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

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

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

The risk of further fractures increases 2–10 times after the first fracture. Actual fracture risk for the given person (absolute fracture risk) can be calculated from data collected in 10-year prospective studies (NHANES or Kanis 2001). To calculate absolute fracture risk one has to multiply age-related risk factor ascertained in above studies by the coefficient estimated for particular factors influencing possible fracture (relative fracture risk). The most commonly used factors are: age (RR 2.0 for each 5 yrs over 65), low BMD (RR/SD 1.4–2.6), low-energy fracture after the age of 40 (RR 4.0), proximal femur fracture in mother (*RR 1.9), body mass lower than 58 kg (*RR 1.9), early menopause – before the age of 45, smoking (RR 1.2), susceptibility to falls (*RR 3.5), corticosteroids intake. Absolute fracture risk in 60-year-old woman whose foreseen 10-year probability of femoral neck fracture is 2.3% with normal BMD but burden by factors marked by asterisks would be: 2.3% x 1.9 x 1.9 x 3.5 = 29%. As 76% of fractures occur in women with normal BMD absolute fracture risk is the most objective information. In case of proximal femoral fracture 10-year probability of 10% or more fracture risk provides a cost effective threshold for women in Sweden. We can increase bone mineral density by pharmacological intervention. Every patient should be given calcium and vit. D supplementation and a specific medication, which should be adjusted to: age, sex and presence of hot flashes and fractures. HRT is preferred in women aged 50–60 yrs suffering from hot flashes. HRT decreases the risk of spine (50%) and proximal femur fracture (40%). However some risk of breast and uterine cancer has to be taken into consideration. Selective estrogen modulators (SERM; raloxifene) act as estrogen agonists on bone and cardiovascular system but as antagonists on breast tissue. Decrease of spinal fracture (45%) and breast cancer incidence (70%) is proven but no positive action on proximal femur is reported. In women who underwent osteoporotic fracture one can apply bisphosphonates, strontium ranelate or PTH. Alendronate reduces spine fractures (47%) and proximal femur fractures (51%). Similar effects are documented for risedronate (spine – 60% and proximal femur 40–56%). Strontium ranelate not only inhibits bone resorption but also stimulates bone formation. Decrease of spine and proximal femur fractures occurrence has been proven (41%). PTH injected sc. in daily doses is the most powerful compound which rebuilds bone trabeculae in severe cases and reduces incidence of peripheral fractures (53%). Calcitonin is effective in spine fractures but not in proximal femur. Fall prevention program should be implemented in all patients with osteoporosis independently from pharmacological intervention

Introduction. Patients (2.7M in EU) with positive cancer prognosis frequently develop metastases (≈1M) in their remaining lifetime. In 30-70% cases, metastases affect the spine, reducing the strength of the affected vertebrae. Fractures occur in ≈30% patients. Clinicians must choose between leaving the patient exposed to a high fracture risk (with dramatic consequences) and operating to stabilise the spine (exposing patients to unnecessary surgeries). Currently, surgeons rely on their sole experience. This often results in to under- or over-treatment. The standard-of-care are scoring systems (e.g. Spine Instability Neoplastic Score) based on medical images, with little consideration of the spine biomechanics, and of the structure of the vertebrae involved. Such scoring systems fail to provide clear indications in ≈60% patients. Method. The HEU-funded METASTRA project is implemented by biomechanicians, modellers, clinicians, experts in verification, validation, uncertainty quantification and certification from 15 partners across Europe. METASTRA aims to improve the stratification of patients with vertebral metastases evaluating their risk of fracture by developing dedicated reliable computational models based on Explainable Artificial Intelligence (AI) and on personalised Physiology-based biomechanical (VPH) models. Result. The METASTRA-AI model is expected to be able to stratify most patients with limited effort end cost, based on parameters extracted semi-automatically from the medical files and images. The cases which are not reliably stratified through the AI model, are examined through a more detailed and personalised biomechanical VPH model. These METASTRA numerical tools are trained through an unprecedentedly large multicentric retrospective study (2000 cases) and validated against biomechanical ex vivo experiments (120 specimens). Conclusion. The METASTRA decision support system is tested in a multicentric prospective observational study (200 patients). The METASTRA approach is expected to cut down the indeterminate diagnoses from the current 60% down to 20% of cases. METASTRA project funded by the European Union, HEU topic HLTH-2022-12-01, grant 101080135

