Predicting the stiffness and strength of human femurs with real metastatic tumors

Patient-specific finite element analysis for assessing hip fracture risk in aging populations

The femur is one of the most important bone in the human body, as it supports the body's weight and helps with movement. The aging global population presents a significant challenge, leading to an increasing demand for artificial joints, particularly in knee and hip replacements, which are among the most prevalent surgical procedures worldwide. This study focuses on hip fractures, a common consequence of osteoporotic fractures in the elderly population. To accurately predict individual bone properties and assess fracture risk, patient-specific finite element models (FEM) were developed using CT data from healthy male individuals. The study employed ANSYS 2023 R2 software to estimate fracture loads under simulated single stance loading conditions, considering strain-based failure criteria. The FEM bone models underwent meticulous reconstruction, incorporating geometrical and mechanical properties crucial for fracture risk assessment. Results revealed an underestimation of the ultimate bearing capacity of bones, indicating potential fractures even during routine activities. The study explored variations in bone density, failure loads, and density/load ratios among different specimens, emphasizing the complexity of bone strength determination. Discussion of findings highlighted discrepancies between simulation results and previous studies, suggesting the need for optimization in modelling approaches. The strain-based yield criterion proved accurate in predicting fracture initiation but required adjustments for better load predictions. The study underscores the importance of refining density-elasticity relationships, investigating boundary conditions, and optimizing models throughin vitrotesting for enhanced clinical applicability in assessing hip fracture risk. In conclusion, this research contributes valuable insights into developing patient-specific FEM bone models for clinical hip fracture risk assessment, emphasizing the need for further refinement and optimization for accurate predictions and enhanced clinical utility.

The influence of femoral lytic tumors segmentation on autonomous finite element analysis

BACKGROUND

The validated CT-based autonomous finite element system Simfini (Yosibash et al., 2020) is used in clinical practice to assist orthopedic oncologists in determining the risk of pathological femoral fractures due to metastatic tumors. The finite element models are created automatically from CT-scans, assigning to lytic tumors a relatively low stiffness as if these were a low-density bone tissue because the tumors could not be automatically identified.

METHODS

The newly developed automatic deep learning algorithm which segments lytic tumors in femurs, presented in (Rachmil et al., 2023), was integrated into Simfini. Finite element models of twenty femurs from ten CT-scans of patients with femoral lytic tumors were analyzed three times using: the original methodology without tumor segmentation, manual segmentation of the lytic tumors, and the new automatic segmentation deep learning algorithm to identify lytic tumors. The influence of explicitly incorporating tumors in the autonomous finite element analysis on computed principal strains is quantified. These serve as an indicator of femoral fracture and are therefore of clinical significance.

FINDINGS

Autonomous finite element models with segmented lytic tumors had generally larger strains in regions affected by the tumor. The deep learning and manual segmentation of tumors resulted in similar average principal strains in 19 regions out of the 23 regions within 15 femurs with lytic tumors. A high dice similarity score of the automatic deep learning tumor segmentation did not necessarily correspond to minor differences compared to manual segmentation.

INTERPRETATION

Automatic tumor segmentation by deep learning allows their incorporation into an autonomous finite element system, resulting generally in elevated averaged principal strains that may better predict pathological femoral fractures.

Examining agreement between finite element modelling methodologies in predicting pathological fracture risk in proximal femurs with bone metastases

BACKGROUND

Finite element modelling methodologies available for assessing femurs with metastases accurately predict strength and pathological fracture risk which has led them to being considered for implementation into the clinic. However, the models available use varying material models, loading conditions, and critical thresholds. The aim of this study was to determine the agreement between finite element modelling methodologies in assessing fracture risk in proximal femurs with metastases.

METHODS

CT images of the proximal femur were obtained of 7 patients who presented with a pathologic femoral fracture (fracture group) and the contralateral femur of 11 patients scheduled for prophylactic surgery (non-fracture group). Fracture risk was predicted for each patient following three established finite modelling methodologies which have previously shown to accurately predict strength and determine fracture risk: non-linear isotropic -based model, strain fold ratio -based model, Hoffman failure criteria -based model.

FINDINGS

The methodologies demonstrated good diagnostic accuracy in assessing fracture risk (AUC = 0.77, 0.73, and 0.67). There was a stronger monotonic association between the non-linear isotropic and Hoffman -based models (τ = 0.74) than with the strain fold ratio model (τ = -0.24 and - 0.37). There was moderate or low agreement between methodologies in discriminating between individuals at high or low risk of fracture (κ = 0.20, 0.39, and 0.62).

INTERPRETATION

The present results suggest there may be a lack of consistency in the management of pathological fractures in the proximal femur based on the finite element modelling methodologies.

Mechanoregulation may drive osteolysis during bone metastasis: A finite element analysis of the mechanical environment within bone tissue during bone metastasis and osteolytic resorption

Metastatic bone disease occurs in 70-80% of advanced breast cancer patients and bone tissue is accepted to have attractive physical properties that facilitate cancer cell attraction, adhesion, and invasion. Bone cells also facilitate tumour invasion by biochemical signalling and through resorption of the bone matrix (osteolysis), which releases factors that further stimulate tumour cell activity. The evolving mechanical environment during tumour invasion might play an important role in these processes, as the activity of both bone and cancer cells is regulated by mechanical cues. In particular bone loss and altered mineralisation have been reported, yet how these alter the mechanical environment local to bone and tumour cells is unknown. The objective of this study is to quantify changes in the mechanical environment within bone tissue, during bone metastasis and osteolytic resorption, using finite element analysis (FEA) models reconstructed from high-resolution μCT images of metastatic mouse bone. In particular, we quantify time-dependent changes in mechanical stimuli, local to and distant from an invading tumour mass, to investigate putative mechanobiological cues for osteolysis during bone metastasis. We report here that in early metastasis (3 weeks after tumour inoculation), there was a decrease in strain distribution within the proximal femur trabecular and distal cortical bone tissue. These changes in the mechanical environment preceded extensive osteolytic destruction, but coincided with the onset of early osteolysis, cortical thickening and mineralisation of proximal and distal femur bone. We propose that early changes in the mechanical environment within bone tissue may activate resorption by osteoclast cells and thereby contribute to the extensive osteolytic bone loss at later stage (6 weeks) bone metastasis.

On the influence of computed tomography's slice thickness on computer tomography based finite element analyses results

BACKGROUND

Patient-specific autonomous finite element analyses of femurs, based on clinical computed tomography scans may be used to monitor the progression of bone-related diseases. Some CT scan protocols provide lower resolution (slice thickness of 3 mm) that affects the accuracy. To investigate the impact of low-resolution scans on the CT-based finite element analyses results, identical CT raw data were reconstructed twice to generate a 1 mm ("gold standard") and a 3 mm slice thickness scans.

