Perspectives on the non-invasive evaluation of femoral strength in the assessment of hip fracture risk

Structural differences contributing to sex-specific associations between FN BMD and whole-bone strength for adult White women and men

Hip areal BMD (aBMD) is widely used to identify individuals with increased fracture risk. Low aBMD indicates low strength, but this association differs by sex with men showing greater strength for a given aBMD than women. To better understand the structural basis giving rise to this sex-specific discrepancy, cadaveric proximal femurs from White female and male donors were imaged using nano-CT and loaded in a sideways fall configuration to assess strength. FN pseudoDXA images were generated to identify associations among structure, aBMD, and strength that differ by sex. Strength correlated significantly with pseudoDXA aBMD for females (R2 = 0.468, P < .001) and males (R2 = 0.393, P < .001), but the elevations (y-intercepts) of the linear regressions differed between sexes (P < .001). Male proximal femurs were 1045 N stronger than females for a given pseudoDXA aBMD. However, strength correlated with pseudoDXA BMC for females (R2 = 0.433, P < .001) and males (R2 = 0.443, P < .001) but without significant slope (P = .431) or elevation (P = .058) differences. Dividing pseudoDXA BMC by FN-width, total cross-sectional area, or FN-volume led to significantly different associations between strength and the size-adjusted BMC measures for women and men. Three structural differences were identified that differentially affected aBMD and strength for women and men: First, men had more bone mass per unit volume than women; second, different cross-sectional shapes resulted in larger proportions of bone mass orthogonal to the DXA image for men than women; and third, men and women had different proportions of cortical and trabecular bone relative to BMC. Thus, the proximal femurs of women were not smaller versions of men but were constructed in fundamentally different manners. Dividing BMC by a bone size measure was responsible for the sex-specific associations between hip aBMD and strength. Thus, a new approach for adjusting measures of bone mass for bone size and stature is warranted.

Proximal femur geometry: a major predictor of proximal femur fracture subtypes

INTRODUCTION

Proximal femur geometry (PFG) represents an important risk factor for the occurrence of hip fractures. There are currently few studies regarding the correlation between PFG and the occurrence of a specific fracture subtype, and those that exist have small cohorts and present with different methodologies and contradictory results. Therefore, there is no consensus in the literature regarding this topic. The present study aimed to establish the contribution of the PFG in the occurrence of different subtypes of proximal femur fractures (PFF): intertrochanteric, neck and subtrochanteric.

METHODS

Analysis of 1022 plain anteroposterior pelvic radiographs of patients consecutively admitted to the emergency room of a Level 1 Trauma Centre between 2013 and 2019 after low energy trauma who presented with PFF and underwent surgical treatment. Patients were analysed considering age, gender and subtype of PFF (intertrochanteric, neck or subtrochanteric). Radiological parameters including cervicodiaphyseal angle (CDA), horizontal offset (HO), femoral neck width (FNW), femoral neck length (FNL), great trochanter-pubic symphysis distance (GTPSD), acetabular teardrop distance (ATD) and width of the intertrochanteric region (WIR) were measured and compared between the different subtypes of fractures (7154 measurements). Statistical analysis was conducted recurring to absolute measurements and measurements ratios. The correlation was assessed using t-test.

RESULTS

There were statistically significant differences in proximal femur geometry between the different subtypes of fractures. Patients presenting with femoral neck fractures had greater CDA (132.5 ± 6.9 vs. 130.0 ± 7.3; p < 0.001) and lower HO (45.8 ± 7.4 vs. 49.0 ± 8.0; p < 0.001), HO/ATD (0.34 ± 0.068 vs. 0.37 ± 0.072; p < 0.001) and HO/GTPSD (0.26 ± 0.049 vs. 0.28 ± 0.039; p < 0.001) than patients with intertrochanteric/subtrochanteric fractures.

CONCLUSIONS

PFG represents an important contributor to the occurrence of different fracture subtypes. Femoral neck fractures are associated with greater CDA and lower HO, HO/ATD and HO/GTPSD when compared to intertrochanteric or subtrochanteric fractures.

