Precision study of DXA-based patient-specific finite element modeling for assessing hip fracture risk

Study of the significance of parameters and their interaction on assessing femoral fracture risk by quantitative statistical analysis

Early assessment of hip fracture helps develop therapeutic and preventive mechanisms that may reduce the occurrence of hip fracture. An accurate assessment of hip fracture risk requires proper consideration of the loads, the physiological and morphological parameters, and the interactions between these parameters. Hence, this study aims at analyzing the significance of parameters and their interactions by conducting a quantitative statistical analysis. A multiple regression model was developed considering different loading directions during a sideways fall (angle [Formula: see text] and [Formula: see text] on the coronal and transverse planes, respectively), age, gender, patient weight, height, and femur morphology as independent parameters and Fracture Risk Index (FRI) as a dependent parameter. Strain-based criteria were used for the calculation of FRI with the maximum principal strain obtained from quantitative computed tomography-based finite element analysis. The statistical result shows that [Formula: see text] [Formula: see text], age [Formula: see text], true moment length [Formula: see text], gender [Formula: see text], FNA [Formula: see text], height [Formula: see text], and FSL [Formula: see text] significantly affect FRI where [Formula: see text] is the most influential parameter. The significance of two-level interaction [Formula: see text] and three-level interaction [Formula: see text] shows that the effect of parameters is dissimilar and depends on other parameters suggesting the variability of FRI from person to person.

Image-Based Polygonal Lattices for Mechanical Modeling of Biological Materials: 2D Demonstrations

Understanding the structure-property relationship of biological materials, such as bones, teeth, cells, and biofilms, is critical for diagnosing diseases and developing bioinspired materials and structures. The intrinsic multiphase heterogeneity with interfaces places great challenges for mechanical modeling. Here, we develop an image-based polygonal lattice model for simulating the mechanical deformation of biological materials with complicated shapes and interfaces. The proposed lattice model maintains the uniform meshes inside the homogeneous phases and restricts the irregular polygonal meshes near the boundaries or interfaces. This approach significantly simplifies the mesh generation from images of biological structures with complicated geometries. The conventional finite element simulations validate this polygonal lattice model. We further demonstrate that the image-based polygonal lattices generate meshes from images of composite structures with multiple inclusions and capture the nonlinear mechanical deformation. We conclude the paper by highlighting a few future research directions that will benefit from the functionalities of polygonal lattice modeling.

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.

Predicting population level hip fracture risk: a novel hierarchical model incorporating probabilistic approaches and factor of risk principles

Fall-related hip fractures are a major public health issue. While individual-level risk assessment tools exist, population-level predictive models could catalyze innovation in large-scale interventions. This study presents a hierarchical probabilistic model that predicts population-level hip fracture risk based on Factor of Risk (FOR) principles. Model validation demonstrated that FOR output aligned with a published dataset categorized by sex and hip fracture status. The model predicted normalized FOR for 100000 individuals simulating the Canadian older-adult population. Predicted hip fracture risk was higher for females (by an average of 38%), and increased with age (by15% per decade). Potential applications are discussed.

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.

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

We reviewed the experimental and clinical evidence that hip bone strength estimated by BMD and/or finite element analysis (FEA) reflects the actual strength of the proximal femur and is associated with hip fracture risk and its changes upon treatment.

INTRODUCTION

The risk of hip fractures increases exponentially with age due to a progressive loss of bone mass, deterioration of bone structure, and increased incidence of falls. Areal bone mineral density (aBMD), measured by dual-energy X-ray absorptiometry (DXA), is the most used surrogate marker of bone strength. However, age-related declines in bone strength exceed those of aBMD, and the majority of fractures occur in those who are not identified as osteoporotic by BMD testing. With hip fracture incidence increasing worldwide, the development of accurate methods to estimate bone strength in vivo would be very useful to predict the risk of hip fracture and to monitor the effects of osteoporosis therapies.

METHODS

We reviewed experimental and clinical evidence regarding the association between aBMD and/orCT-finite element analysis (FEA) estimated femoral strength and hip fracture risk as well as their changes with treatment.

RESULTS

Femoral aBMD and bone strength estimates by CT-FEA explain a large proportion of femoral strength ex vivo and predict hip fracture risk in vivo. Changes in femoral aBMD are strongly associated with anti-fracture efficacy of osteoporosis treatments, though comparable data for FEA are currently not available.