METHODS

CT-based finite element analyses of twenty-four femurs (twelve patients) under stance and sideways fall loads were performed based on 1 and 3 mm slice thickness scans. Bone volume, load direction, and strains were extracted at different locations along the femurs and differences were evaluated.

FINDINGS

Average differences in bone volume were 1.0 ± 1.5%. The largest average difference in strains in stance position was in the neck region (11.0 ± 13.4%), whereas in other regions these were much smaller. For sidewise fall loading, the average differences were at most 9.2 ± 16.0%.

INTERPRETATION

Whole-body low dose CT scans (3 mm-slice thickness) are suboptimal for monitoring strain changes in patient's femurs but may allow longitudinal studies if larger than 5% in all areas and larger than 12% in the upper neck. CT-based finite element analyses with slice thickness of 3 mm may be used in clinical practice for patients with smoldering myeloma to associate changes in strains with progression to active myeloma if above ∼10%.

Finite Element Model-Computed Mechanical Behavior of Femurs with Metastatic Disease Varies Between Physiologic and Idealized Loading Simulations

Background and objectives:

Femurs affected by metastatic bone disease (MBD) frequently undergo surgery to prevent impending pathologic fractures due to clinician-perceived increases in fracture risk. Finite element (FE) models can provide more objective assessments of fracture risk. However, FE models of femurs with MBD have implemented strain- and strength-based estimates of fracture risk under a wide variety of loading configurations, and “physiologic” loading models typically simulate a single abductor force. Due to these variations, it is currently difficult to interpret mechanical fracture risk results across studies of femoral MBD. Our aims were to evaluate (1) differences in mechanical behavior between idealized loading configurations and those incorporating physiologic muscle forces, and (2) differences in the rankings of mechanical behavior between different loading configurations, in FE simulations to predict fracture risk in femurs with MBD.

Methods:

We evaluated 9 different patient-specific FE loading simulations for a cohort of 54 MBD femurs: strain outcome simulations—physiologic (normal walking [NW], stair ascent [SA], stumbling), and joint contact only (NW contact force, excluding muscle forces); strength outcome simulations—physiologic (NW, SA), joint contact only, offset torsion, and sideways fall. Tensile principal strain and femur strength were compared between simulations using statistical analyses.

Results:

Tensile principal strain was 26% higher ( R2 = 0.719, P < .001) and femur strength was 4% lower ( R2 = 0.984, P < .001) in simulations excluding physiologic muscle forces. Rankings of the mechanical predictions were correlated between the strain outcome simulations (ρ = 0.723 to 0.990, P < .001), and between strength outcome simulations (ρ = 0.524 to 0.984, P < .001).

Conclusions:

Overall, simulations incorporating physiologic muscle forces affected local strain outcomes more than global strength outcomes. Absolute values of strain and strength computed using idealized (no muscle forces) and physiologic loading configurations should be used within the appropriate context when interpreting fracture risk in femurs with MBD.

Predicting fracture in the proximal humerus using phase field models

Proximal humerus impacted fractures are of clinical concern in the elderly population. Prediction of such fractures by CT-based finite element methods encounters several major obstacles such as heterogeneous mechanical properties and fracture due to compressive strains. We herein propose to investigate a variation of the phase field method (PFM) embedded into the finite cell method (FCM) to simulate impacted humeral fractures in fresh frozen human humeri. The force-strain response, failure loads and the fracture path are compared to experimental observations for validation purposes. The PFM (by means of the regularization parameter ℓ₀) is first calibrated by one experiment and thereafter used for the prediction of the mechanical response of two other human fresh frozen humeri. All humeri are fractured at the surgical neck and strains are monitored by Digital Image Correlation (DIC). Experimental strains in the elastic regime are reproduced with good agreement (R²=0.726), similarly to the validated finite element method (Dahan et al., 2022). The failure pattern and fracture evolution at the surgical neck predicted by the PFM mimic extremely well the experimental observations for all three humeri. The maximum relative error in the computed failure loads is 3.8%. To the best of our knowledge this is the first method that can predict well the experimental compressive failure pattern as well as the force-strain relationship in proximal humerus fractures.

Can neck fractures in proximal humeri be predicted by CT-based FEA?

BACKGROUND

Proximal humeri fractures at anatomical and surgical neck (∼5% and ∼50% incidence respectively) are frequent in elderly population. Yet, neither in-vitro experiments nor CT-based finite element analyses (CTFEA) have investigated these in depth. Herein we enhance (Dahan et al., 2019) (addressing anatomical neck fractures) by more experiments and specimens, accounting for surgical neck fractures and explore CTFEA's prediction of humeri mechanical response and yield force.

METHODS

Four fresh frozen human humeri were tested in a new experimental configuration inducing surgical neck fractures. Digital image correlation (DIC) provided strains and displacements on humeri surfaces and used to validate CTFEA predictions. CTFEA were enhanced herein to improve the accuracy at the proximal neck: A cortical bone mapping (CBM) algorithm was implemented to overcome insufficient scanning resolution, and a new trabecular material mapping was investigated.

RESULTS

The new experimental setting induced impacted surgical neck fractures in all humeri. Excellent DIC to CTFEA correlation in strains was obtained at the shaft (slope 0.984, R²=0.99) and a fair agreement (slope 0.807, R²=0.73) at the neck. CBM algorithm had worsened the correlation, whereas the new material mapping had a negligible influence. Yield loads predictions improved considerably when trabecular yielding (maximum principal strain criterion) was considered instead of surface cortical yielding.

DISCUSSION

CTFEA well predicts strains on the shaft and reasonably well on the neck. This enhances former conclusions by past studies conducted using SGs, now also evident by DIC. Yield load prediction for surgical neck fractures (involving crushing of trabecular bone) is predicted better by trabecular failure laws rather than cortex ones. Further FEA studies using trabecular orthotropic constitutive models and failure laws are warrant.

Finite Element Assessment of Bone Fragility from Clinical Images

PURPOSE OF REVIEW

We re-evaluated clinical applications of image-to-FE models to understand if clinical advantages are already evident, which proposals are promising, and which questions are still open.