Relationship between mechanical and microstructural parameters of rat lumbar spine in different ages

Exploring the relationships between microstructure and mechanical properties of bones may provide effective suggestions for increasing bone strength and reducing osteoporotic fracture. In this research, the tissue-level mechanical parameters, microstructure parameters of cancellous bone, and apparent mechanical parameters of L6 vertebral body were calculated in female SD rats aged 1-, 3-, 5-, 7-, 9-, 11-, 13-, 15-, 16-, and 17-month-old. Data were processed with Kruskal-Wallis test, linear regression and Spearman's rank correlation analysis. Appropriately increasing the plate Tb.N could enhance mechanical properties of bone. Tb.Th and Tb.N were two key factors in determining the tissue-level mechanical properties of cancellous bone. The microstructure could significantly predict mechanical parameters. Our findings may help to further understand the mechanism of osteoporotic fractures.

Short-Term Precision and Repeatability of Radiofrequency Echographic Multi Spectrometry (REMS) on Lumbar Spine and Proximal Femur: An In Vivo Study

To determine the short-term intra-operator precision and inter-operator repeatability of radiofrequency echographic multi-spectrometry (REMS) at the lumbar spine (LS) and proximal femur (FEM). All patients underwent an ultrasound scan of the LS and FEM. Both precision and repeatability, expressed as root-mean-square coefficient of variation (RMS-CV) and least significant change (LSC) were obtained using data from two consecutive REMS acquisitions by the same operator or two different operators, respectively. The precision was also assessed in the cohort stratified according to BMI classification. The mean (±SD) age of our subjects was 48.9 ± 6.8 for LS and 48.3 ± 6.1 for FEM. Precision was assessed on 42 subjects at LS and 37 subjects on FEM. Mean (±SD) BMI was 24.71 ± 4.2 for LS and 25.0 ± 4.84 for FEM. Respectively, the intra-operator precision error (RMS-CV) and LSC resulted in 0.47% and 1.29% at the spine and 0.32% and 0.89% at the proximal femur evaluation. The inter-operator variability investigated at the LS yielded an RMS-CV error of 0.55% and LSC of 1.52%, whereas for the FEM, the RMS-CV was 0.51% and the LSC was 1.40%. Similar values were found when subjects were divided into BMI subgroups. REMS technique provides a precise estimation of the US-BMD independent of subjects' BMI differences.

The predictive ability of a QCT-FE model of the proximal femoral stiffness under multiple load cases is strongly influenced by experimental uncertainties

Despite significant improvements in terms of the predictive ability of Quantitative Computed Tomography based Finite Element (QCT-FE) models in estimating femoral strength (fracture load and stiffness), no substantial clinical adoption of this method has taken place to date. Narrowing the wide variability of FE results by standardizing the methodology and validation protocols, as well as reducing the uncertainties in the FEA process have been proposed as routes towards improved reliability. The aim of this study was to: First, validate a QCT-FE model of proximal femoral stiffness in multiple stance load cases, and second, using a parametric approach, determine the influence of select experimental and modeling parameters on the predictive ability of our model. Ten fresh frozen human femoral samples were tested in neutral stance, 15° adducted and 15° abducted load cases. Voxel-based linear-elastic QCT-FE models of the samples were generated to predict the models' stiffness values in all load cases. The base FE models were validated against the experimental results using linear regression. Thirty six deviated models were created using the minimum and maximum values of experiment-based "plausible range" for 18 parameters in 4 categories of embedding, loading, material, and segmentation. The predictive ability of the models were compared in terms of the coefficient of determination (R²) of the linear regression between the measured and predicted stiffness values in all load cases. Our model was capable of capturing 90% of the variation in the experimental stiffness of the samples in neutral stance position (R² = 0.9, concordance correlation coefficient (CCC) = 0.93, percent root mean squared error (RMSE%) = 8.4%, slope and intercept not significantly different from unity and zero, respectively). Embedding and loading categories strongly affected the predictive ability of the models with an average percent difference in R² of 4.36% ± 2.77 and 2.96% ± 1.69 for the stance-neutral load case, respectively. The performance of the models were significantly different in adducted and abducted load cases with their R² dropping to 71% and 70%, respectively. Similarly, off-axes load cases were affected by the parameters differently compared to the neutral load case, with the loading parameter category imposing more than 10% difference on their R², larger than all other categories. We also showed that automatically selecting the best performing plausible value for each parameter and each sample would result in a perfectly linear correlation (R²> 0.99) between the "tuned" model's predicted stiffness and experimental results. Based on our results, high sensitivity of the model performance to experimental parameters requires extra diligence in modeling the embedding geometry and the loading angles since these sources of uncertainty could dwarf the effects of material modeling and image processing parameters. The results of this study could help in improving the robustness of the QCT-FE models of proximal femur by limiting the uncertainties in the experimental and modeling steps.

