Aims

The modern prevalence of primary tumours causing metastatic bone disease is ill-defined in the oncological literature. Therefore, the purpose of this study is to identify the prevalence of primary tumours in the setting of metastatic bone disease, as well as reported rates of pathological fracture, postoperative complications, 90-day mortality, and 360-day mortality for each primary tumour subtype.

Mirels’ score predicts the likelihood of sustaining pathological fractures using pain, lesion site, size and morphology. The aim is to investigate its reproducibility, reliability and accuracy in upper limb bony metastases and validate its use in pathological fracture prediction. A retrospective cohort study of patients with upper limb metastases, referred to an Orthopaedic Trauma Centre (2013–18). Mirels’ was calculated in 32 patients; plain radiographs at presentation scored by 6 raters. Radiological aspects were scored twice by each rater, 2-weeks apart. Inter- and intra-observer reliability were calculated (Fleiss’ kappa test). Bland-Altman plots compared variances of individual score components &total Mirels’ score. Mirels’ score of ≥9 did not accurately predict lesions that would fracture (11% 5/46 vs 65.2% Mirels’ score ≤8, p<0.0001). Sensitivity was 14.3% &specificity was 72.7%. When Mirels’ cut-off was lowered to ≥7, patients were more likely to fracture (48% 22/46 versus 28% 13/46, p=0.045). Sensitivity rose to 62.9%, specificity fell to 54.6%. Kappa values for interobserver variability were 0.358 (fair, 0.288–0.429) for lesion size, 0.107 (poor, 0.02–0.193) for radiological appearance and 0.274 (fair, 0.229–0.318) for total Mirels’ score. Values for intraobserver variability were 0.716 (good, 95% CI 0.432–0.999) for lesion size, 0.427 (moderate, 95% CI 0.195–0.768) for radiological appearance and 0.580 (moderate, 0.395–0.765) for total Mirels’ score. We showed moderate to substantial agreement between &within raters using Mirels’ score on upper limb radiographs. Mirels’ has poor sensitivity &specificity predicting upper limb fractures - we recommend the cut-off score for prophylactic surgery should be lower than for lower limb lesions

Aims

The aim of this study was to inform the epidemiology and treatment of slipped capital femoral epiphysis (SCFE).

Patients with cancer and bone metastases can have an increased risk of fracturing their femur. Treatment is based on the impending fracture risk: patients with a high fracture risk are considered for prophylactic surgery, whereas low fracture risk patients are treated conservatively with radiotherapy to decrease pain. Current clinical guidelines suggest to determine fracture risk based on axial cortical involvement of the lesion on conventional radiographs, but that appears to be difficult. Therefore, we developed a patient-specific finite element (FE) computer model that has shown to be able to predict fracture risk in an experimental setting and in patients. The goal of this study was to determine whether patient-specific finite element (FE) computer models are better at predicting fracture risk for femoral bone metastases compared to clinical assessments based on axial cortical involvement on conventional radiographs, as described in current clinical guidelines. 45 patients (50 affected femurs) affected with predominantly lytic bone metastases who were treated with palliative radiotherapy for pain were included. CT scans were made and patients were followed for six months to determine whether or not they fractured their femur. Non-linear isotropic FE models were created with the patient-specific geometry and bone density obtained from the CT scans. Subsequently, an axial load was simulated on the models mimicking stance. Failure loads normalized for bodyweight (BW) were calculated for each femur. High and low fracture risks were determined using a failure load of 7.5 × BW as a threshold. Experienced assessors measured axial cortical involvement on conventional radiographs. Following clinical guidelines, patients with lesions larger than 30 mm were identified as having a high fracture risk. FE predictions were compared to clinical assessments by means of diagnostic accuracy values (sensitivity, specificity and positive (PPV) and negative predictive values (NPV)). Seven femurs (14%) fractured during follow-up. Median time to fracture was 8 weeks. FE models were better at predicting fracture risk in comparison to clinical assessments based on axial cortical involvement (sensitivity 100% vs. 86%, specificity 74% vs. 42%, PPV 39% vs. 19%, and NPV 100% vs. 95%, for the FE computer model vs. axial cortical involvement, respectively). We concluded that patient-specific FE computer models improve fracture risk predictions of femoral bone metastases in advanced cancer patients compared to clinical assessments based on axial cortical involvement, which is currently used in clinical guidelines. Therefore, we are initiating a pilot for clinical implementation of the FE model

Aims

Hip displacement, common in patients with cerebral palsy (CP), causes pain and hinders adequate care. Hip reconstructive surgery (HRS) is performed to treat hip displacement; however, only a few studies have quantitatively assessed femoral head sphericity after HRS. The aim of this study was to quantitatively assess improvement in hip sphericity after HRS in patients with CP.