RECENT FINDINGS

CT-to-FE is useful in longitudinal treatment evaluation and groups discrimination. In metastatic lesions, CT-to-FE strength alone accurately predicts impending femoral fractures. In osteoporosis, strength from CT-to-FE or DXA-to-FE predicts incident fractures similarly to DXA-aBMD. Coupling loads and strength (possibly in dynamic models) may improve prediction. One promising MRI-to-FE workflow may now be tested on clinical data. Evidence of artificial intelligence usefulness is appearing. CT-to-FE is already clinical in opportunistic CT screening for osteoporosis, and risk of metastasis-related impending fractures. Short-term keys to improve image-to-FE in osteoporosis may be coupling FE with fall risk estimates, pool FE results with other parameters through robust artificial intelligence approaches, and increase reproducibility and cross-validation of models. Modeling bone modifications over time and bone fracture mechanics are still open issues.

Fracture Risk Evaluation of Bone Metastases: A Burning Issue

Major progress has been achieved to treat cancer patients and survival has improved considerably, even for stage-IV bone metastatic patients. Locomotive health has become a crucial issue for patient autonomy and quality of life. The centerpiece of the reflection lies in the fracture risk evaluation of bone metastasis to guide physician decision regarding physical activity, antiresorptive agent prescription, and local intervention by radiotherapy, surgery, and interventional radiology. A key mandatory step, since bone metastases may be asymptomatic and disseminated throughout the skeleton, is to identify the bone metastasis location by cartography, especially within weight-bearing bones. For every location, the fracture risk evaluation relies on qualitative approaches using imagery and scores such as Mirels and spinal instability neoplastic score (SINS). This approach, however, has important limitations and there is a need to develop new tools for bone metastatic and myeloma fracture risk evaluation. Personalized numerical simulation qCT-based imaging constitutes one of these emerging tools to assess bone tumoral strength and estimate the femoral and vertebral fracture risk. The next generation of numerical simulation and artificial intelligence will take into account multiple loadings to integrate movement and obtain conditions even closer to real-life, in order to guide patient rehabilitation and activity within a personalized-medicine approach.

Assessing hip fracture risk in type-2 diabetic patients using CT-based autonomous finite element methods : a feasibility study

AIMS

Type 2 diabetes mellitus (T2DM) impairs bone strength and is a significant risk factor for hip fracture, yet currently there is no reliable tool to assess this risk. Most risk stratification methods rely on bone mineral density, which is not impaired by diabetes, rendering current tests ineffective. CT-based finite element analysis (CTFEA) calculates the mechanical response of bone to load and uses the yield strain, which is reduced in T2DM patients, to measure bone strength. The purpose of this feasibility study was to examine whether CTFEA could be used to assess the hip fracture risk for T2DM patients.

METHODS

A retrospective cohort study was undertaken using autonomous CTFEA performed on existing abdominal or pelvic CT data comparing two groups of T2DM patients: a study group of 27 patients who had sustained a hip fracture within the year following the CT scan and a control group of 24 patients who did not have a hip fracture within one year. The main outcome of the CTFEA is a novel measure of hip bone strength termed the Hip Strength Score (HSS).

RESULTS

The HSS was significantly lower in the study group (1.76 (SD 0.46)) than in the control group (2.31 (SD 0.74); p = 0.002). A multivariate model showed the odds of having a hip fracture were 17 times greater in patients who had an HSS ≤ 2.2. The CTFEA has a sensitivity of 89%, a specificity of 76%, and an area under the curve of 0.90.

CONCLUSION

This preliminary study demonstrates the feasibility of using a CTFEA-based bone strength parameter to assess hip fracture risk in a population of T2DM patients. Cite this article: Bone Joint J 2021;103-B(9):1497-1504.

Fracture Risk of Long Bone Metastases: A Review of Current and New Decision-Making Tools for Prophylactic Surgery

Simple Summary

Long bone metastases are frequently a pivotal point in the oncological history of patients. Weakening of the bone results in pathologic fractures that not only compromise patient function but also their survival. Therefore, the main issue for tumor boards remains timely assessment of the risk of fracture, as this is a key consideration in providing preventive surgery while also avoiding overtreatment. As the Mirels scoring system takes into account both the radiological and the clinical criteria, it has been used worldwide since the 1990s. However, due to increasing concern regarding the lack of accuracy, new thresholds have been defined for the identification of impending fractures that require prophylactic surgery, on the basis of axial cortical involvement and biomechanical models involving quantitative computed tomography. The aim of this review is to establish a state-of-the-art of the risk assessment of long bone metastases fractures, from simple radiologic scores to more complex multidimensional bone models, in order to define new decision-making tools.

Abstract

Long bone pathological fractures very much reflect bone metastases morbidity in many types of cancer. Bearing in mind that they not only compromise patient function but also survival, identifying impending fractures before the actual event is one of the main concerns for tumor boards. Indeed, timely prophylactic surgery has been demonstrated to increase patient quality of life as well as survival. However, early surgery for long bone metastases remains controversial as the current fracture risk assessment tools lack accuracy. This review first focuses on the gold standard Mirels rating system. It then explores other unique imaging thresholds such as axial or circumferential cortical involvement and the merits of nuclear imaging tools. To overcome the lack of specificity, other fracture prediction strategies have focused on biomechanical models based on quantitative computed tomography (CT): computed tomography rigidity analysis (CT-RA) and finite element analysis (CT-FEA). Despite their higher specificities in impending fracture assessment, their limited availability, along with a need for standardization, have limited their use in everyday practice. Currently, the prediction of long bone pathologic fractures is a multifactorial process. In this regard, machine learning could potentially be of value by taking into account clinical survival prediction as well as clinical and improved CT-RA/FEA data.

Alterations in femoral neck strength following pelvic irradiation. A finite element analysis of simulated eccentric forces using bone density data derived from CT

BACKGROUND AND PURPOSE

Radiotherapy is known to produce long-term skeletal complications. We aim to evaluate the biomechanical effect on femoral neck bone from hypothetical eccentric loads on pre- and post-radiotherapy CT-images for patients treated for rectal cancer.

MATERIAL AND METHODS

Pre- and post-radiotherapy CT-images of rectal cancer from 10 patients were selected randomly. The cortical and trabecular bone was segmented by hand. The biomechanical simulations of 4 eccentric loads and one load aligned with the femoral neck axis were completed using finite element analysis (FEA) in both pre- and post-patient models.

RESULTS

A comparative statistical study was completed of pre- and post-radiotherapy patient models of stress and displacement factors. Significant differences were found in eccentric loads in both factors. Natural load has a significant difference in stress, but no differences were found for displacements. The absolute difference in eccentric load applied to the anterior area location on the same patient implies from 5.3% to 40.5% of the stress yield values reported in previous studies.