Prediction of femoral strength of elderly men based on quantitative computed tomography images using machine learning

Hip fracture is the most common complication of osteoporosis, and its major contributor is compromised femoral strength. This study aimed to develop practical machine learning models based on clinical quantitative computed tomography (QCT) images for predicting proximal femoral strength. Eighty subjects with entire QCT data of the right hip region were randomly selected from the full MrOS cohorts, and their proximal femoral strengths were calculated by QCT-based finite element analysis (QCT/FEA). A total of 50 parameters of each femur were extracted from QCT images as the candidate predictors of femoral strength, including grayscale distribution, regional cortical bone mapping (CBM) measurements, and geometric parameters. These parameters were simplified by using feature selection and dimensionality reduction. Support vector regression (SVR) was used as the machine learning algorithm to develop the prediction models, and the performance of each SVR model was quantified by the mean squared error (MSE), the coefficient of determination (R² ), the mean bias, and the SD of bias. For feature selection, the best prediction performance of SVR models was achieved by integrating the grayscale value of 30% percentile and specific regional CBM measurements (MSE ≤ 0.016, R² ≥ 0.93); and for dimensionality reduction, the best prediction performance of SVR models was achieved by extracting principal components with eigenvalues greater than 1.0 (MSE ≤ 0.014, R² ≥ 0.93). The femoral strengths predicted from the well-trained SVR models were in good agreement with those derived from QCT/FEA. This study provided effective machine learning models for femoral strength prediction, and they may have great potential in clinical bone health assessments.

2D-3D reconstruction of the proximal femur from DXA scans: Evaluation of the 3D-Shaper software

Introduction: Osteoporosis is currently diagnosed based on areal bone mineral density (aBMD) computed from 2D DXA scans. However, aBMD is a limited surrogate for femoral strength since it does not account for 3D bone geometry and density distribution. QCT scans combined with finite element (FE) analysis can deliver improved femoral strength predictions. However, non-negligible radiation dose and high costs prevent a systematic usage of this technique for screening purposes. As an alternative, the 3D-Shaper software (3D-Shaper Medical, Spain) reconstructs the 3D shape and density distribution of the femur from 2D DXA scans. This approach could deliver a more accurate estimation of femoral strength than aBMD by using FE analysis on the reconstructed 3D DXA. Methods: Here we present the first independent evaluation of the software, using a dataset of 77 ex vivo femora. We extend a prior evaluation by including the density distribution differences, the spatial correlation of density values and an FE analysis. Yet, cortical thickness is left out of this evaluation, since the cortex is not resolved in our FE models. Results: We found an average surface distance of 1.16 mm between 3D DXA and QCT images, which shows a good reconstruction of the bone geometry. Although BMD values obtained from 3D DXA and QCT correlated well (r ² = 0.92), the 3D DXA BMD were systematically lower. The average BMD difference amounted to 64 mg/cm³, more than one-third of the 3D DXA BMD. Furthermore, the low correlation (r ² = 0.48) between density values of both images indicates a limited reconstruction of the 3D density distribution. FE results were in good agreement between QCT and 3D DXA images, with a high coefficient of determination (r ² = 0.88). However, this correlation was not statistically different from a direct prediction by aBMD. Moreover, we found differences in the fracture patterns between the two image types. QCT-based FE analysis resulted mostly in femoral neck fractures and 3D DXA-based FE in subcapital or pertrochanteric fractures. Discussion: In conclusion, 3D-Shaper generates an altered BMD distribution compared to QCT but, after careful density calibration, shows an interesting potential for deriving a standardized femoral strength from a DXA scan.