Prophylactic treatment is advised for metastatic bone disease patients with a high risk of fracture. Clinicians face the task of identifying these patients with high fracture risk and determining the optimal surgical treatment method. Subject-specific finite element (FE) models can aid in this decision process by predicting the mechanical effect of surgical treatment. In this study, we specifically evaluated the potential of FE models to simulate femoroplasty, as uncertainty remains whether this prophylactic procedure provides sufficient mechanical strengthening to the weight-bearing femur. In eight pairs of human cadaveric femurs artificial metastatic lesions were created. In each pair, an identical defect was milled in the left and right femur. Four pairs received a spherical lesion in the neck and the other four an ellipsoidal lesion in the intertrochanteric region, each at the medial, superior/lateral, anterior and posterior side, respectively. One femur of each pair was augmented with polymethylmethacrylate (5–10 ml), while the contralateral femur was left untreated. CT scans were made at three different time points: from the unaffected intact femurs, the defect femurs with lesion and the augmented femurs. Bone strength was measured by mechanical testing until failure in eight defect and eight augmented femurs. Nonlinear CT-based FE models were developed and validated against the experimentally measured bone strength. Subsequently, the validated FE model was applied to the available CT scans for the three different cases: intact (16 scans), defect (16) and augmented (8). The FE predicted strength was compared for the three different cases. The FE models predicted the experimental bone strength with a strong correspondence, both for the defect (R. 2. = 0.97, RMSE= 0.75 kN) and the augmented femurs (R. 2. = 0.90, RMSE = 0.98 kN). Although all lesions had a “moderate” to “high” risk for fracture according to the Mirels’ scoring system (score 7 or 8), three defect femurs did not fracture through the lesion (intertrochanteric anterior, lateral and posterior), indicating that these lesions did not act as a critical weak spot. In accordance with the experimental findings, the FE models indicated almost no reduction in strength between the intact and defect state for these femurs (0.02 ± 0.1%). For the remaining “critical” lesions, bone strength was reduced with 15.7% (± 14.9%) on average. The largest reduction was observed for lesions on the medial side (up to 43.1%). For the femurs with critical lesions, augmentation increased bone strength with 29.5% (± 29.7%) as compared to the defect cases, reaching strength values that were 2.5% (± 3.7%) higher than the intact bone strength. Our findings demonstrate that FE models can accurately predict the experimental bone strength before and after augmentation, thereby enabling to quantify the mechanical benefit of femoroplasty. This way FE models could aid in identifying suitable patients for whom femoroplasty provides sufficient increase in strength. For all lesions evaluated in this study, femoroplasty effectively restored the initial bone strength. Yet, additional studies on larger datasets with a wide variation of lesion types are required to confirm these results