Advances in cancer therapy have prolonged patient survival even in the presence of disseminated disease and an increasing number of cancer patients are living with metastatic bone disease (MBD). The proximal femur is the most common long bone involved in MBD and pathologic fractures of the femur are associated with significant morbidity, mortality and loss of quality of life (QoL). Successful prophylactic surgery for an impending fracture of the proximal femur has been shown in multiple cohort studies to result in longer survival, preserved mobility, lower transfusion rates and shorter post-operative hospital stays. However, there is currently no optimal method to predict a pathologic fracture. The most well-known tool is Mirel's criteria, established in 1989 and is limited from guiding clinical practice due to poor specificity and sensitivity. The ideal clinical decision support tool will be of the highest sensitivity and specificity, non-invasive, generalizable to all patients, and not a burden on hospital resources or the patient's time. Our research uses novel machine learning techniques to develop a model to fill this considerable gap in the treatment pathway of MBD of the femur. The goal of our study is to train a convolutional neural network (CNN) to predict fracture risk when metastatic bone disease is present in the proximal femur. Our fracture risk prediction tool was developed by analysis of prospectively collected data of consecutive MBD patients presenting from 2009–2016. Patients with primary bone tumors, pathologic fractures at initial presentation, and hematologic malignancies were excluded. A total of 546 patients comprising 114 pathologic fractures were included. Every patient had at least one Anterior-Posterior X-ray and clinical data including patient demographics, Mirel's criteria, tumor biology, all previous radiation and chemotherapy received, multiple pain and function scores, medications and time to fracture or time to death. We have trained a convolutional neural network (CNN) with AP X-ray images of 546 patients with metastatic bone disease of the proximal femur. The digital X-ray data is converted into a matrix representing the color information at each pixel. Our CNN contains five convolutional layers, a fully connected layers of 512 units and a final output layer. As the information passes through successive levels of the network, higher level features are abstracted from the data. The model converges on two fully connected deep neural network layers that output the risk of fracture. This prediction is compared to the true outcome, and any errors are back-propagated through the network to accordingly adjust the weights between connections, until overall prediction accuracy is optimized. Methods to improve learning included using stochastic gradient descent with a learning rate of 0.01 and a momentum rate of 0.9. We used average classification accuracy and the average F1 score across five test sets to measure model performance. We compute F1 = 2 x (precision x recall)/(precision + recall). F1 is a measure of a model's accuracy in binary classification, in our case, whether a lesion would result in pathologic fracture or not. Our model achieved 88.2% accuracy in predicting fracture risk across five-fold cross validation testing. The F1 statistic is 0.87. This is the first reported application of convolutional neural networks, a machine learning algorithm, to this important Orthopaedic problem. Our neural network model was able to achieve reasonable accuracy in classifying fracture risk of metastatic proximal femur lesions from analysis of X-rays and clinical information. Our future work will aim to externally validate this algorithm on an international cohort