CONCLUSIONS

Loads applied to the anterior area of the femoral head must be considered in fracture simulations because the percentage of yield stress of pre- and post-irradiated bones shows a significant biomechanical change.

Patient-specific computed tomography-based finite element analysis: a new tool to assess fracture risk in benign bone lesions of the femur

BACKGROUND

Most benign active and latent lesions of proximal femur do not predispose a patient to a pathologic fracture. Nonetheless, there is a tendency to perform internal fixation due to the lack of accurate clinical tools that may reliably confirm low risk of pathologic fracture. As many as 30% of these surgeries may be unnecessary. A patient-specific CT-based finite element analysis may quantify bone strength and risk of fracture under normal weight-bearing conditions.

METHODS

The clinical relevance of such finite element analysis was investigated in a retrospective study on a cohort of 17 patients. Finite element analysis results (high risk = indication for surgery, low or moderate risk = follow-up) were compared to actual clinical decisions (surgery vs follow-up). All patients predicted by the finite element analysis as high risk underwent internal fixation and had good outcomes (n = 6).

FINDINGS

Four of the 11 low- and moderate-risk finite element analysis patients (36%) were operated immediately, and seven (64%) were either operated after a delay of at least 6 months or were never operated. None sustained a pathologic fracture. Patients who were predicted as low fracture risk by finite element analysis remained fracture-free for a minimal period of 6 months. Prediction of high risk of pathologic fracture by finite element analysis was in complete agreement with the conventional clinical evaluation.

INTERPRETATION

We consider finite element analysis a promising decision support system for the management of patients with benign tumors of femur, and that it may reliably endorse the decision to withhold surgery for patients at low fracture-risk.

Trends in the Characterization of the Proximal Humerus in Biomechanical Studies: A Review

Proximal humerus fractures are becoming more common due to the aging of the population, and more related scientific research is also emerging. Biomechanical studies attempt to optimize treatments, taking into consideration the factors involved, to obtain the best possible treatment scenario. To achieve this, the use of finite element analysis (FEA) is necessary, to experiment with situations that are difficult to replicate, and which are sometimes unethical. Furthermore, low costs and time requirements make FEA the perfect choice for biomechanical studies. Part of the complete process of an FEA involves three-dimensional (3D) bone modeling, mechanical properties assignment, and meshing the bone model to be analyzed. Due to the lack of standardization for bone modeling, properties assignment, and the meshing processes, this article aims to review the most widely used techniques to model the proximal humerus bone, according to its anatomy, for FEA. This study also seeks to understand the knowledge and bias behind mechanical properties assignment for bone, and the similarities/differences in mesh properties used in previous FEA studies of the proximal humerus. The best ways to achieve these processes, according to the evidence, will be analyzed and discussed, seeking to obtain the most accurate results for FEA simulations.

The effects of metastatic lesion on the structural determinants of bone: Current clinical and experimental approaches

Metastatic bone disease is incurable with an associated increase in skeletal-related events, particularly a 17-50% risk of pathologic fractures. Current surgical and oncological treatments are palliative, do not reduce overall mortality, and therefore optimal management of adults at risk of pathologic fractures presents an unmet medical need. Plain radiography lacks specificity and may result in unnecessary prophylactic fixation. Radionuclide imaging techniques primarily supply information on the metabolic activity of the tumor or the bone itself. Magnetic resonance imaging and computed tomography provide excellent anatomical and structural information but do not quantitatively assess bone matrix. Research has now shifted to developing unbiased data-driven tools that can predict risk of impending fractures and guide individualized treatment decisions. This review discusses the state-of-the-art in clinical and experimental approaches for prediction of pathologic fractures with bone metastases. Alterations in bone matrix quality are associated with an age-related increase in skeletal fragility but the impact of metastases on the intrinsic material properties of bone is unclear. Engineering-based analyses are non-invasive with the capability to evaluate oncological treatments and predict failure due to the progression of metastasis. The combination of these approaches may improve our understanding of the underlying deterioration in mechanical performance.

Image-based finite-element modeling of the human femur

Fracture is considered a critical clinical endpoint in skeletal pathologies including osteoporosis and bone metastases. However, current clinical guidelines are limited with respect to identifying cases at high risk of fracture, as they do not account for many mechanical determinants that contribute to bone fracture. Improving fracture risk assessment is an important area of research with clear clinical relevance. Patient-specific numerical musculoskeletal models generated from diagnostic images are widely used in biomechanics research and may provide the foundation for clinical tools used to quantify fracture risk. However, prior to clinical translation, in vitro validation of predictions generated from such numerical models is necessary. Despite adopting radically different models, in vitro validation of image-based finite element (FE) models of the proximal femur (predicting strains and failure loads) have shown very similar, encouraging levels of accuracy. The accuracy of such in vitro models has motivated their application to clinical studies of osteoporotic and metastatic fractures. Such models have demonstrated promising but heterogeneous results, which may be explained by the lack of a uniform strategy with respect to FE modeling of the human femur. This review aims to critically discuss the state of the art of image-based femoral FE modeling strategies, highlighting principal features and differences among current approaches. Quantitative results are also reported with respect to the level of accuracy achieved from in vitro evaluations and clinical applications and are used to motivate the adoption of a standardized approach/workflow for image-based FE modeling of the femur.

Fracture risk assessment and clinical decision making for patients with metastatic bone disease

Metastatic breast, prostate, lung, and other cancers often affect bone, causing pain, increasing fracture risk, and decreasing function. Management of metastatic bone disease (MBD) is clinically challenging when there is potential but uncertain risk of pathological fracture. Management of MBD has become a major focus within orthopedic oncology with respect to fracture and impending fracture care. If impending skeletal-related events (SREs), particularly pathologic fracture, could be predicted, increasing evidence suggests that prophylactic surgical treatment improves patient outcomes. However, current fracture risk assessment and radiographic metrics do not have high accuracy and have not been combined with relevant patient survival tools. This review first explores the prevalence, incidence, and morbidity of MBD and associated SREs for different cancer types. Strengths and limitations of current fracture risk scoring systems for spinal stability and long bone fracture are highlighted. More recent computed tomography (CT)-based structural rigidity analysis (CTRA) and finite element (FE) analysis methods offer advantages of increased specificity (true negative rate), but are limited in availability. Other fracture prediction approaches including parametric response mapping and positron emission tomography/computed tomography measures show early promise. Substantial new information to inform clinical decision-making includes measures of survival, clinical benefits, and economic analysis of prophylactic treatment compared to after-fracture stabilization. Areas of future research include use of big data and machine learning to predict SREs, greater access and refinement of CTRA/FE approaches, combination of clinical survival prediction tools with radiographically based fracture risk assessment, and net benefit analysis for fracture risk assessment and prophylactic treatment.