Purpose: Stability of thoracic vertebrae affected by metastatic disease has been shown to be dependent on tumour size and bone density, but additional structural and geometric factors may also play a role in burst fracture risk assessment. The objective of this study was to use parametric finite element modeling to determine the effects of vertebral level, geometry, and metastatic compromise to the cortical shell on the risk of burst fracture initiation in the thoracic spine. Methods: An experimentally validated parametric biphasic finite element model of a metastatically involved spinal motion segment was analysed with scenarios representing motion segments from T2-T4 through T10-T12. Variations in vertebral geometry, kyphotic angulation and endplate angulation were evaluated. Additionally, four scenarios with transcortical breach of the tumour were compared to a central tumour scenario to determine the effect of cortical destruction. Vertebral bulge (VB), load induced canal narrowing (LICN), and posterior wall tensile hoop strain (PWTHS) were utilised as the main outcome parameters to assess burst fracture risk. Results: Burst fracture risk outcome parameters were largest in upper vertebrae, decreasing inferiorly at each subsequent level, with T11 exhibiting a 35.5% decrease in VB relative to T3, despite greater applied loads. An increase in endplate angles led to a 6.59% decrease in VB and a 2.38% decrease in LICN. A 5° increase in kyphotic angle further decreased VB and LICN by 7.29% and 4.34% respectively. Transcortical tumour scenarios led to an average decrease in PWTHS of 25.8%. Conclusions: Patients affected by spinal metastases in upper thoracic vertebrae may be at greater risk of burst fracture. Decreased burst fracture risk with greater thoracic kyphotic angulation may be due to a change in loading direction for curved segments, reducing the amount of pure axial load applied. Decreased tensile hoop strains are generated during loading of transcortical tumours. This may be attributed to large deformation of tumour tissue through the breach in the cortical shell, reducing the potential for burst fracture. Improved burst fracture risk assessment in the thoracic spine may motivate more informed clinical decision-making. Funding: Other Education Grant. Funding Parties: Natural Sciences and Engineering Research Council

Background. Periprosthetic femoral fractures following total hip arthroplasty are relatively uncommon but are associated with significant morbidity. With an increasing number of total hip arthroplasties being carried out in an aging population we need to ensure correct implants are chosen for our patients. A recent review of NJR data suggested a significantly higher revision risk for the Zimmer CPT stems due to periprosthetic fractures when compared to the Stryker Exeter stems. Objectives. Our aim was to compare the biomechanics of periprosthetic fractures around the CPT and Exeter V40 stems in a composite saw bone model to identify if a difference in fracture risk exists between the two stems. We also compared the engineering design of the two implants in order to analyse the possible effect this may have on fracture risk. Study Design & Methods. Fourteen composite femurs were divided into two groups and cemented using Palacos R cement with either the CPT or Exeter V40 stem by a single surgeon. The implanted femurs were then mounted onto an Instron machine and were axially loaded and torqued to fracture with an axial compressive force of 2000N over 10 seconds followed by a rotation of 40 degrees applied over 1 second. A power calculation from a previous composite saw bone model study suggested that a minimum of 6 implanted femurs would be required in each group. Results. The implanted femurs invariably sustained fracture patterns similar to the Vancouver B2 periprosthetic fracture which are commonly seen in clinical practice. Implanted femurs with CPT stems suffered periprosthetic fractures with less rotation when compared to those femurs with the Exeter V40 stem (20.10 versus 33.60, p<0.01). We also found that CPT implanted femurs were fracturing at significantly lower torque values when compared to the Exeter V40 implanted femurs (124Nm Versus 174Nm, p<0.01). The energy release rate (G111) for CPT stems was 21.8Nm compared to 61.2Nm for Exeter V40 stems. The higher energy release with Exeter stems led to more comminuted fractures in Exeter implanted femurs when compared to the CPT femurs, which fractured earlier, but with simpler fracture patterns. Finite element method (FEM) simulation analysis showed that fractures initiated between the prosthesis and cement at the proximal end of the femur. Two dimensional sections at the same height showed a difference in bone-cement-implant geometrics at the critical point of failure suggesting that a design cause may be the reason for the higher risk of periprosthetic fractures in CPT implanted femurs. Conclusions. Our observations may explain the higher revision risk secondary to periprosthetic fractures that has been observed with the CPT stem when compared to the Exeter V40 stem