Advances in cancer therapy have prolonged cancer patient survival even in the presence of disseminated disease and an increasing number of cancer patients are living with metastatic bone disease (MBD). The proximal femur is the most common long bone involved in MBD and pathologic fractures of the femur are associated with significant morbidity, mortality and loss of quality of life (QoL). Successful prophylactic surgery for an impending fracture of the proximal femur has been shown in multiple cohort studies to result in patients more likely to walk after surgery, longer survival, lower transfusion rates and shorter post-operative hospital stays. However, there is currently no optimal method to predict a pathologic fracture. The most well-known tool is Mirel's criteria, established in 1989 and is limited from guiding clinical practice due to poor specificity and sensitivity. The goal of our study is to train a convolutional neural network (CNN) to predict fracture risk when metastatic bone disease is present in the proximal femur. Our fracture risk prediction tool was developed by analysis of prospectively collected data for MBD patients (2009–2016) in order to determine which features are most commonly associated with fracture. Patients with primary bone tumors, pathologic fractures at initial presentation, and hematologic malignancies were excluded. A total of 1146 patients comprising 224 pathologic fractures were included. Every patient had at least one Anterior-Posterior X-ray. The clinical data includes patient demographics, tumor biology, all previous radiation and chemotherapy received, multiple pain and function scores, medications and time to fracture or time to death. Each of Mirel's criteria has been further subdivided and recorded for each lesion. We have trained a convolutional neural network (CNN) with X-ray images of 1146 patients with metastatic bone disease of the proximal femur. The digital X-ray data is converted into a matrix representing the color information at each pixel. Our CNN contains five convolutional layers, a fully connected layers of 512 units and a final output layer. As the information passes through successive levels of the network, higher level features are abstracted from the data. This model converges on two fully connected deep neural network layers that output the fracture risk. This prediction is compared to the true outcome, and any errors are back-propagated through the network to accordingly adjust the weights between connections. Methods to improve learning included using stochastic gradient descent with a learning rate of 0.01 and a momentum rate of 0.9. We used average classification accuracy and the average F1 score across test sets to measure model performance. We compute F1 = 2 x (precision x recall)/(precision + recall). F1 is a measure of a test's accuracy in binary classification, in our case, whether a lesion would result in pathologic fracture or not. Five-fold cross validation testing of our fully trained model revealed accurate classification for 88.2% of patients with metastatic bone disease of the proximal femur. The F1 statistic is 0.87. This represents a 24% error reduction from using Mirel's criteria alone to classify the risk of fracture in this cohort. This is the first reported application of convolutional neural networks, a machine learning algorithm, to an important Orthopaedic problem. Our neural network model was able to achieve impressive accuracy in classifying fracture risk of metastatic proximal femur lesions from analysis of X-rays and clinical information. Our future work will aim to validate this algorithm on an external cohort

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.

Aims

Severe spinal deformity in growing patients often requires surgical management. We describe the incidence of spinal deformity surgery in a National Health Service.

Aims

The aim of this study is to evaluate the clinical results of operative intervention for femoral metastases which were selected based on expected survival and to discuss appropriate surgical strategies.

Objectives

In this prospective cohort study, we investigated whether patient-specific finite element (FE) models can identify patients at risk of a pathological femoral fracture resulting from metastatic bone disease, and compared these FE predictions with clinical assessments by experienced clinicians.

Purpose. The decision to undertake prophylactic pinning to prevent contralateral slipped upper femoral epiphysis (SUFE) remains controversial; we hypothesised that the grade of initial SUFE could predict the grade of a second SUFE and risk of poor outcome. Method. We retrospectively reviewed radiographs of all children who presented to Alder Hey with a new diagnosis of SUFE between 2007–2014. Of those who developed a contralateral SUFE, grade of first and second SUFE was determined radio-graphically using % slip and Southwick angle on frog lateral radiograph. Results. 100 patients that presented with a new diagnosis of SUFE were identified. 73 had no contralateral surgery at first presentation. Of these, 56 reached skeletal maturity with no contralateral slip. 17 re-presented acutely with a contralateral slip requiring operative intervention. 4 presented with unilateral symptoms but radiographic evidence of bilateral slips and underwent bilateral surgery at time of first presentation. No cases of severe slip where seen in children with initial mild or moderate slips. 100% of children presented with a contralateral SUFE of the same or less severe grade. In cases of initial severe SUFE, grade of second SUFE was unpredictable; 3 pre-slip, 1 mild, 1 moderate, 2 severe. Conclusion. Grade of initial SUFE may be a useful adjunct to decision making when considering risk- benefit of prophylactic contralateral surgery. In cases of initial mild slip re-presentation with a severe contralateral SUFE is unlikely and a higher threshold for prophylactic intervention may be appropriate