Improving the Hip Fracture Risk Prediction with a Statistical Shape-and-Intensity Model of the Proximal Femur

Severe predictions have been made regarding osteoporotic fracture incidence for the next years, with major economic and social impacts in a worldwide greying society. However, the performance of the currently adopted gold standard for fracture risk prediction, the areal Bone Mineral Density (aBMD), remains moderate. To overcome current limitations, the construction of statistical models of the proximal femur, based on three-dimensional shape and intensity (a hallmark of bone density), is here proposed for predicting hip fracture in a Caucasian postmenopausal cohort. Partial Least Square (PLS)-based statistical models of the shape, intensity and their combination were developed, and the corresponding modes and components were identified. Logistic regression models using the first two shape, intensity and shape-intensity PLS components were implemented and tested within a 10-fold cross-validation procedure as predictors of hip fracture. It emerged that (1) intensity components were superior to shape components in stratifying patients according to their fracture status, and that (2) a combination of intensity and shape improved patients risk stratification. The area under the ROC curve was 0.64, 0.85 and 0.92 for the models based on shape, intensity and shape-intensity combination respectively, against a 0.72 value for the aBMD standard approach. Based on these findings, the presented methodology turns out to be promising in tackling the need for an enhanced fracture risk assessment.

Limit analysis of human proximal femur

A limit analysis numerical approach oriented to predict the peak/collapse load of human proximal femur, under two different loading conditions, is presented. A yield criterion of Tsai-Hu-type, expressed in principal stress space, is used to model the orthotropic bone tissues. A simplified human femur 3D model is envisaged to carry on numerical simulation of in-vitro tests borrowed from the relevant literature and to reproduce their findings. A critical discussion, together with possible future developments, is presented.

Bringing Mechanical Context to Image-Based Measurements of Bone Integrity

PURPOSE OF REVIEW

Image-based measurements of bone integrity are used to estimate failure properties and clinical fracture risk. This paper (1) reviews recent imaging studies that have enhanced our understanding of the mechanical pathways to bone fracture and (2) discusses the influence that inter-individual differences in image-based measurements may have on the clinical assessment of fracture risk RECENT FINDINGS: Increased tissue mineralization is associated with improved bone strength but reduced fracture toughness. Trabecular architecture that is important for fatigue resistance is less important for bone strength. The influence of porosity on bone failure properties is heavily dependent on pore location and size. The interaction of various characteristics, such as bone area and mineral content, can further complicate their influence on bone failure properties. What is beneficial for bone strength is not always beneficial for bone toughness or fatigue resistance. Additionally, given the large amount of imaging data that is clinically available, there is a need to develop effective translational strategies to better interpret non-invasive measurements of bone integrity.

Operator-Related Errors and Pitfalls in Dual Energy X-Ray Absorptiometry: How to Recognize and Avoid Them

Dual-energy X-ray absorptiometry (DXA) is the most common modality for quantitative measurements of bone mineral density. Nevertheless, errors related to this exam are still very common, and may significantly impact on the final diagnosis and therapy. Operator-related errors may occur during each DXA step and can be related to wrong patient positioning, error in the acquisition process or in the scan analysis. The aim of this review is to provide a practical guide on how to recognize such errors in spine and hip DXA scan and how to avoid them, also presenting some of the most common artifacts encountered in clinical practice.