Background. A large proportion of the expense incurred due to hip fractures arises due to secondary factors such as duration of hospital stay and additional theatre time due to surgical complications. Studies have shown that the use of intramedullary (IM) nail fixation presents a statistically higher risk of re-fracture than plating, which has been attributed to the stress riser at the end of the nail. It is not clear, however, if this situation also applies to unstable fractures, for which plating has a higher fixation failure rate. Moreover, biomechanical studies to date have not considered newer designs of IM nails which have been specifically designed to better distribute weight-bearing loads. This aim of this experimental study was to evaluate the re-fracture risk produced by a newer type of nailing system compared to an equivalent plate. Methods. Experimental testing was conducted using fourth generation Sawbones composite femurs and X-Bolt IM hip nail (n=4) and fracture plate (n=4) implants. An unstable pertrochanteric fracture pattern was used (AO classification: 31-A1 / 31-A2). Loading was applied along the peak loading vector experienced during walking, up to a maximum load of 500N. The risk of re-fracture was evaluated from equivalent strains measured using four rosette strain gauges on the surface of the bone at known stress riser locations. Results. Strain gauge readings determined that the equivalent strains in the femoral diaphysis were approximately 25% larger for the nail than the plate (p < 0.005). The strain levels at the location coinciding with the end of the plate were also larger for the nail, but not significantly (p > 0.26). Conclusions. Although the risk of re-fracture for displaced tronchantaric fractures was found to be larger for nailing than plating, measured strains were substantially lower than the failure strain of cortical bone (even when scaled for full weight-bearing loads of 1800N). This indicates that fracture risk is not present in either implant for bone of healthy quality, but may still become problematic in highly osteoporotic patients. Level of Evidence. IIb - Evidence from at least one well designed experimental trial

Spinal metastatic disease can result in burst fracture and neurologic compromise. This study aims to examine the effects of tumour location, shape and surface texture on burst fracture risk in the metastatic spine using a parametric poroelastic finite element model. Tumours were found to be most hazardous in the posterior region of the vertebral body, whereas the multiple tumour scenarios reduced risk. Tumour shape may affect the mechanism of burst fracture. Serrated and smooth outer tumour surfaces yielded similar trends. These results can be used to improve guidelines for burst fracture risk assessment in patients with spinal metastases. This study aims to examine the effects of tumour location, shape and surface texture on burst fracture risk in the metastatic spine. Both tumour location and shape are important factors in assessing the risk of burst fracture in the meta-static spine. Improving risk prediction may reduce burst fracture in patients with spinal metastases. Vertebral bulge increased over 30% when the tumour was moved posteriorly. Conversely, for the multi-tumour scenarios, vertebral bulge and axial displacement decreased by 41% and 35% in comparison to a single central tumour. Anterior and lateral movement demonstrated only small effects. Vertebral bulge increased proportionally to mediolateral tumour length and axial displacement increased proportionally to superior-inferior tumour length. Similar trends were seen with smoothed and serrated tumour surfaces. Using a parametric poroelastic finite element model of a metastaticaly involved T7 spinal motion segment, fourteen single and two multi-tumour scenarios were analyzed, each comprising approximately 24% tumour volume. Ellispoidal tumours were positioned in central, anterior, posterior and lateral locations. Tumour shape was altered by adjusting tumour radii for a centrally located tumour. Tumours were modeled using smoothed and serrated outer surface configurations. Burst fracture risk was assessed by measuring maximum vertebral bulge and axial displacement under load. Tumours were found to be most hazardous in the posterior region of the vertebral body, whereas the multi-tumour scenarios reduced risk. Modeling of tumour surface texture did not impact shape or location effects. Tumour shape may affect the mechanism of burst fracture. Funding: This study was supported by the National Science and Engineering Research Council