Finite element models for fracture prevention in patients with metastatic bone disease. A literature review

Patients with bone metastases have an increased risk to sustain a pathological fracture as lytic metastatic lesions damage and weaken the bone. In order to prevent fractures, prophylactic treatment is advised for patients with a high fracture risk. Mechanical stabilization of the femur can be provided through femoroplasty, a minimally invasive procedure where bone cement is injected into the lesion, or through internal fixation with intra- or extramedullary implants. Clinicians face the task of determining whether or not prophylactic treatment is required and which treatment would be the most optimal. Finite element (FE) models are promising tools that could support this decision process. The aim of this paper is to provide an overview of the state-of-the-art in FE modeling for the treatment decision of metastatic bone lesions in the femur. First, we will summarize the clinical and mechanical results of femoroplasty as a prophylactic treatment method. Secondly, current FE models for fracture risk assessment of metastatic femurs will be reviewed and the remaining challenges for clinical implementation will be discussed. Thirdly, we will elaborate on the simulation of femoroplasty in FE models and discuss future opportunities. Femoroplasty has already proven to effectively relieve pain and improve functionality, but there remains uncertainty whether it provides sufficient mechanical strengthening to prevent pathological fractures. FE models could help to select appropriate candidates for whom femoroplasty provides sufficient increase in strength and to further improve the mechanical benefit by optimizing the locations for cement augmentation.

When and where do patients with bone metastases actually break their femurs?

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.

Nonlinear voxel-based finite element model for strength assessment of healthy and metastatic proximal femurs

Nonlinear finite element (FE) models can accurately quantify bone strength in healthy and metastatic femurs. However, their use in clinical practice is limited since state-of-the-art implementations using tetrahedral meshes involve a lot of manual work for which specific modelling software and engineering knowledge are required. Voxel-based meshes could enable the transition since they are robust and can be highly automated. Therefore, the aim of this work was to bridge the modelling gap between the tetrahedral and voxel-based approach. Specifically, we validated a nonlinear voxel-based FE method relative to experimental data from 20 femurs with and without artificial metastases that had been mechanically loaded until failure. CT scans of the femurs were segmented and automatically converted into a voxel-based mesh with hexahedral elements. Nonlinear material properties were implemented in an open-source linear voxel-based FE solver by adding an additional loop to the routine such that the material properties could be adapted after each increment. Bone strength, quantified as the maximum force in the force-displacement curve, was evaluated. The results were compared to a previously established nonlinear tetrahedral FE approach as well as to the experimentally measured bone strength. The voxel-based FE model predicted the experimental bone strength very well both for healthy (R² = 0.90, RMSE = 0.88 kN) and metastatic femurs (R² = 0.93, RMSE = 0.64 kN). The model precision and accuracy were very similar to the ones obtained with the tetrahedral model (R² = 0.90/0.93, RMSE = 0.90/0.64 kN for intact/metastatic respectively). The more intuitive voxel-based meshes thus quantified macroscale femoral strength equally well as state-of-the-art tetrahedral models. The robustness, high level of automation and time-efficiency (< 30 min) of the implemented workflow offer great potential for developing FE models to improve fracture risk prediction in clinical practice.

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A nonlinear voxel-based FE model was evaluated to assess femoral bone strength

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Both healthy and metastatic femurs were evaluated

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The FE models predicted bone strength with high accuracy and precision

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Voxel-based and tetrahedral FE models showed similar accuracy and precision

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An iterative routine enabled material nonlinearity in a linear FE solver

Patient-specific finite element computer models improve fracture risk assessments in cancer patients with femoral bone metastases compared to clinical guidelines

PURPOSE

To determine whether patient-specific finite element (FE) computer models are better at assessing fracture risk for femoral bone metastases compared to clinical assessments based on axial cortical involvement on conventional radiographs, as described in current clinical guidelines.

METHODS

Forty-five patients with 50 femoral bone metastases, who were treated with palliative radiotherapy for pain, were included (64% single fraction (8Gy), 36% multiple fractions (5 or 6x4Gy)) and were followed for six months to determine whether they developed a pathological femoral fracture. All plain radiographs available within a two month period prior to radiotherapy were obtained. Patient-specific FE models were constructed based on the geometry and bone density obtained from the baseline quantitative CT scans used for radiotherapy planning. Femoral failure loads normalized for body weight (BW) were calculated. Patients with a failure load of 7.5 x BW or lower were identified as having high fracture risk, whereas patients with a failure load higher than 7.5 x BW were classified as low fracture risk. Experienced assessors measured axial cortical involvement on conventional radiographs. Following clinical guidelines, patients with lesions larger than 30mm 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)).

RESULTS

Seven femurs (14%) fractured during follow-up. Median time to fracture was 8 weeks. FE models were better at assessing fracture risk in comparison to 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).

CONCLUSIONS

Patient-specific FE computer models improve fracture risk assessments of femoral bone metastases in advanced cancer patients compared to clinical assessments based on axial cortical involvement, which is currently used in clinical guidelines.

Patient-Specific Bone Multiscale Modelling, Fracture Simulation and Risk Analysis-A Survey

This paper provides a starting point for researchers and practitioners from biology, medicine, physics and engineering who can benefit from an up-to-date literature survey on patient-specific bone fracture modelling, simulation and risk analysis. This survey hints at a framework for devising realistic patient-specific bone fracture simulations. This paper has 18 sections: Section 1 presents the main interested parties; Section 2 explains the organzation of the text; Section 3 motivates further work on patient-specific bone fracture simulation; Section 4 motivates this survey; Section 5 concerns the collection of bibliographical references; Section 6 motivates the physico-mathematical approach to bone fracture; Section 7 presents the modelling of bone as a continuum; Section 8 categorizes the surveyed literature into a continuum mechanics framework; Section 9 concerns the computational modelling of bone geometry; Section 10 concerns the estimation of bone mechanical properties; Section 11 concerns the selection of boundary conditions representative of bone trauma; Section 12 concerns bone fracture simulation; Section 13 presents the multiscale structure of bone; Section 14 concerns the multiscale mathematical modelling of bone; Section 15 concerns the experimental validation of bone fracture simulations; Section 16 concerns bone fracture risk assessment. Lastly, glossaries for symbols, acronyms, and physico-mathematical terms are provided.