Fracture Risk Assessment: An Update

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Our ability to accurately identify high fracture risk in individuals has improved as the volume of clinical data has expanded and fracture risk assessment tools have been developed.

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Given its accessibility, affordability, and low radiation exposure, dual x-ray absorptiometry (DXA) remains the standard for osteoporosis screening and monitoring response to treatment.

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The trabecular bone score (TBS) is a DXA software add-on that uses lumbar spine DXA imaging to produce an output that correlates with bone microarchitecture. It has been identified as an independent fracture risk factor and may prove useful in further stratifying fracture risk among those with a bone mineral density (BMD) in the osteopenic range (-1.0 to -2.4 standard deviations), in those with low-energy fractures but normal or only mildly low BMD, or in those with conditions known to impair bone microarchitecture.

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Fracture risk assessment tools, including the Fracture Risk Assessment Tool (FRAX), Garvan fracture risk calculator, and QFracture, evaluate the impact of multiple clinical factors on fracture risk, even in the absence of BMD data. Each produces an absolute fracture risk output over a defined interval of time. When used appropriately, these enhance our ability to identify high-risk patients and allow us to differentiate fracture risk among patients who present with similar BMDs.

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For challenging clinical cases, a combined approach is likely to improve accuracy in the identification of high-risk patients who would benefit from the available osteoporosis therapies.

On challenges in clinical assessment of hip fracture risk using image-based biomechanical modelling: a critical review

INTRODUCTION

Hip fracture is a common health risk among elderly people, due to the prevalence of osteoporosis and accidental fall in the population. Accurate assessment of fracture risk is a crucial step for clinicians to consider patient-by-patient optimal treatments for effective prevention of fractures. Image-based biomechanical modeling has shown promising progress in assessment of fracture risk, and there is still a great possibility for improvement. The purpose of this paper is to identify key issues that need be addressed to improve image-based biomechanical modeling.

MATERIALS AND METHODS

We critically examined issues in consideration and determination of the four biomechanical variables, i.e., risk of fall, fall-induced impact force, bone geometry and bone material quality, which are essential for prediction of hip fracture risk. We closely inspected: limitations introduced by assumptions that are adopted in existing models; deficiencies in methods for construction of biomechanical models, especially for determination of bone material properties from bone images; problems caused by separate use of the variables in clinical study of hip fracture risk; availability of clinical information that are required for validation of biomechanical models.

RESULTS AND CONCLUSIONS

A number of critical issues and gaps were identified. Strategies for effectively addressing the issues were discussed.

Short-Term Precision Error of Bone Strain Index, a New DXA-Based Finite Element Analysis Software for Assessing Hip Strength

Bone Strain Index (BSI) is a new finite element analysis tool applied to hip dual energy X-ray absorptiometry scans. The aim of this study was to assess the short-term precision error of BSI on the proximal femur, both on a phantom and patients. The International Society for Clinical Densitometry guidelines were followed for short-term precision error assessment. Dual energy X-ray absorptiometry measurements were performed on an anthropomorphic femur phantom that was scanned twice for 30 times, for a total of 60 scans. For the in vivo part, 30 subjects were scanned twice. BSI precision error was compared to that of bone mineral density (BMD). Both for the phantom and the in vivo study BSI reproducibility was lower compared to that of BMD, as the precision error of BSI resulted 3 times higher compared to that BMD. For phantom measurements, the highest precision value was that of total femur (TF) BMD (coefficient of variation [CoV] = 0.63%, reproducibility = 98.24%), while the lowest precision was the femoral neck (FN) BSI (CoV = 3.08%, reproducibility = 91.48%). Similarly, for the in vivo study, the highest precision was found at TF BMD (CoV = 1.36%, reproducibility = 96.22%), while the lowest value of precision was found for FN BSI (CoV = 4.17%, reproducibility = 88.46%). Reproducibility at TF was always better compared to that of the FN. BSI precision error was about 3 times higher compared to BMD, confirming previous results of lumbar spine BSI. The main source of variability of this new software is related to patient positioning.