Aim: Hip fracture is the most devastating outcome of osteoporosis with high early mortality. Less is known about men in terms of long-term survival and fracture risk, information of outmost importance in terms of strategies for fracture prevention. The aim of this study was to evaluate long-term survival, new fractures and residual life time risk of fracture in a cohort of men with hip fracture in different ages. This is the first study with a follow-up above 10 years. Methods: All men above 20 yrs of age suffering a hip fracture 1984–1985 in Malmö, Sweden were identified and followed up to 22 years or death. All new radiographic examinations related to musculoskeletal trauma with or without fracture were individually registered. Survival and fractures were evaluated in 5-year age bands and age-groups (< 75, 75–84 and ≥85 years). Kaplan Meier survival analyses were used to evaluate mortality and fracture risk. Results: 263 men (74.2 yrs, range 33–101) with an index hip fracture due to low energy trauma were identified. 56% had cervical fractures and 44% trochanteric with 6% having concomitant fractures. 10 % had suffered a previous hip fracture. After 22 years 94% were dead; 32 % within 1 yr, 62% within 5 yrs and 79% within 10yrs. Mean age at death was 80.1 yr (range 41–101), equal to a mean of 5.8 yrs above the mean age for fracture. The 50% survival in respectively age groups < 75, 75–84 and ≥ 85 years was 7 yrs, 2 yrs and 3 months. 74/263 (28%) suffered totally 131 fractures (1.8, range 1–7 fractures/patient) at 121 occasions. The majority suffered only a new fracture at one occasion (n=48, 65%). 14% of the fracture occasions occurred within 1 yr and half of the fractures occurred within 3.2 yrs. Mortality adjusted life time risk of fracture was 62% and 10-year risk of fracture was 47%. Conclusion: In this study we report fracture risk and mortality in a residual life time perspective in men after hip fracture. Men suffer hip fractures earlier in life and have, compared to women from the same cohort, higher early mortality (32% resp 21% (1 yr)) and lower residual lifetime risk of fracture (28% resp 45%). 1. The high early mortality probably mirrors a higher morbidity among male hip fracture patients. The consequence is that fracture preventing strategies need to consider both gender, age and mortality

We aimed to audit the results of one stop fragility fracture risk assessment service at fracture clinic for non-hip fractures in 50–75 years old patients at Newcastle General Hospital. Currently, fewer than 30% of patients with fragility fractures benefit from secondary prevention in the form of comprehensive risk assessment and bone protection because of multifactorial reasons. We have a fragility fracture risk assessment service staffed by an Osteoporosis Specialist Nurse equipped with a DEXA scanner located at the fracture clinic itself. We carried out a retrospective audit of 349 patients of 50–75 years with suspected non-hip fractures referred from A& E Department from October 2006 to September 2007. Patients over 75 years were excluded because as per NICE guidelines, they should receive bone protection without need of a DEXA scan. Out of these 349 patients with suspected fractures, 171 had fragility fractures. Median age was 64 years. 69 patients had humerus fracture, 65 had forearm fracture and 23 patients had ankle fracture and 14 had metatarsal fractures. Fracture risk assessment was carried out in 120 (70%) patients. Thirty Seven (31%) patients had osteoporosis and bone protection was recommended to GP. 38 (32%) had osteopenia and lifestyle advice was provided. 45 (37%) had normal axial bone densitometry. 90% patients had DEXA scan at the same time of fracture clinic appointment. Patients with male gender, undisplaced fracture and fewer fracture clinic appointments were more likely to miss fracture risk assessment. Our experience suggests that locating fragility fracture risk assessment service co-ordinated by an Osteoporosis Specialist Nurse at fracture clinic is an efficient way of providing secondary prevention for patients with fragility fractures. This can improve team communication, eliminate delay and improve patient compliance because of ‘One Stop Shop’ service at the time of fracture clinic appointment

There is an established link between bone quality and fracture risk. It has been suggested that reduced bone quality will also reduce the toughening mechanisms displayed during loading at a high strain rate. We hypothesised that partially decalcified bone will not demonstrate an increase in force required to cause failure when comparing low and high strain rate loading. Mechanical properties were defined by the maximum force at failure. Bone quality was defined by the mineral content. This was altered by subjecting the bones to ultrasonically assisted decalcification in 10M EDTA to achieve an average 18% mineral reduction (A 70 yr old woman has approx 18% of her peak bone mass). 20 pairs of sheep femurs were harvested and split into four equal groups: normal bone quality, fast strain rate (NF); normal bone quality, slow strain rate (NS); low bone quality, fast strain rate (LF) and low bone quality, slow strain rate (LS). All mechanical testing was carried out by means of 3-point bending. Load representing the slow strain rate was applied by a mechanical testing machine (Zwick) at a rate resulting in a deflection of 1mm/s. The dynamic loading was applied by a custom designed pneumatic ram at a mean rate of deflection between the specimens of 2983 mm/s (±SD 1155), this equates to strain rates experienced in a road traffic accident. The following results for force at failure were found (mean ± SD). NF: Force 5503N (± 1012); NS: Force 3969N (± 572); LF: Force 3485N (± 772); LS: Force 3165N (± 605). Groups were compared using a Mann-Whitney U test. Significant results were found between the following groups: Normal bone quality, strain rate compared (NF-NS) p<0.002; Fast strain rate, bone quality compared (NF-LF) p=0.008; Slow strain rate, bone quality compared (NS-LS) p=0.02. No statistical significance was found when comparing low bone quality, strain rate compared (LF-LS) p=0.47. These results show that normal healthy bone has an ability to withstand higher strain rates which protects it against fracture. This ability to withstand high strain rates is lost in decalcified bone making it more susceptible to fracture. The results of this study indicate the importance of strain rate reduction as well as energy absorption in the design of hip protectors and in environmental modifications