Aims. Treatment guidelines for atypical femoral fractures associated with bisphosphonates have not been established. We conducted a systematic review of the treatment of atypical femoral fractures first, to evaluate the outcomes of surgical fixation of complete atypical fractures and secondly, to assess whether prophylactic surgery is necessary for incomplete atypical fractures. Materials and Methods. Case reports and series were identified from the PubMed database and were included if they described the treatment of atypical femoral fractures. In total, 77 publications met our inclusion criteria and 733 patients with 834 atypical complete or incomplete femoral fractures were identified. Results. For complete fractures, internal fixation was predominantly achieved by intramedullary nailing. The mean time to healing post-operatively was 7.3 months (2 to 31). Revision surgery for nonunion or implant failure was needed in 77 fractures (12.6%). A greater percentage of fractures treated with plate fixation (31.3%) required revision surgery than those treated with intramedullary nailing (12.9%) (p < 0.01). Non-operative treatment of incomplete fractures failed and surgery was eventually needed in nearly half of the patients (47%), whereas prophylactic surgery was successful and achieved a 97% rate of healing. Conclusion. Intramedullary nailing is the first-line treatment for a complete fracture, although the risk of delayed healing and revision surgery seems to be higher than with a typical femoral fracture. Non-operative treatment does not appear to be a reliable way of treating an incomplete fracture: prophylactic intramedullary nailing should be considered if the patient is in intractable pain. Radiographs of the opposite side should be obtained routinely looking for an asymptomatic fracture. Bisphosphonates must be discontinued but ongoing metabolic management in the form of calcium and/or vitamin D supplements is advisable. Teriparatide therapy can be considered as an alternative treatment. Cite this article: Bone Joint J 2017;99-B:295–302

Controversy remains whether the contralateral hip should be fixed in patients presenting with unilateral slipped capital femoral epiphysis (SCFE). This retrospective study compares the outcomes and cost of those patients who had prophylactic fixation with those who did not.

Between January 2000 and December 2010 a total of 50 patients underwent unilateral fixation and 36 had prophylactic fixation of the contralateral hip. There were 54 males and 32 females with a mean age of 12.3 years (9 to 16). The rate of a subsequent slip without prophylactic fixation was 46%. The risk of complications was greater, the generic health measures (Short Form-12 physical (p < 0.001) and mental (p = 0.004) summary scores) were worse. Radiographic cam lesions in patients presenting with unilateral SCFE were only seen in patients who did not have prophylactic fixation. Furthermore, prophylactic fixation of the contralateral hip was found to be a cost-effective procedure, with a cost per quality adjusted life year gained of £1431 at the time of last follow-up.

Prophylactic fixation of the contralateral hip is a cost-effective operation that limits the morbidity from the complications of a further slip, and the diminished functional outcome associated with unilateral fixation.

Cite this article: Bone Joint J 2015;97-B:1428–34.

The December 2013 Hip & Pelvis Roundup. 360 . looks at: Enhanced recovery works; Acetabular placement; Exercise better than rest in osteoarthritis patients; if Birmingham hip resurfacing is immune from pseudotumour; HIV and arthroplasty; Labral tears revisited; Prophylactic surgery for FAI; and Ceramics and impaction grafting

The technical advances in arthroscopic surgery of the hip, including the improved ability to manage the capsule and gain extensile exposure, have been paralleled by a growth in the number of conditions that can be addressed. This expanding list includes symptomatic labral tears, chondral lesions, injuries of the ligamentum teres, femoroacetabular impingement (FAI), capsular laxity and instability, and various extra-articular disorders, including snapping hip syndromes. With a careful diagnostic evaluation and technical execution of well-indicated procedures, arthroscopic surgery of the hip can achieve successful clinical outcomes, with predictable improvements in function and pre-injury levels of physical activity for many patients.

This paper reviews the current position in relation to the use of arthroscopy in the treatment of disorders of the hip.

Cite this article: Bone Joint J 2013;95-B:10–19.