The risk factors of postoperative delirium in patients with hip fracture: implication for clinical management

Background

Delirium is a common complication of hip surgery patients. It is necessary to investigate the epidemiological characteristics and related risk factors of delirium after hip fracture surgery, to provide evidence supports for the prevention and management of delirium.

Methods

Hip fracture patients admitted to our hospital for surgical treatment from March 2018 to March 2020 were identified as participants. The characteristics and laboratory examinations in patients with and without postoperative delirium were compared and analyzed. Logistic regression analyses were conducted to ascertain the independent risk factors, and the area under the curve (AUC) were calculated to analyze the predictive value.

Results

A total of 568 postoperative patients with hip fracture were included, the incidence of delirium in postoperative patients with hip fracture was 14.44 %. The preoperative albumin (OR 4.382, 2.501 ~ 5.538), history of delirium (OR 2.197, 1.094 ~ 3.253), TSH (OR1.245, 1.077 ~ 1.638), the resting score on the first postoperative day (OR1.235, 0.944 ~ 1.506) and age(OR1.185, 0.065 ~ 1.814) were the independent risk factors for the postoperative delirium in patients with hip fracture(all p < 0.05). The AUC of albumin, history of delirium, TSH, the resting score on the first postoperative day and age were 0.794, 0.754, 0.746, 0.721 and 0.689 respectively.

Conclusions

The incidence of delirium in postoperative patients with hip fracture is rather high, especially for patients with old age and history of delirium. Monitoring albumin, TSH and resting score may be beneficial to the management of postoperative delirium.

Evaluating and Strengthening the Evidence for Nutritional Bone Research: Ready to Break New Ground?

A healthy diet is essential to attain genetically determined peak bone mass and maintain optimal skeletal health across the adult lifespan. Despite the importance of nutrition for bone health, many of the nutritional requirements of the skeleton across the lifespan remain underexplored, poorly understood, or controversial. With increasingly aging populations, combined with rapidly changing diets and lifestyles globally, one anticipates large increases in the prevalence of osteoporosis and incidence of osteoporotic fractures. Robust, transparent, and reproducible nutrition research is a cornerstone for developing reliable public health recommendations to prevent osteoporosis and osteoporotic fractures. However, nutrition research is often criticized or ignored by healthcare professionals due to the overemphasis of weak science, conflicting, confusing or implausible findings, industry interests, common misconceptions, and strong opinions. Conversely, spurious research findings are often overemphasized or misconstrued by the media or prominent figures especially via social media, potentially leading to confusion and a lack of trust by the general public. Recently, reforms of the broader discipline of nutrition science have been suggested and promoted, leading to new tools and recommendations to attempt to address these issues. In this perspective, we provide a brief overview of what has been achieved in the field on nutrition and bone health, focusing on osteoporosis and osteoporotic fractures. We discuss what we view as some of the challenges, including inherent difficulties in assessing diet and its change, disentangling complex interactions between dietary components and between diet and other factors, selection of bone-related outcomes for nutrition studies, obtaining evidence with more unbiased designs, and perhaps most importantly, ensuring the trust of the public and healthcare professionals. This perspective also provides specific recommendations and highlights new developments and future opportunities for scientists studying nutrition and bone health. © 2021 American Society for Bone and Mineral Research (ASBMR).

Dual-energy X-ray absorptiometry bone densitometry in pediatrics: a practical review and update

The assessment of pediatric bone mineral content and density is an evolving field. In this manuscript we provide a practical review and update on the interpretation of dual-energy X-ray absorptiometry (DXA) in pediatrics including historical perspectives as well as a discussion of the recently published 2019 Official Position Statements of the International Society of Clinical Densitometry (ISCD) that apply to children.