Introduction: Bone mineral density (BMD) is currently the gold standard in predicting osteoporotic fracture, but evidence suggests that over one third of such fractures occur in those with osteopenia or even normal BMD. The level of bone turnover may affect bone quality in these patients independently of BMD. Bone markers have evolved as tools in monitoring anti-resorptive treatment in osteoporosis. Aims: The aim of this study was to investigate if levels of bone markers in postmenopausal women could be used as an adjunct to BMD measurements in the assessment of fragility fracture risk. Patients and Methods: 60 postmenopausal women (30 osteoporotic, 30 with normal BDM) were studied. A single BMD measurement by dual energy x-ray absorptiometry (DEXA) enabled categorisation. Serum bone formation markers (bone specific alkaline phosphatase (BSAP) and osteocalcin (OC)), and resorption marker (C-telopetide of type 1 collagen (CTX)), were measured. History of low trauma fracture was documented for each woman. Results: 36% of the osteoporotic group had experienced at least one fragility fracture. However, the femoral neck and combined spinal BMD in these women was not significantly different from the 64% of osteoporotic women who had no prior fracture. There was also no significant difference in the age of women in both subgroups. Serum bone markers were significantly increased in the osteoporotic fracture subgroup when compared to the non-fracture subgroup and also to the non-osteoporotic controls. The largest increases were seen in the levels of CTX. Smaller increases in all markers were seen when the non-fracture subgroup was compared to the non-osteoporotic control group but these increases did not reach statistical significance. Conclusions: Bone turnover is significantly increased in postmenopausal osteoporotic women with previous fracture compared to both osteoporotic non-fracture counterparts and non-osteoporotic controls. This suggests higher bone turnover will increase fracture risk in osteoporotic women. It is possible that combining 2 or 3 markers to produce an “index of bone turnover” would be a useful tool when used in addition to BMD to identify those at greatest fracture risk

Introduction. With an ageing population comes an increased prevalence of osteoporosis and associated fracture. Whilst treatment of the condition following such a fracture is partially effective, primary prevention through screening and appropriate follow-up is the ideal. In order to assess a population's risk of fracture, paper questionnaires would traditionally have to be sent, however this is an wasteful and costly. A more efficient method may be to have patients assess their own FRAX score through a modified computer application. Aim. To investigate the feasibility of patients self-reporting their FRAX score from the use of a touch screen application. Methods. A patient-friendly application based on the FRAX questionnaire was developed for use on iPad. This was then trialled on inpatients and outpatients at the RNOH, Stanmore and at 2 GP's surgeries. A paper questionnaire then was used to assess ease of use of the application. Results. 314 patients completed the iPad application with 68 patients over 55 completing the paper questionnaire. The mean useability score was 2.6 (1-easy, 10-hard). 75% of respondents preferred using a touch screen application than paper or phone surveys and 83% stated they would use the touch screen if it was offered in GP surgeries. Discussion and Conclusion. Touch screen applications are readily used to self-report fracture risk by the majority of the over 55 population. Applications such as these have the potential to collect large amounts of data quickly and cheaply, as well as engaging patients in becoming aware of the risks of osteoporosis