CONCLUSIONS

Hip aBMD and estimated femoral strength are good predictors of fracture risk and could potentially be used as surrogate endpoints for fracture in clinical trials. Further improvements of FEA may be achieved by incorporating trabecular orientations, enhanced cortical modeling, effects of aging on bone tissue ductility, and multiple sideway fall loading conditions.

Fracture Risk Indices From DXA-Based Finite Element Analysis Predict Incident Fractures Independently From FRAX: The Manitoba BMD Registry

OBJECTIVE

Finite element analysis (FEA) is a computational method to predict the behavior of materials under applied loading. We developed a software tool that automatically performs FEA on dual-energy X-ray absorptiometry hip scans to generate site-specific fracture risk indices (FRIs) that reflect the likelihood of hip fracture from a sideways fall. This longitudinal study examined associations between FRIs and incident fractures.

METHODS

Using the Manitoba Bone Mineral Density (BMD) Registry, femoral neck (FN), intertrochanter (IT), and subtrochanter (ST) FRIs were automatically derived from 13,978 anonymized dual-energy X-ray absorptiometry scans (Prodigy, GE Healthcare) in women and men aged 50 yr or older (mean age 65 yr). Baseline covariates and incident fractures were assessed from population-based data. We compared c-statistics for FRIs vs FN BMD alone and fracture risk assessment (FRAX) probability computed with BMD. Cox regression was used to estimate hazard ratios and 95% confidence intervals (95% CIs) for incident hip, major osteoporotic fracture (MOF) and non-hip MOF adjusted for relevant covariates including age, sex, FN BMD, FRAX probability, FRAX risk factors, and hip axis length (HAL).

RESULTS

During mean follow-up of 6 yr, there were 268 subjects with incident hip fractures, 1003 with incident MOF, and 787 with incident non-hip MOF. All FRIs gave significant stratification for hip fracture (c-statistics FN-FRI: 0.76, 95% CI 0.73-0.79, IT-FRI 0.74, 0.71-0.77; ST-FRI 0.72, 0.69-0.75). FRIs continued to predict hip fracture risk even after adjustment for age and sex (hazard ratio per standard deviation FN-FRI 1.89, 95% CI 1.66-2.16); age, sex, and BMD (1.26, 1.07-1.48); FRAX probability (1.30, 1.11-1.52); FRAX probability with HAL (1.26, 1.05-1.51); and individual FRAX risk factors (1.32, 1.09-1.59). FRIs also predicted MOF and non-hip MOF, but the prediction was not as strong as for hip fracture.

SUMMARY

Automatically-derived FN, IT, and ST FRIs are associated with incident hip fracture independent of multiple covariates, including FN BMD, FRAX probability and risk factors, and HAL.

Assessment of finite element models for prediction of osteoporotic fracture

With increasing life expectancy and mortality rates, the burden of osteoporotic hip fractures is continually on an upward trend. In terms of prevention, there are several osteoporosis treatment strategies such as anti-resorptive drug treatments, which attempt to retard the rate of bone resorption, while promoting the rate of formation. With respect to prediction, several studies have provided insights into obtaining bone strength by non-invasive means through the application of FE analysis. However, what valuable information can we obtain from FE studies that have focused on osteoporosis research, with respect to the prediction of osteoporotic fractures? This paper aims to fine studies that have used FE analysis to predict fractures in the proximal femur through a systematic search of literature using PUBMED, with the main objective of supporting the diagnosis of osteoporosis. The focus of these FE studies is first discussed, and the methodological aspects are summarized, by mainly comparing and contrasting their meshing properties, material properties, and boundary conditions. The implications of these methodological differences in FE modelling processes and propositions with the aim of consolidating or minimalizing these differences are further discussed. We proved that studies need to start converging in terms of their input parameters to make the FE method applicable to clinical settings. This, in turn, will decrease the time needed for in vitro tests. Current advancements in FE analysis need to be consolidated before any further steps can be taken to implement engineering analysis into the clinical scenario.