Validation of 3D finite element models from simulated DXA images for biofidelic simulations of sideways fall impact to the hip

Computed tomography (CT)-derived finite element (FE) models have been proposed as a tool to improve the current clinical assessment of osteoporosis and personalized hip fracture risk by providing an accurate estimate of femoral strength. However, this solution has two main drawbacks, namely: (i) 3D CT images are needed, whereas 2D dual-energy x-ray absorptiometry (DXA) images are more generally available, and (ii) quasi-static femoral strength is predicted as a surrogate for fracture risk, instead of predicting whether a fall would result in a fracture or not. The aim of this study was to combine a biofidelic fall simulation technique, based on 3D computed tomography (CT) data with an algorithm that reconstructs 3D femoral shape and BMD distribution from a 2D DXA image. This approach was evaluated on 11 pelvis-femur constructs for which CT scans, ex vivo sideways fall impact experiments and CT-derived biofidelic FE models were available. Simulated DXA images were used to reconstruct the 3D shape and bone mineral density (BMD) distribution of the left femurs by registering a projection of a statistical shape and appearance model with a genetic optimization algorithm. The 2D-to-3D reconstructed femurs were meshed, and the resulting FE models inserted into a biofidelic FE modeling pipeline for simulating a sideways fall. The median 2D-to-3D reconstruction error was 1.02 mm for the shape and 0.06 g/cm³ for BMD for the 11 specimens. FE models derived from simulated DXAs predicted the outcome of the falls in terms of fracture versus non-fracture with the same accuracy as the CT-derived FE models. This study represents a milestone towards improved assessment of hip fracture risk based on widely available clinical DXA images.

Combining shape and intensity dxa-based statistical approaches for osteoporotic HIP fracture risk assessment

Aiming to improve osteoporotic hip fracture risk detection, factors other than the largely adopted Bone Mineral Density (BMD) have been investigated as potential risk predictors. In particular Hip Structural Analysis (HSA)-derived parameters accounting for femur geometry, extracted from Dual-energy X-ray Absorptiometry (DXA) images, have been largely considered as geometric risk factors. However, HSA-derived parameters represent discrete and cross-correlated quantities, unable to describe proximal femur geometry as a whole and tightly related to BMD. Focusing on a post-menopausal cohort (N = 28), in this study statistical models of bone shape and BMD distribution have been developed to investigate their possible role in fracture risk. Due to unavailable retrospective patient-specific fracture risk information, here a surrogate fracture risk based on 3D computer simulations has been employed for the statistical framework construction. When considered separately, BMD distribution performed better than shape in explaining the surrogate fracture risk variability for the analysed cohort. However, the combination of BMD and femur shape quantities in a unique statistical model yielded better results. In detail, the first shape-intensity combined mode identified using a Partial Least Square (PLS) algorithm was able to explain 70% of the surrogate fracture risk variability, thus suggesting that a more effective patients stratification can be obtained applying a shape-intensity combination approach, compared to T-score. The findings of this study strongly advocate future research on the role of a combined shape-BMD statistical framework in fracture risk determination.

Subject-specific FE models of the human femur predict fracture path and bone strength under single-leg-stance loading

Hip fractures are a major health problem with high socio-economic costs. Subject-specific finite element (FE) models have been suggested to improve the fracture risk assessment, as compared to clinical tools based on areal bone mineral density, by adding an estimate of bone strength. Typically, such FE models are limited to estimate bone strength and possibly the fracture onset, but do not model the fracture process itself. The aim of this study was to use a discrete damage approach to simulate the full fracture process in subject-specific femur models under stance loading conditions. A framework based on the partition of unity finite element method (PUFEM), also known as XFEM, was used. An existing PUFEM framework previously used on a homogeneous generic femur model was extended to include a heterogeneous material description together with a strain-based criterion for crack initiation. The model was tested on two femurs, previously mechanically tested in vitro. Our results illustrate the importance of implementing a subject-specific material distribution to capture the experimental fracture pattern under stance loading. Our models accurately predicted the fracture pattern and bone strength (1% and 5% error) in both investigated femurs. This is the first study to simulate complete fracture paths in subject-specific FE femur models and it demonstrated how discrete damage models can provide a more complete picture of fracture risk by considering both bone strength and fracture toughness in a subject-specific fashion.

