preoperatively to -3.5° (SD 6.9°) postoperatively, a mean change of -7.4 (SD 4.5°; p < 0.001). A total of 95 patients (10.2%) had ≤ -13° change in PT from preoperative supine to postoperative standing. The strongest predictive preoperative factors of large changes in PT (≤ -13°) from preoperative supine to postoperative standing were a large posterior change in PT from supine to standing, increased supine PT, and decreased standing PT (p < 0.001). Flexed-seated PT (p = 0.006) and female sex (p = 0.045) were weaker significant predictive factors. When including all predictive factors, the accuracy of the AUC prediction was 84.9%, with 83.5% sensitivity and 71.2% specificity. Conclusion. A total of 10% of patients had > 13° of posterior PT postoperatively compared with their supine pelvic position, resulting in an increased functional anteversion of > 10°. The strongest predictive factors of changes in postoperative PT were the preoperative supine-to-standing differences, the anterior supine PT, and the posterior standing PT. Surgeons who introduce the acetabular component with the patient supine using an anterior approach should be aware of the potentially large increase in functional anteversion occurring in these patients. Cite this article: Bone Joint J 2024;106-B(3 Supple A):74–80

Aims. Accurate estimations of the risk of fracture due to metastatic bone disease in the femur is essential in order to avoid both under-treatment and over-treatment of patients with an impending pathological fracture. The purpose of the current retrospective in vivo study was to use CT-based finite element analyses (CTFEA) to identify a clear quantitative differentiating factor between patients who are at imminent risk of fracturing their femur and those who are not, and to identify the exact location of maximal weakness where the fracture is most likely to occur. Methods. Data were collected on 82 patients with femoral metastatic bone disease, 41 of whom did not undergo prophylactic fixation. A total of 15 had a pathological fracture within six months following the CT scan, and 26 were fracture-free during the five months following the scan. The Mirels score and strain fold ratio (SFR) based on CTFEA was computed for all patients. A SFR value of 1.48 was used as the threshold for a pathological fracture. The sensitivity, specificity, positive, and negative predicted values for Mirels score and SFR predictions were computed for nine patients who fractured and 24 who did not, as well as a comparison of areas under the receiver operating characteristic curves (AUC of the ROC curves). Results. The sensitivity of SFR was 100% compared with 88% for the Mirels score, and the specificity of SFR was 67% compared with 38% for the Mirels score. The AUC was 0.905 for SFR compared with 0.578 for the Mirels score (p = 0.008). Conclusion. All the patients who sustained a pathological fracture of the femur had an SFR of > 1.48. CTFEA was far better at predicting the risk of fracture and its location accurately compared with the Mirels score. CTFEA is quick and automated and can be incorporated into the protocol of CT scanners. Cite this article: Bone Joint J 2020;102-B(5):638–645

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

The application of robotics in the operating theatre for knee arthroplasty remains controversial. As with all new technology, the introduction of new systems might be associated with a learning curve. However, guidelines on how to assess the introduction of robotics in the operating theatre are lacking. This systematic review aims to evaluate the current evidence on the learning curve of robot-assisted knee arthroplasty. An extensive literature search of PubMed, Medline, Embase, Web of Science, and Cochrane Library was conducted. Randomized controlled trials, comparative studies, and cohort studies were included. Outcomes assessed included: time required for surgery, stress levels of the surgical team, complications in regard to surgical experience level or time needed for surgery, size prediction of preoperative templating, and alignment according to the number of knee arthroplasties performed. A total of 11 studies met the inclusion criteria. Most were of medium to low quality. The operating time of robot-assisted total knee arthroplasty (TKA) and unicompartmental knee arthroplasty (UKA) is associated with a learning curve of between six to 20 cases and six to 36 cases respectively. Surgical team stress levels show a learning curve of seven cases in TKA and six cases for UKA. Experience with the robotic systems did not influence implant positioning, preoperative planning, and postoperative complications. Robot-assisted TKA and UKA is associated with a learning curve regarding operating time and surgical team stress levels. Future evaluation of robotics in the operating theatre should include detailed measurement of the various aspects of the total operating time, including total robotic time and time needed for preoperative planning. The prior experience of the surgical team should also be evaluated and reported. Cite this article: Bone Joint J 2020;102-B(4):407–413

Aims. The early mortality in patients with hip fractures from bony metastases is unknown. The objectives of this study were to quantify 30- and 90-day mortality in patients with proximal femoral metastases, and to create a mortality prediction tool based on biomarkers associated with early death. Methods. This was a retrospective cohort study of consecutive patients referred to the orthopaedic department at a UK trauma centre with a proximal femoral metastasis (PFM) over a seven-year period (2010 to 2016). The study group were compared to a matched control group of non-metastatic hip fractures. Minimum follow-up was one year. Results. There was a 90-day mortality of 46% in patients with metastatic hip fractures versus 12% in controls (89/195 and 24/192, respectively; p < 0.001). Mean time to surgery was longer in symptomatic metastases versus complete fractures (9.5 days (SD 19.8) and 3.4 days (SD 11.4), respectively; p < 0.05). Albumin, urea, and corrected calcium were all independent predictors of early mortality and were used to generate a simple tool for predicting 90-day mortality, titled the Metastatic Early Prognostic (MEP) score. An MEP score of 0 was associated with the lowest risk of death at 30 days (14%, 3/21), 90 days (19%, 4/21), and one year (62%, 13/21). MEP scores of 3/4 were associated with the highest risk of death at 30 days (56%, 5/9), 90 days (100%, 9/9), and one year (100%, 9/9). Neither age nor primary cancer diagnosis was an independent predictor of mortality at 30 and 90 days. Conclusion. This score could be used to predict early mortality and guide perioperative counselling. The delay to surgery identifies a potential window to intervene and correct these abnormalities with the aim of improving survival. Cite this article: Bone Joint J. 2020;102-B(1):72–81