Assessment of hip fracture risk by cross-sectional strain-energy derived from image-based beam model

BACKGROUND

Clinicians have been looking for a simple and effective biomechanical tool for the assessment of hip fracture risk. Dual-energy X-ray absorptiometry (DXA) is currently the primary bone imaging modality in clinic, and the engineering beam is the simplest model for a mechanical analysis. Therefore, we developed a DXA-based beam model for the above purpose.

METHODS

A beam model of the proximal femur was constructed from the subject's hip DXA image and denoted DXA-beam. Femur stiffness was calculated at cross-sections of interest using areal bone-mineral-density profile. Impact force induced in a sideways fall was applied as a critical loading. Fracture risk index at a cross-section was defined as the ratio of strain-energy induced by the impact force to the allowable strain-energy. A clinic cohort was used to study the discriminability of DXA-beam, which was measured by the area under the curve and odds ratio, both with 95% confidential interval.

FINDINGS

Fracture risk measured by DXA-beam model at the femoral neck [odds ratio 2.23, 95% confidence interval (1.83, 2.57)], inter-trochanter [2.49, (2.14, 3.25)] and sub-trochanter [2.82, (2.38, 3.51)] were strongly associated with hip fracture. The area under the curve by DXA-beam at the femoral neck [0.74, 95% confidence interval (0.70, 0.76)], inter-trochanter [0.77, (0.75, 0.82)] and sub-trochanter [0.76, (0.74, 0.81)] were higher than that by femoral neck bone mineral density [0.71, (0.65, 0.78)].

INTERPRETATION

The DXA-beam model is a simple and yet effective mechanical model. It had promising performance in discrimination of fracture cases from controls.

Lean mass and lower limb muscle function in relation to hip strength, geometry and fracture risk indices in community-dwelling older women

In a population-based sample of British women aged over 70 years old, lean mass and peak lower limb muscle force were both independently associated with hip strength and fracture risk indices, thereby suggesting a potential benefit of promoting leg muscle strengthening exercise for the prevention of hip fractures in postmenopausal women.

INTRODUCTION

To investigate cross-sectional associations of lean mass and physical performance, including lower limb muscle function, with hip strength, geometry and fracture risk indices (FRIs) in postmenopausal women.

METHODS

Data were from the Cohort of Skeletal Health in Bristol and Avon. Total hip (TH) and femoral neck (FN) bone mineral density (BMD), hip geometry and total body lean mass (TBLM) were assessed by dual x-ray absorptiometry (DXA). Finite element analysis of hip DXA was used to derive FN, intertrochanteric and subtrochanteric FRIs. Grip strength, gait speed and chair rise time were measured objectively. Lower limb peak muscle force and muscle power were assessed by jumping mechanography.

RESULTS

In total, 241 women were included (age = 76.4; SD = 2.6 years). After adjustment for age, height, weight/fat mass and comorbidities, TBLM was positively associated with hip BMD (β_{TH BMD} = 0.36, P ≤ 0.001; β_{FN BMD} = 0.26, P = 0.01) and cross-section moment of inertia (0.24, P ≤ 0.001) and inversely associated with FN FRI (- 0.21, P = 0.03) and intertrochanteric FRI (- 0.11, P = 0.05) (estimates represent SD difference in bone measures per SD difference in TBLM). Lower limb peak muscle force was positively associated with hip BMD (β_{TH BMD} = 0.28, P ≤ 0.001; β_{FN BMD} = 0.23, P = 0.008) and inversely associated with FN FRI (- 0.17, P = 0.04) and subtrochanteric FRI (- 0.18, P = 0.04). Associations of grip strength, gait speed, chair rise time and peak muscle power with hip parameters were close to the null.

CONCLUSIONS

Lean mass and lower limb peak muscle force were associated with hip BMD and geometrical FRIs in postmenopausal women. Leg muscle strengthening exercises may therefore help prevent hip fractures in older women.

Comparison of femoral strength and fracture risk index derived from DXA-based finite element analysis for stratifying hip fracture risk: A cross-sectional study

BACKGROUND

Dual-energy X-ray absorptiometry (DXA)-based finite element analysis (FEA) has been studied for assessment of hip fracture risk. Femoral strength (FS) is the maximum force that the femur can sustain before its weakest region reaches the yielding limit. Fracture risk index (FRI), which also considers subject-specific impact force, is defined as the ratio of von Mises stress induced by a sideways fall to the bone yield stress over the proximal femur. We compared risk stratification for prior hip fracture using FS and FRI derived from DXA-based FEA.