Objectives: To formulate a scoring system enabling decision making for prophylactic stabilization of the femur following surgical resection of a soft tissue sarcoma (STS) of the thigh. Methods: A logistic regression model was developed using patient variables collected from a prospective database. The test group included 22 patients with radiation-related pathological femur fracture following surgery and radiation for a thigh STS. The control group of 79 patients had similar treatment but without a fracture. No patients received chemotherapy. Mean follow-up was 8.6 years. Variables examined were: Age (< 49, 50–70, > 70 years), gender, tumor size (0–7, 8–14, > 14 cm), radiation dose (low=5000 cGy, high> 6000 cGy), extent of periosteal stripping (< 10, 10–20, > 20 cm) and thigh compartment (posterior, adductor, anterior). A score was assigned to each variable category based on the coefficients obtained in the logistic regression model. Results: Based on the regression model and an optimal cut-point, the ability to predict radiation associated fracture risk was 91% sensitive and 86% specific. The area under the Receiver Operating Characteristic (ROC) curve was 0.9, which supports this model as a very accurate predictor. Conclusions: Radiation-related femur fractures following combined surgery and radiation treatment for STS are uncommon, but are difficult to manage and their non-union rate is extremely high. These results suggest that it is possible to predict radiation-associated pathological fracture risk with high sensitivity and specificity. This would allow identification of high risk patients and treatment with prophylactic IM nail stabilization. Presentation of this model as a clinical nomogram will facilitate its clinical use

Purpose: We searched to define a fracture index useful for predicting the risk of bone fracture in children with essential bone cysts. Material and method: We reviewed 96 children with an essential bone cyst. The following clinical data were recorded: age, cyst localisation, circumstances of diagnosis. The radiological analysis was based on 193 AP and lateral x-rays. We measured: 1) the distance separating the superior pole of the cyst from the suprajacent growth cartilage, 2) the largest cyst diameter, 3) the greatest cyst height, 4) the thinnest cortical width facing the cyst, 5) the cyst surface area calculated exactly using surface area software and expressed as a a ratio of shaft diameter (S/d2, Kaelin index). These different parameters were compared for cysts associated with fracture or not. Results: Mean age at diagnosis was 10.4 years (range 2 – 12.8 years). Most of the cysts were located in the upper portion of the humerus (72%). Fracture was the inaugural sign in 68% of the cases. Comparing the two cohorts of patients demonstrated that the following differences were significant (Student’s t test): 1) cyst width (p=0.0038): below 16 mm none of the cysts fractured. For wider cysts, there was no difference between the fracture and non-fracture cysts. 2) cortical thickness (p=0.0002); cortical thickness greater than 5 mm protected against fracture. If the cortical measured less than 3 mm, the risk of fracture was greater than 50%. 3) Kaelin index: (p< 0.0001) was directly correlated with fracture risk but no cutoff could be identified. For an 80 – 100% risk of fracture, the cyst must have the following characteristics: width > 30 mm, height > 75 mm, cortical thickness < 2.4 mm, Kaelin index > 5. For a 50% risk of fracture, the cyst must have the following characteristics: width > 24 mm, height > 55 mm, cortical thickness < 3 mm, Kaelin index > 3. Conclusion: The Kaelin index is reliable but difficult to calculate in the consultation setting. Cortical thickness is a good indicator. Its predictive value can be improved by correlating the height of the cyst with its width. These measure can be obtained easily during consultation