Finite element analyses for predicting anatomical neck fractures in the proximal humerus

BACKGROUND

Proximal humerus fractures which occur as a result of a fall on an outstretched arm are frequent among the elderly population. The necessity of stabilizing such fractures by surgical procedures is a controversial matter among surgeons. Validating a personalized FE analysis by ex-vivo experiments of humeri and mimicking such fractures by experiments is the first step along the path to determine the necessity of such surgeries.

METHODS

Four fresh frozen human humeri were loaded using a new simple experimental setting, so to fracture the humeri at the anatomical neck. Strains on humeri's surfaces predicted by the high order FE analyses (as in Dahan et al., 2016) were compared to the experimental observations to further enhance the validity of the FE analyses. A simplified yield criterion based on a linear elastic analysis and principal strains was used to predict the anatomical neck fracture as observed in the experiment.

FINDINGS

An excellent correlation between experimental measured and FE predicted strains was obtained (slope of 0.99 and R²=0.98). All humeri were fractured at the anatomical neck. The predicted yield load was within 10%-20% accuracy.

INTERPRETATION

High-order FE analyses reliably predict strains and yield loads in the humeri. Fractures induced by the experimental setting correspond to anatomical neck fractures noticed in practice and classified as AO C1.1-C1.3. Surgical neck fractures, which are most common in clinical practice, could not be realized in the proposed experiments, and a different experimental setting should be sought to obtain them ex-vivo.

A novel phase field method for modeling the fracture of long bones

A proximal humerus fracture is an injury to the shoulder joint that necessitates medical attention. While it is one of the most common fracture injuries impacting the elder community and those who suffer from traumatic falls or forceful collisions, there are almost no validated computational methods that can accurately model these fractures. This could be due to the complex, inhomogeneous bone microstructure, complex geometries, and the limitations of current fracture mechanics methods. In this paper, we develop a novel phase field method to investigate the proximal humerus fracture. To model the fracture in the inhomogeneous domain, we propose a power-law relationship between bone mineral density and critical energy release rate. The method is validated by an in vitro experiment, in which a human humerus is constrained on both ends while subjected to compressive loads on its head, in the longitudinal direction, that lead to fracture at the anatomical neck. CT scans are employed to acquire the bone geometry and material parameters, from which detailed finite element meshes with inhomogeneous Young modulus distributions are generated. The numerical method, implemented in a high performance computing environment, is used to quantitatively predict the complex 3D brittle fracture of the bone and is shown to be in good agreement with experimental observations. Furthermore, our findings show that the damage is initiated in the trabecular bone-head and propagates outward towards the bone cortex. We conclude that the proposed phase field method is a promising approach to model bone fracture.

QCT-based finite element prediction of pathologic fractures in proximal femora with metastatic lesions

Predicting pathologic fractures in femora with metastatic lesions remains a clinical challenge. Currently used guidelines are inaccurate, especially to predict non-impeding fractures. This study evaluated the ability of a nonlinear quantitative computed tomography (QCT)-based homogenized voxel finite element (hvFE) model to predict patient-specific pathologic fractures. The hvFE model was generated highly automated from QCT images of human femora. The femora were previously loaded in a one-legged stance setup in order to assess stiffness, failure load, and fracture location. One femur of each pair was tested in its intact state, while the contralateral femur included a simulated lesion on either the superolateral- or the inferomedial femoral neck. The hvFE model predictions of the stiffness (0.47 < R² < 0.94), failure load (0.77 < R² < 0.98), and exact fracture location (68%) were in good agreement with the experimental data. However, the model underestimated the failure load by a factor of two. The hvFE models predicted significant differences in stiffness and failure load for femora with superolateral- and inferomedial lesions. In contrast, standard clinical guidelines predicted identical fracture risk for both lesion sites. This study showed that the subject-specific QCT-based hvFE model could predict the effect of metastatic lesions on the biomechanical behaviour of the proximal femur with moderate computational time and high level of automation and could support treatment strategy in patients with metastatic bone disease.

A round-robin finite element analysis of human femur mechanics between seven participating laboratories with experimental validation

Finite element analysis is a common tool that has been used for the past few decades to predict the mechanical behavior of bone. However, to our knowledge, there are no round-robin finite element analyses of long human bones with more than two participating biomechanics laboratories published yet, where the results of the experimental tests were not known in advance. We prepared a fresh-frozen human femur for a compression test in a universal testing machine measuring the strains at 10 bone locations as well as the deformation of the bone in terms of the displacement of the loading point at a load of 2 kN. The computed tomography data of the bone with a calibration phantom as well as the orientation of the bone in the testing machine with the according boundary conditions were delivered to seven participating laboratories. These were asked to perform a finite element analysis simulating the experimental setup and deliver their results to the coordinator without knowing the experimental results. Resultantly, four laboratories had deviations from the experimentally measured strains of less than 40%, and three laboratories had deviations of their numerically determined values compared to the experimental data of more than 120%. These deviations are thought to be based on different material laws and material data, as well as different material mapping methods. Investigations will be conducted to clarify and assess the reasons for the large deviations in the numerical data. It was shown that the precision of finite element models of the human femur is not yet as developed as desired by the biomechanics community.

Neuro-musculoskeletal flexible multibody simulation yields a framework for efficient bone failure risk assessment

Fragility fractures are a major socioeconomic problem. A non-invasive, computationally-efficient method for the identification of fracture risk scenarios under the representation of neuro-musculoskeletal dynamics does not exist. We introduce a computational workflow that integrates modally-reduced, quantitative CT-based finite-element models into neuro-musculoskeletal flexible multibody simulation (NfMBS) for early bone fracture risk assessment. Our workflow quantifies the bone strength via the osteogenic stresses and strains that arise due to the physiological-like loading of the bone under the representation of patient-specific neuro-musculoskeletal dynamics. This allows for non-invasive, computationally-efficient dynamic analysis over the enormous parameter space of fracture risk scenarios, while requiring only sparse clinical data. Experimental validation on a fresh human femur specimen together with femur strength computations that were consistent with literature findings provide confidence in the workflow: The simulation of an entire squat took only 38 s CPU-time. Owing to the loss (16% cortical, 33% trabecular) of bone mineral density (BMD), the strain measure that is associated with bone fracture increased by 31.4%; and yielded an elevated risk of a femoral hip fracture. Our novel workflow could offer clinicians with decision-making guidance by enabling the first combined in-silico analysis tool using NfMBS and BMD measurements for optimized bone fracture risk assessment.