METHODS

The study cohort included women aged ≥65years undergoing baseline hip DXA, with femoral neck T-scores <-1 and no osteoporosis treatment; 324 cases had prior hip fracture and 655 controls had no prior fracture. Using anonymized DXA hip scans, we measured FS and FRI. Separate multivariable logistic regression models were used to estimate odds ratios (ORs), c-statistics and their 95% confidence intervals (95% CIs) for the association of hip fracture with FS and FRI.

RESULTS

Increased hip fracture risk was associated with lower FS (OR per SD 1.36, 95% CI: 1.15, 1.62) and higher FRI (OR per SD 1.99, 95% CI: 1.63, 2.43) after adjusting for Fracture Risk Assessment Tool (FRAX) hip fracture probability computed with bone mineral density (BMD). The c-statistic for the model containing FS (0.69; 95% CI: 0.65, 0.72) was lower than the c-statistic for the model with FRI (0.77; 95% CI: 0.74, 0.80) or femoral neck BMD (0.74; 95% CI: 0.71, 0.77; all P<0.05).

CONCLUSIONS

FS and FRI were independently associated with hip fracture, but there were differences in performance characteristics.

Automation of a DXA-based finite element tool for clinical assessment of hip fracture risk

Finite element analysis of medical images is a promising tool for assessing hip fracture risk. Although a number of finite element models have been developed for this purpose, none of them have been routinely used in clinic. The main reason is that the computer programs that implement the finite element models have not been completely automated, and heavy training is required before clinicians can effectively use them. By using information embedded in clinical dual energy X-ray absorptiometry (DXA), we completely automated a DXA-based finite element (FE) model that we previously developed for predicting hip fracture risk. The automated FE tool can be run as a standalone computer program with the subject's raw hip DXA image as input. The automated FE tool had greatly improved short-term precision compared with the semi-automated version. To validate the automated FE tool, a clinical cohort consisting of 100 prior hip fracture cases and 300 matched controls was obtained from a local community clinical center. Both the automated FE tool and femoral bone mineral density (BMD) were applied to discriminate the fracture cases from the controls. Femoral BMD is the gold standard reference recommended by the World Health Organization for screening osteoporosis and for assessing hip fracture risk. The accuracy was measured by the area under ROC curve (AUC) and odds ratio (OR). Compared with femoral BMD (AUC = 0.71, OR = 2.07), the automated FE tool had a considerably improved accuracy (AUC = 0.78, OR = 2.61 at the trochanter). This work made a large step toward applying our DXA-based FE model as a routine clinical tool for the assessment of hip fracture risk. Furthermore, the automated computer program can be embedded into a web-site as an internet application.

Automated DXA-based finite element analysis for hip fracture risk stratification: a cross-sectional study

Fracture risk indices (FRIs) generated from DXA-based finite element analysis were associated with hip fracture independent of FRAX score computed with femoral neck bone mineral density (BMD). Prospective studies are warranted to determine whether FRIs represent an improvement over BMD for predicting incident hip fractures.

INTRODUCTION

The study aims to examine the association between prior hip fracture and FRIs derived from automated finite element analysis (FEA) of DXA hip scans. Femoral neck, intertrochanteric, and subtrochanteric FRIs were calculated as the von Mises stress induced by a sideways fall divided by the bone yield stress over the specified region of interest (ROI).

METHODS

Using the Manitoba Bone Mineral Density Database, we selected women age ≥ 65 years with femoral neck T-scores below - 1 and no osteoporosis treatment. From this population, we identified 324 older women with hip fracture before DXA testing and a random sample of 658 non-fracture controls. FRIs were derived from the anonymized DXA scans. Logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (95% CIs) for the associations between FRIs (per SD increase) and hip fracture.

RESULTS

After adjusting for FRAX score (hip fracture with BMD), femoral neck FRI (OR 1.36, 95% CI 1.13, 1.64), intertrochanteric FRI (OR 1.81, 95% CI 1.44, 2.27), and subtrochanteric FRI (OR 2.09, 95% CI 1.68, 2.60) were associated with hip fracture. Intertrochanteric and subtrochanteric FRIs gave significantly higher c-statistics (all P ≤ 0.05) than femoral neck BMD. Subgroup analyses showed that all FRIs were more strongly associated with hip fracture in women who were younger and had higher body mass index (BMI) or non-osteoporotic BMD (all P _interaction < 0.1).