Hip load capacity and yield load in men and women of all ages

Quantitative computed tomography (QCT) based finite element (FE) models can compute subject-specific proximal femoral strengths, or fracture loads, that are associated with hip fracture risk. These fracture loads are more strongly associated with measured fracture loads than are DXA and QCT measures and are predictive of hip fracture independently of DXA bone mineral density (BMD). However, interpreting FE-computed fracture loads of younger subjects for the purpose of evaluating hip fracture risk in old age is challenging due to limited reference data. The goal of this study was to address this issue by providing reference data for male and female adult subjects of all ages. QCT-based FE models of the left proximal femur of 216 women and 181 men, age 27 to 90 years, from a cohort of Rochester, MN residents were used to compute proximal femoral load capacities, i.e. the maximum loads that can be supported, in single-limb stance and posterolateral fall loading (Stance_LC and Fall_LC, respectively) [US Patent No. 9,245,069] and yield load under fall loading (Fall_yield). To relate these measures to information about hip fracture, the CT scanner and calibration phantom were cross-calibrated with those from our previous prospective study of hip fracture in older fracture and control subjects, the Age Gene/Environment Susceptibility (AGES) Reykjavik cohort. We then plotted Stance_LC_, Fall_LC and Fall_yield versus age for the two cohorts on the same graphs. Thus, proximal femoral strengths in individuals above 70 years of age can be assessed through direct comparison with the FE data from the AGES cohort which were analyzed using identical methods. To evaluate younger individuals, reductions in Stance_LC, Fall_LC and Fall_yield from the time of evaluation to age 70 years can be cautiously estimated from the average yearly cross-sectional decreases found in this study (108 N, 19.4 N and 14.4 N, respectively, in men and 120 N, 19.4 N and 21.6 N, respectively, in women), and the projected fracture loads can be compared with data from the AGES cohort. Although we did not set specific thresholds for identifying individuals at risk of hip fracture, these data provide some guidance and may be used to help establish diagnostic criteria in future. Additionally, given that these data were nearly entirely from Caucasian subjects, future research involving subjects of other races/ethnicities is necessary.

Biomechanical Computed Tomography analysis (BCT) for clinical assessment of osteoporosis

The surgeon general of the USA defines osteoporosis as "a skeletal disorder characterized by compromised bone strength, predisposing to an increased risk of fracture." Measuring bone strength, Biomechanical Computed Tomography analysis (BCT), namely, finite element analysis of a patient's clinical-resolution computed tomography (CT) scan, is now available in the USA as a Medicare screening benefit for osteoporosis diagnostic testing. Helping to address under-diagnosis of osteoporosis, BCT can be applied "opportunistically" to most existing CT scans that include the spine or hip regions and were previously obtained for an unrelated medical indication. For the BCT test, no modifications are required to standard clinical CT imaging protocols. The analysis provides measurements of bone strength as well as a dual-energy X-ray absorptiometry (DXA)-equivalent bone mineral density (BMD) T-score at the hip and a volumetric BMD of trabecular bone at the spine. Based on both the bone strength and BMD measurements, a physician can identify osteoporosis and assess fracture risk (high, increased, not increased), without needing confirmation by DXA. To help introduce BCT to clinicians and health care professionals, we describe in this review the currently available clinical implementation of the test (VirtuOst), its application for managing patients, and the underlying supporting evidence; we also discuss its main limitations and how its results can be interpreted clinically. Together, this body of evidence supports BCT as an accurate and convenient diagnostic test for osteoporosis in both sexes, particularly when used opportunistically for patients already with CT. Biomechanical Computed Tomography analysis (BCT) uses a patient's CT scan to measure both bone strength and bone mineral density at the hip or spine. Performing at least as well as DXA for both diagnosing osteoporosis and assessing fracture risk, BCT is particularly well-suited to "opportunistic" use for the patient without a recent DXA who is undergoing or has previously undergone CT testing (including hip or spine regions) for an unrelated medical condition.