Influence of bone lesion location on femoral bone strength assessed by MRI-based finite-element modeling

Currently, clinical determination of pathologic fracture risk in the hip is conducted using measures of defect size and shape in the stance loading condition. However, these measures often do not consider how changing lesion locations or how various loading conditions impact bone strength. The goal of this study was to determine the impact of defect location on bone strength parameters in both the sideways fall and stance-loading conditions. We recruited 20 female subjects aged 48-77 years for this study and performed MRI of the proximal femur. Using these images, we simulated 10-mm pathologic defects in greater trochanter, superior, middle, and inferior femoral head, superior, middle, and inferior femoral neck, and lateral, middle, and medial proximal diaphysis to determine the effect of defect location on change in bone strength by performing finite element analysis. We compared the effect of each osteolytic lesion on bone stiffness, strength, resilience, and toughness. For the sideways fall loading, defects in the inferior femoral head (12.21%) and in the greater trochanter (6.43%) resulted in the greatest overall reduction in bone strength. For the stance loading, defects in the mid femoral head (-7.91%) and superior femoral head (-7.82%) resulted in the greatest overall reduction in bone strength. Changes in stiffness, yield force, ultimate force, resilience, and toughness were not found to be significantly correlated between the sideways fall and stance-loading for the majority of defect locations, suggesting that calculations based on the stance-loading condition are not predictive of the change in bone strength experienced in the sideways fall condition. While stiffness was significantly related to yield force (R² > 0.82), overall force (R² > 0.59), and resilience (R² > 0.55), in both, the stance-loading and sideways fall conditions for most defect locations, stiffness was not significantly related to toughness. Therefore, structure-dependent measure such as stiffness may not fully explain the post-yield measures, which depend on material failure properties. The data showed that MRI-based models have the sensitivity to determine the effect of pathologic lesions on bone strength.

Mechanical behavior of metastatic femurs through patient-specific computational models accounting for bone-metastasis interaction

This paper proposes a computational model based on a finite-element formulation for describing the mechanical behavior of femurs affected by metastatic lesions. A novel geometric/constitutive description is introduced by modelling healthy bone and metastases via a linearly poroelastic constitutive approach. A Gaussian-shaped graded transition of material properties between healthy and metastatic tissues is prescribed, in order to account for the bone-metastasis interaction. Loading-induced failure processes are simulated by implementing a progressive damage procedure, formulated via a quasi-static displacement-driven incremental approach, and considering both a stress- and a strain-based failure criterion. By addressing a real clinical case, left and right patient-specific femur models are geometrically reconstructed via an ad-hoc imaging procedure and embedding multiple distributions of metastatic lesions along femurs. Significant differences in fracture loads, fracture mechanisms, and damage patterns, are highlighted by comparing the proposed constitutive description with a purely elastic formulation, where the metastasis is treated as a pseudo-healthy tissue or as a void region. Proposed constitutive description allows to capture stress/strain localization mechanisms within the metastatic tissue, revealing the model capability in describing possible strain-induced mechano-biological stimuli driving onset and evolution of the lesion. The proposed approach opens towards the definition of effective computational strategies for supporting clinical decision and treatments regarding metastatic femurs, contributing also to overcome some limitations of actual standards and procedures.

Optimization of Taylor spatial frame half-pins diameter for bone deformity correction: Application to femur

Using external fixtures for bone deformity correction takes advantages of less soft tissue injury, better bone alignment and enhances strain development for bone formation on cutting section, which cause shorter healing time. Among these fixtures, Taylor spatial frame is widely used and includes two rings and six adjustable struts developing 6 degrees of freedom, making them very flexible for this type of application. The current study describes a method to optimize Taylor spatial frame pin-sizes currently chosen from the surgeon's experiences. A three-dimensional model of femur was created from computed tomography images; segmentation of the medical images was made based on the Hounsfield unit (gray scale) in order to allocate adequate mechanical properties into cortical and trabecular bone sections. Both the cortical and trabecular sections were assumed to be isotropic and homogeneous. The diameter optimization of Taylor spatial frame's half-pins was carried out by coupling genetic algorithm and finite element analysis. The finite element analysis was based on a static mechanical load corresponding to a standing person's body weight. Finite element analysis results were validated with experimentally measured strains obtained from bone compression tests. A cost function, based on the developed bone stresses, was defined close to the Taylor spatial frame's half-pins. The calculated cost function showed a decrease of over 33% from the initial half-pin selection by the surgeon and the genetic algorithm optimization. Consequently, the maximum stresses experienced by the bone in the connected location of the half-pins decreased from 121.4 MPa in the surgeon's selection to 73.07 MPa as a result of the optimization process.

Patient-specific finite element analysis of femurs with cemented hip implants

BACKGROUND

Over 1.6 million hip replacements are performed annually in Organisation for Economic Cooperation and Development countries, half of which involve cemented implants. Quantitative computer tomography based finite element methods may be used to assess the change in strain field in a femur following such a hip replacement, and thus determine a patient-specific optimal implant. A combined experimental-computational study on fresh frozen human femurs with different cemented implants is documented, aimed at verifying and validating the methods.

METHODS

Ex-vivo experiments on four fresh-frozen human femurs were conducted. Femurs were scanned, fractured in a stance position loading, and thereafter implanted with four different prostheses. All femurs were reloaded in stance positions at three different inclination angles while recording strains on bones' and prosthesis' surfaces. High-order FE models of the intact and implanted femurs were generated based on the computer tomography scans and X-ray radiographs. The models were virtually loaded mimicking the experimental conditions and FE results were compared to experimental observations.

FINDINGS

Strains predicted by finite element analyses in all four femurs were in excellent correlation with experimental observations FE = 1.01 × EXP - 0.07,R² = 0.976, independent of implant's type, loading angle and fracture location.

INTERPRETATION

Computer tomography based finite element models can reliably determine strains on femur surface and on inserted implants at the contact with the cement. This allows to investigate suitable norms to rank implants for a patient-specific femur so to minimize changes in strain patterns in the operated femur.