Total Hip Replacement (THR) is one of the most successful operations in all of medicine, however surgeons just rely on their experience and expertise when choosing between cemented or cementless stem, without having any quantitative guidelines. The aim of this project is to provide clinicians with some tools to support in their decision making. A novel method based on bone mineral density (BMD) measurements and assessments was developed 1) to estimate the periprosthetic fracture risk (FR) while press-fitting cementless stem; 2) to evaluate post-operative bone remodeling in terms of BMD changes after primary THR. Data for 5 out of over 70 patients (already involved in a previous study. 1. ) that underwent primary THA in Iceland were selected for developing novel methods to assess intra-operative FR and bone mineral density (BMD) changes after the operation. For each patient three CT images were acquired (Philips Brilliance 64 Spiral-CT, 120 kVp, slice thickness: 1 mm, slice increment: 0.5 mm): pre-op, 24 hours and 1 year post-operative. Pre-op CT scan was used to create 3D finite element model (Materialise Mimics) of the proximal femur. The material properties were assigned based on Hounsfield Units. Different strategies were analyzed for simulating the press-fitting operation, developing what has already been done in prior study. 1. In the finite element simulation (Ansys Workbench), a pressure (related to the implant hammering force of 9.25 kN. 2. ) was applied around the femur's hollow for the stem and the distribution of maximum principal elastic strain over the bone was calculated. Assuming a critical failure value. 3. of 7300 με, the percentage of fractured elements was calculated (i.e. FR). Post 24 hours and Post 1 year CT images were co-registrated and compared (Materialise Mimics) in order to assess BMD changes. Successively, volumes of bone lost and bone gained were calculated and represented in a 3D model. Age and gender should not be taken as unique indicators to choose between implants typologies, since also three dimensional BMD distribution along with volume of cortical bone influence the risk of periprosthetic fractures. Highest FR values were experienced in the calcar-femorale zone and in similar location on the posterior side. BMD loss volume fractions after 1 year were usually higher than BMD gain ones. Consistently with prior studies. 4. , BMD loss was mainly concentrated around the proximal end (lesser trochanter area, outer bone). If present, BMD gain occurred at the distal end (below stem's tip) or proximally (lesser trochanter area, interface contact with the stem). The use of clinical data for BMD assessments serves as an important tool to develop a quantitative method which will support surgeons in their decisions, guiding them to the optimal implant for the patient. Knowing the risk of fracture if choosing a cementless stem and being aware of how the bone will remodel around the stem in one year's time can eventually lead to reduction in revisions and increased quality of life for the patient. Further work will target analysis of a larger cohort of patients and validate FE models

Purpose: The mechanical integrity of vertebral bone is compromised when metastatic cancer cells migrate to the spine, rendering it susceptible to burst fracture under physiologic loading. Risk of burst fracture has been shown to be dependent on the magnitude of the applied load, however limited work has been conducted to determine the effect of load type on the stability of the metastatic spine. The objective of this study was to evaluate the effect of multiple loading conditions and the presence of the ribcage on a metastatically-involved thoracic spinal motion segment. Methods: A parametric biphasic finite element model was developed and validated against experimental data under axial compressive loading. Fifteen loading scenarios were analysed, including axial compression, flexion, extension, lateral bending, torsion, and combined loads. Axial loads were applied up to 800N and moment loads up to 2Nm. Multiple analyses were conducted with and without the ribcage to assess its impact on thoracic spinal stability. Vertebral bulge (VB) and load induced canal narrowing (LICN) were utilised as main outcome parameters to assess burst fracture risk. Results: For single loads, pure 800N axial loading yielded the highest level of VB (0.48mm) and LICN (0.26mm). The smallest increases in VB were measured in 1Nm pure flexion (0.018mm). Combined loading scenarios also demonstrated that axial loading is the principal factor contributing to VB, as changes in VB for combined loads were no greater than 4.35% of VB under axial loading alone. Inclusion of the ribcage was found to reduce the potential for burst fracture by 27% under axial load. Conclusions: Axial loading is the predominant load type leading to increased risk of burst fracture initiation. Rotational loading (bending, flexion and extension) led to only moderate increases in risk. The ribcage provides substantial stability to reduce overall risk of burst fracture. These findings are important in developing a more comprehensive understanding of burst fracture mechanics in the metastatic spine and in directing future modeling efforts. The results in this study may also be useful in advising less harmful activities for patients affected by lytic spinal metastases. Funding: Other Education Grant. Funding Parties: Natural Sciences and Engineering Research Council