CONCLUSIONS

FRIs derived from DXA-based FEA were independently associated with prior hip fracture, suggesting that they could potentially improve hip fracture risk assessment.

Denoising images of dual energy X-ray absorptiometry using non-local means filters

BACKGROUND

In general, the image quality of high and low energy images of dual energy X-ray absorptiometry (DXA) suffers from noise due to the use of a small amount of X-rays. Denoising of DXA images could be a key process to improve a bone mineral density map, which is derived from a pair of high and low energy images. This could further improve the accuracy of diagnosis of bone fractures and osteoporosis.

OBJECTIVE

This study aims to develop and test a new technology to improve the quality, remove the noise, and preserve the edges and fine details of real DXA images.

METHODS

In this study, a denoising technique for high and low energy DXA images using a non-local mean filter (NLM) was presented. The source and detector noises of a DXA system were modeled for both high and low DXA images. Then, the optimized parameters of the NLM filter were derived utilizing the experimental data from CIRS-BFP phantoms. After that, the optimized NLM was tested and verified using the DXA images of the phantoms and real human spine and femur.

RESULTS

Quantitative evaluation of the results showed average 24.22% and 34.43% improvement of the signal-to-noise ratio for real high and low spine images, respectively, while the improvements were about 15.26% and 13.55% for the high and low images of the femur. The qualitative visual observations of both phantom and real structures also showed significantly improved quality and reduced noise while preserving the edges in both high and low energy images. Our results demonstrate that the proposed NLM outperforms the conventional method using an anisotropic diffusion filter (ADF) and median techniques for all phantom and real human DXA images.

CONCLUSIONS

Our work suggests that denoising via NLM could be a key preprocessing method for clinical DXA imaging.

Non-BMD DXA measurements of the hip

Hip fracture is one of the most serious complications of osteoporosis. More than 50% of hip and other fractures occur in patients without densitometric osteoporosis. Therefore, areal bone mineral density (aBMD) may not be the best way to assess fracture risk. In order to improve assessment of fracture risk, many other approaches have been taken. At the present time, the Fracture Risk Algorithm (FRAX©) is one of the most notable ways to improve assessment of fracture risk. However, since early in the initiation of the dual energy x-ray absorptiometry (DXA) era, several non-BMD DXA approaches to the assessment of hip fracture risk have been proposed. This review will cover some of those methodologies, including hip-axis length (HAL), hip-structural analysis (HSA), finite element analysis (FEA) by DXA, and body composition of the thigh by DXA (BCT). These methods have been utilized in models of hip fracture occurrence and in pharmacological clinical trials. How they should be used in clinical practice or if they should be used in clinical practice is more of an issue. In addition, we will discuss the recent proposal of the use of Long Femur Scan Field in the effort to diagnose atypical femoral fractures.

Non-local means filter denoising for DEXA images

Dual high and low energy images of Dual Energy X-ray Absorptiometry (DEXA) suffer from noises due to the use of weak amount of X-rays. Denoising these DEXA images could be a key process to enhance and improve a Bone Mineral Density (BMD) map which is derived from a pair of high and low energy images. This could further improve the accuracy of diagnosis of bone fractures, osteoporosis, and etc. In this paper, we present a denoising technique for dual high and low energy images of DEXA via non-local means filter (NLMF). The noise of dual DEXA images is modeled based on both source and detector noises of a DEXA system. Then, the parameters of the proposed NLMF are optimized for denoising utilizing the experimental data from uniform phantoms. The optimized NLMF is tested and verified with the DEXA images of the uniform phantoms and real human spine. The quantitative evaluation shows the improvement of Signal-to-Noise Ratio (SNR) for the high and low phantom images on the order of 30.36% and 27.02% and for the high and low real spine images on the order of 22.28% and 33.43%, respectively. Our work suggests that denoising via NLMF could be a key preprocessing process for clinical DEXA imaging.