Pathological fracture risk assessment in patients with femoral metastases using CT-based finite element methods. A retrospective clinical study

Physician recommendation for prophylactic surgical fixation of a femur with metastatic bone disease (MBD) is usually based on Mirels' criteria and clinical experience, both of which suffer from poor specificity. This may result in a significant number of these health compromised patients undergoing unnecessary surgery. CT-based finite element analyses (CTFEA) have been shown to accurately predict strength in femurs with metastatic tumors in an ex-vivo study. In order to assess the utility of CTFEA as a clinical tool to determine the need for fixation of patients with MBD of the femur, an ad hoc CTFEA was performed on a retrospective cohort of fifty patients. Patients with CT scans appropriate for CTFEA analysis were analyzed. Group 1 was composed of 5 MBD patients who presented with a pathologic femoral fracture and had a scan of their femurs just prior to fracture. Group 2 was composed of 45 MBD patients who were scheduled for a prophylactic surgery because of an impending femoral fracture. CTFEA models were constructed for both femurs for all patients, loaded with a hip contact force representing stance position loading accounting for the patient's weight and femur anatomy. CTFEA analysis of Group 1 patients revealed that they all had higher tumor associated strains compared to typical non-diseased femur bone strains at the same region (>45%). Based on analysis of the 5 patients in Group 1, the ratio between the absolute maximum principal strain in the vicinity of the tumor and the typical median strain in the region of the tumor of healthy bones (typical strain fold ratio) was found to be the 1.48. This was considered to be the predictive threshold for a pathological femoral fracture. Based on this typical strain fold ratio, twenty patients (44.4%) in Group 2 were at low risk of fracture and twenty-five patients (55.5%) high risk of fracture. Eleven patients in Group 2 choose not to have surgery and none fractured in the 5month follow-up period. CTFEA predicted that seven of these patients were below the pathological fracture threshold and four above, for a specificity of 63% Based on CTFEA, 39% of the patients with femoral MBD who were referred and underwent prophylactic stabilization may not have needed surgery. These results indicate that a prospective randomized clinical trial evaluating CTFEA as a criterion for determining the need for surgical stabilization in patients with MBD of the femur may be warranted.

Effect of simulated metastatic lesions on the biomechanical behavior of the proximal femur

Pathologic fractures of femora in patients with metastatic cancer are associated with high morbidity and mortality. Prediction of impending fractures is based on unspecific clinical criteria or past clinician's experience, which leads to underestimation or overtreatment. The aim of this study was to investigate the effect of the site of metastatic lesions on biomechanical behavior of the proximal femur. Sixteen pairs of human femora were scanned with quantitative computed tomography (QCT) to asses bone mineral density. One femur of each pair remained intact while a defined lesion was reamed out in either the superolateral or inferomedial portion of the femoral neck of the contralateral femur. All femora were loaded in a mechanical test setup mimicking one-legged stance and stiffness, failure load, and fracture location were determined. In the biomechanical experiments the superolateral lesion and the inferomedial lesion caused a stiffness reduction of 19% and 66%, respectively. The average failure load was 40% and 75% lower for specimens with the superolateral (4.53 ± 1.56 kN) and inferomedial (1.89 ± 1.73 kN) lesions, respectively, compared to intact specimens (7.66 ± 3.34 kN). Lesions in the femoral neck led to reduction in both stiffness and failure load of the proximal femur. Furthermore, the site of the lesion had a large effect on the magnitude of the reduction in biomechanical properties. The presented data emphasize the importance of differentiating between locations of the lesion in pathologic fracture prediction of the metastatic femur and underline the insufficient accuracy of current predictive guidelines. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:2407-2414, 2017.

The insufficiencies of risk analysis of impending pathological fractures in patients with femoral metastases: A literature review

Purpose

Pathologic fractures in patients with bone metastases are a common problem in clinical orthopaedic routine. On one hand recognition of metastatic lesions, which are at a high risk of fracture, is essential for timely prophylactic fixation, while on the other hand patients with a low risk of pathologic fractures should be spared from overtreatment.

The purpose of this review is to identify all methods for fracture risk evaluation in patients with femoral metastases in the literature and to evaluate their predictive values in clinical applications.

Methods

A MEDLINE database literature research was conducted in order to identify clinical scoring systems, conclusions from prospective and retrospective radiologic and/or clinical studies, as well as data from biomechanical experiments, numerical computational methods, and computer simulations.

Results

The search identified 441 articles of which 18 articles met the inclusion criteria; 4 more articles were identified from citations of the primarily found studies. In principle there are two distinct methodologies, namely fracture risk prediction factors based on clinical and radiological data such as the most deployed the Mirels' score and fracture risk prediction based on engineering methods. Fracture risk prediction using Mirels' score, based on pure clinical data, shows a negative predictive value between 86 and 100%, but moderate to poor results in predicting non-impending fractures with a positive predictive value between 23 and 70%. Engineering methods provide a high accuracy (correlation coefficient between ex vivo and results from numerical calculations: 0.68 < r² < 0.96) in biomechanical lab experiments, but have not been applied to clinical routine yet.

Conclusion

This review clearly points out a lack of adequate clinical methods for fracture risk prediction in patients with femoral metastases. Today's golden standard, the Mirels' score leads to an overtreatment. Whereas, engineering methods showed high potential but require a clinical validation. In future definition of patient-specific, quantitative risk factor based modelling methods could serve as useful decision support for individualized treatment strategies in patients with a metastatic lesion.

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There is a lack of adequate clinical methods for fracture risk prediction in patients with femoral metastases.

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Mirels' score fails to recognise non-impending fractures and leads to an overtreatment.

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Engineering methods showed high potential but require a clinical validation.

Validating hospital antibiotic purchasing data as a metric of inpatient antibiotic use

OBJECTIVES

Antibiotic purchasing data are a widely used, but unsubstantiated, measure of antibiotic consumption. To validate this source, we compared purchasing data from hospitals and external medical databases with patient-level dispensing data.

METHODS

Antibiotic purchasing and dispensing data from internal hospital records and purchasing data from IMS Health were obtained for two hospitals between May 2013 and April 2015. Internal purchasing data were validated against dispensing data, and IMS data were compared with both internal metrics. Scatterplots of individual antimicrobial data points were generated; Pearson's correlation and linear regression coefficients were computed. A secondary analysis re-examined these correlations over shorter calendar periods.

RESULTS

Internal purchasing data were strongly correlated with dispensing data, with correlation coefficients of 0.90 (95% CI = 0.83-0.95) and 0.98 (95% CI = 0.95-0.99) at hospitals A and B, respectively. Although dispensing data were consistently lower than purchasing data, this was attributed to a single antibiotic at both hospitals. IMS data were favourably correlated with, but underestimated, internal purchasing and dispensing data. This difference was accounted for by eight antibiotics for which direct sales from some manufacturers were not included in the IMS database. The correlation between purchasing and dispensing data was consistent across periods as short as 3 months, but not at monthly intervals.

CONCLUSIONS

Both internal and external antibiotic purchasing data are strongly correlated with dispensing data. If outliers are accounted for appropriately, internal purchasing data could be used for cost-effective evaluation of antimicrobial stewardship programmes, and external data sets could be used for surveillance and research across geographical regions.