Aims. Bone is a common site of metastatic disease. Skeletal complications include disabling pain and pathological fractures. Palliative surgery for incurable metastatic bone lesions aims to preserve quality of life and function by providing pain relief and stable mobility with fixation or replacement. Current literature has few treatment studies. We present a 5 year longitudinal cohort study of surgery for metastatic bone disease at our large teaching hospital reviewing our complication and mortality rates. Methods. Patients that underwent palliative surgery for metastatic bone lesions were identified from operative records. Demographics, clinical details and outcomes were recorded. Kaplan-Meier analysis was used to calculate survivorship. Results. 43 patients were treated for 44 bone metastases (34 IM Nails, 9 prosthetic replacements, 1 plate). The median age at primary diagnosis was 66 (33–92). Lung cancer was the most common primary. 56% presented with complete fractures and 44% with impending fractures (median Mirel score of 10). Pertrochanteric bone lesions were the most common (74%). Two out of 43 patients died within one day of surgery. 30 day mortality was 12% and 45% at 1 year. In those surviving the 30 day perioperative period, we report a complication rate of 14%. One patient had a dislocated prosthesis. Two patients had delayed or non union and two patients had failure of metalwork. No patient required re operation. Conclusion. Our series observed a 5% fixation failure rate and significant perioperative mortality. Whilst surgery may offer benefit in the non moribund patient with pathological fracture the decision to offer prophylactic surgery is more difficult in light of the high perioperative mortality seen in our study. Indeed, the patients in our study who died within 24 hours of surgery had prophylactic fixations. We conclude that surgical intervention must be carefully considered with realistic expectations of outcomes

Previous studies have shown improved outcome following surgery for spinal cord compression due to metastatic disease. Further papers have shown that many patients with metastatic disease are not referred for orthopaedic opinion. The aims of this paper are to study the survival and morbidity of patients with spinal metastatic disease who receive radiotherapy. Do patients develop instability and progressive neurological compromise? Can we predict which patients will benefit from surgery?. Retrospective review of patients receiving radiotherapy for pain relief or cord compression as a result of metastatic disease. Patients were scored with regards to Tomita and Tokuhashi, survival and for deterioration in neurology or spinal instability. 94 patients reviewed. All patients were followed up for a minimum of 1 year or until deceased. Majority of patients had a primary diagnosis of lung, prostate or breast carcinoma. Mean Tomita score of 6, Tokuhashi score 7, and mean survival following radiotherapy of 8 months. 11:94 patients referred for surgical opinion. Poor correlation with Tomita scores (-0.25) & Tokuhashi scores (0.24) to predict survival. Four patients developed progressive neurology on follow-up. One patient developed spinal instability. The remainder of the patients did not deteriorate in neurology and did not develop spinal instability. All patients with normal neurology at time of radiotherapy did not develop spinal cord compression or cauda equina at a later date. This study suggests that the vast majority of patients with spinal metastatic disease do not progress to spinal instability or cord compression, and that prophylactic surgery would not be of benefit. The predictive scoring systems remain unreliable making it difficult to select those patients who would benefit. The referral rate to spinal surgeons remains low as few patients under the care of the oncologists develop spinal complications

We retrospectively examined the prevalence and natural history of asymptomatic lumbar canal stenosis in patients treated surgically for cervical compressive myelopathy in order to assess the influence of latent lumbar canal stenosis on the recovery after surgery. Of 214 patients who had undergone cervical laminoplasty for cervical myelopathy, we identified 69 (32%) with myelographically documented lumbar canal stenosis. Of these, 28 (13%) patients with symptomatic lumbar canal stenosis underwent simultaneous cervical and lumbar decompression. Of the remaining 41 (19%) patients with asymptomatic lumbar canal stenosis who underwent only cervical surgery, 39 were followed up for ≥ 1 year (mean 4.9 years (1 to 12)) and were included in the analysis (study group). Patients without myelographic evidence of lumbar canal stenosis, who had been followed up for ≥ 1 year after the cervical surgery, served as controls (135 patients; mean follow-up period 6.5 years (1 to 17)). Among the 39 patients with asymptomatic lumbar canal stenosis, seven had lumbar-related leg symptoms after the cervical surgery.

Kaplan–Meier analysis showed that 89.6% (95% confidence interval (CI) 75.3 to 96.0) and 76.7% (95% CI 53.7 to 90.3) of the patients with asymptomatic lumbar canal stenosis were free from leg symptoms for three and five years, respectively. There were no significant differences between the study and control groups in the recovery rate measured by the Japanese Orthopaedic Association score or improvement in the Nurick score at one year after surgery or at the final follow-up.

These results suggest that latent lumbar canal stenosis does not influence recovery following surgery for cervical myelopathy; moreover, prophylactic lumbar decompression does not appear to be warranted as a routine procedure for coexistent asymptomatic lumbar canal stenosis in patients with cervical myelopathy, when planning cervical surgery.