Finite Element-Based Mechanical Assessment of Bone Quality on the Basis of In Vivo Images

Beyond bone mineral density (BMD), bone quality designates the mechanical integrity of bone tissue. In vivo images based on X-ray attenuation, such as CT reconstructions, provide size, shape, and local BMD distribution and may be exploited as input for finite element analysis (FEA) to assess bone fragility. Further key input parameters of FEA are the material properties of bone tissue. This review discusses the main determinants of bone mechanical properties and emphasizes the added value, as well as the important assumptions underlying finite element analysis. Bone tissue is a sophisticated, multiscale composite material that undergoes remodeling but exhibits a rather narrow band of tissue mineralization. Mechanically, bone tissue behaves elastically under physiologic loads and yields by cracking beyond critical strain levels. Through adequate cell-orchestrated modeling, trabecular bone tunes its mechanical properties by volume fraction and fabric. With proper calibration, these mechanical properties may be incorporated in quantitative CT-based finite element analysis that has been validated extensively with ex vivo experiments and has been applied increasingly in clinical trials to assess treatment efficacy against osteoporosis.

Study of sex differences in the association between hip fracture risk and body parameters by DXA-based biomechanical modeling

There is controversy about whether or not body parameters affect hip fracture in men and women in the same way. In addition, although bone mineral density (BMD) is currently the most important single discriminator of hip fracture, it is unclear if BMD alone is equally effective for men and women. The objective of this study was to quantify and compare the associations of hip fracture risk with BMD and body parameters in men and women using our recently developed two-level biomechanical model that combines a whole-body dynamics model with a proximal-femur finite element model. Sideways fall induced impact force of 130 Chinese clinical cases, including 50 males and 80 females, were determined by subject-specific dynamics modeling. Then, a DXA-based finite element model was used to simulate the femur bone under the fall-induced loading conditions and calculate the hip fracture risk. Body weight, body height, body mass index, trochanteric soft tissue thickness, and hip bone mineral density were determined for each subject and their associations with impact force and hip fracture risk were quantified. Results showed that the association between impact force and hip fracture risk was not strong enough in both men (r=-0.31,p<0.05) and women (r=0.42,p<0.001) to consider the force as a sole indicator of hip fracture risk. The correlation between hip BMD and hip fracture risk in men (r=-0.83,p<0.001) was notably stronger than that in women (r=-0.68,p<0.001). Increased body mass index was not a protective factor against hip fracture in men (r=-0.13,p>0.05), but it can be considered as a protective factor among women (r=-0.28,p<0.05). In contrast to men, trochanteric soft tissue thickness can be considered as a protective factor against hip fracture in women (r=-0.50,p<0.001). This study suggested that the biomechanical risk/protective factors for hip fracture are sex-specific. Therefore, the effect of body parameters should be considered differently for men and women in hip fracture risk assessment tools. These findings support further exploration of sex-specific preventive and protective measurements to reduce the incidence of hip fractures.

A biomechanical sorting of clinical risk factors affecting osteoporotic hip fracture

Osteoporotic fracture has been found associated with many clinical risk factors, and the associations have been explored dominantly by evidence-based and case-control approaches. The major challenges emerging from the studies are the large number of the risk factors, the difficulty in quantification, the incomplete list, and the interdependence of the risk factors. A biomechanical sorting of the risk factors may shed lights on resolving the above issues. Based on the definition of load-strength ratio (LSR), we first identified the four biomechanical variables determining fracture risk, i.e., the risk of fall, impact force, bone quality, and bone geometry. Then, we explored the links between the FRAX clinical risk factors and the biomechanical variables by looking for evidences in the literature. To accurately assess fracture risk, none of the four biomechanical variables can be ignored and their values must be subject-specific. A clinical risk factor contributes to osteoporotic fracture by affecting one or more of the biomechanical variables. A biomechanical variable represents the integral effect from all the clinical risk factors linked to the variable. The clinical risk factors in FRAX mostly stand for bone quality. The other three biomechanical variables are not adequately represented by the clinical risk factors. From the biomechanical viewpoint, most clinical risk factors are interdependent to each other as they affect the same biomechanical variable(s). As biomechanical variables must be expressed in numbers before their use in calculating LSR, the numerical value of a biomechanical variable can be used as a gauge of the linked clinical risk factors to measure their integral effect on fracture risk, which may be more efficient than to study each individual risk factor.

Assessment of Hip Fracture Risk Using Cross-Section Strain Energy Determined by QCT-Based Finite Element Modeling

Accurate assessment of hip fracture risk is very important to prevent hip fracture and to monitor the effect of a treatment. A subject-specific QCT-based finite element model was constructed to assess hip fracture risk at the critical locations of femur during the single-leg stance and the sideways fall. The aim of this study was to improve the prediction of hip fracture risk by introducing a novel failure criterion to more accurately describe bone failure mechanism. Hip fracture risk index was defined using cross-section strain energy, which is able to integrate information of stresses, strains, and material properties affecting bone failure. It was found that the femoral neck and the intertrochanteric region have higher fracture risk than other parts of the femur, probably owing to the larger content of cancellous bone in these regions. The study results also suggested that women are more prone to hip fracture than men. The findings in this study have a good agreement with those clinical observations reported in the literature. The proposed hip fracture risk index based on strain energy has the potential of more accurate assessment of hip fracture risk. However, experimental validation should be conducted before its clinical applications.

A two-level subject-specific biomechanical model for improving prediction of hip fracture risk

BACKGROUND

Sideways fall-induced hip fracture is a major worldwide health problem among the elderly population. However, all existing biomechanical models for predicting hip fracture mainly consider the femur related parameters. Their accuracy is limited as hip fracture is significantly affected by loading conditions as well. The objective of this study was to develop a biomechanical model for improving assessment of hip fracture risk by subject-specific prediction of fall-induced loading conditions.

METHOD

All information required to construct the models was extracted from the subject's whole-body and hip medical image in order to make the models subject-specific. Fall-induced hip fracture risk for eighty clinical cases was calculated under two sets of loading conditions: subject-specific determined by the proposed model, and non-subject-specific obtained from empirical functions. The predicted hip fracture risk indices were then compared with clinical observations.

FINDINGS

It was found that the subject-specific prediction of fall-induced loading conditions significantly improves the hip fracture risk assessment. Consistent to the clinical observations, the fracture risk predicted by the proposed model suggested that obesity is a protective factor for hip fracture and underweight subjects are more likely to experience a hip fracture.

INTERPRETATIONS

This study shows that hip fracture risk is affected by a number of factors, including body weight, body height, impact force, body mass index, hip soft tissue thickness, and bone quality. The proposed model provides a comprehensive, fast, accurate, and non-expensive method for prediction of hip fracture risk which should lead to more effective prevention of hip fractures.

Study of DXA-derived lateral-medial cortical bone thickness in assessing hip fracture risk

The currently available clinical tools have limited accuracy in predicting hip fracture risk in individuals. We investigated the possibility of using normalized cortical bone thickness (NCBT) estimated from the patient's hip DXA (dual energy X-ray absorptiometry) as an alternative predictor of hip fracture risk. Hip fracture risk index (HFRI) derived from subject-specific DXA-based finite element model was used as a guideline in constructing the mathematical expression of NCBT. We hypothesized that if NCBT has stronger correlations with HFRI than the single risk factors such as areal BMD (aBMD), then NCBT can be a better predictor. The hypothesis was studied using 210 clinical cases, including 60 hip fracture cases, obtained from the Manitoba Bone Mineral Density Database. The results showed that, in general HFRI has much stronger correlations with NCBT than any of the single risk factors; the strongest correlation was observed at the superior side of the narrowest femoral neck with r² = 0.81 (p < 0.001), which is much higher than the correlation with femoral aBMD, r² = 0.50 (p < 0.001). The capability of aBMD, NCBT, and HFRI in discriminating the hip fracture cases from the non-fracture ones, expressed as the area under the curve with 95% confidence interval, AUC (95% CI), is respectively 0.627 (0.593–0.657), 0.714 (0.644–0.784) and 0.839 (0.787–0.892). The short-term repeatability of aBMD, NCBT, and HFRI, measured by the coefficient of variation (CV, %), was found to be in the range of (0.64–1.22), (1.93–3.41), (3.10–4.16), respectively. We thus concluded that NCBT is potentially a better predictor of hip fracture risk.

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A new algorithm developed for estimating cortical bone thickness from clinical DXA images.

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Validation of the algorithm with QCT Pro, a commercial software for processing QCT scans.

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A more effective risk predictor constructed from finite element studies.

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The risk predictor can be readily integrated into the current clinical procedure.