Biofidelic finite element models for accurately classifying hip fracture in a retrospective clinical study of elderly women from the AGES Reykjavik cohort

Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis

OBJECTIVES

Early identification of fracture risk in patients with osteoporosis is essential. Machine learning (ML) has emerged as a promising technique to predict the risk, whereas its predictive performance remains controversial. Therefore, we conducted this systematic review and meta-analysis to explore the predictive efficiency of ML for the risk of fracture in patients with osteoporosis.

METHODS

Relevant studies were retrieved from four databases (PubMed, Embase, Cochrane Library and Web of Science) until 31 May 2023. A meta-analysis of the C-index was performed using a random-effects model, while a bivariate mixed-effects model was used for the meta-analysis of sensitivity and specificity. In addition, subgroup analysis was performed according to the types of ML models and fracture sites.

RESULTS

Fifty-three studies were included in our meta-analysis, involving 15 209 268 patients, 86 prediction models specifically developed for the osteoporosis population and 41 validation sets. The most commonly used predictors in these models encompassed age, BMI, past fracture history, bone mineral density T-score, history of falls, BMD, radiomics data, weight, height, gender and other chronic diseases. Overall, the pooled C-index of ML was 0.75 (95% CI: 0.72, 0.78) and 0.75 (95% CI: 0.71, 0.78) in the training set and validation set, respectively; the pooled sensitivity was 0.79 (95% CI: 0.72, 0.84) and 0.76 (95% CI: 0.80, 0.81) in the training set and validation set, respectively; and the pooled specificity was 0.81 (95% CI: 0.75, 0.86) and 0.83 (95% CI: 0.72, 0.90) in the training set and validation set, respectively.

CONCLUSIONS

ML has a favourable predictive performance for fracture risk in patients with osteoporosis. However, most current studies lack external validation. Thus, external validation is required to verify the reliability of ML models.

PROSPERO REGISTRATION NUMBER

CRD42022346896.

3D Finite Element Models Reconstructed From 2D Dual-Energy X-Ray Absorptiometry (DXA) Images Improve Hip Fracture Prediction Compared to Areal BMD in Osteoporotic Fractures in Men (MrOS) Sweden Cohort

Bone strength is an important contributor to fracture risk. Areal bone mineral density (aBMD) derived from dual-energy X-ray absorptiometry (DXA) is used as a surrogate for bone strength in fracture risk prediction tools. 3D finite element (FE) models predict bone strength better than aBMD, but their clinical use is limited by the need for 3D computed tomography and lack of automation. We have earlier developed a method to reconstruct the 3D hip anatomy from a 2D DXA image, followed by subject-specific FE-based prediction of proximal femoral strength. In the current study, we aim to evaluate the method's ability to predict incident hip fractures in a population-based cohort (Osteoporotic Fractures in Men [MrOS] Sweden). We defined two subcohorts: (i) hip fracture cases and controls cohort: 120 men with a hip fracture (<10 years from baseline) and two controls to each hip fracture case, matched by age, height, and body mass index; and (ii) fallers cohort: 86 men who had fallen the year before their hip DXA scan was acquired, 15 of which sustained a hip fracture during the following 10 years. For each participant, we reconstructed the 3D hip anatomy and predicted proximal femoral strength in 10 sideways fall configurations using FE analysis. The FE-predicted proximal femoral strength was a better predictor of incident hip fractures than aBMD for both hip fracture cases and controls (difference in area under the receiver operating characteristics curve, ΔAUROC = 0.06) and fallers (ΔAUROC = 0.22) cohorts. This is the first time that FE models outperformed aBMD in predicting incident hip fractures in a population-based prospectively followed cohort based on 3D FE models obtained from a 2D DXA scan. Our approach has potential to notably improve the accuracy of fracture risk predictions in a clinically feasible manner (only one single DXA image is needed) and without additional costs compared to the current clinical approach. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Exercise to Prevent and Manage Frailty and Fragility Fractures

PURPOSE OF REVIEW

This review identifies exercise-based recommendations to prevent and manage frailty and fragility fractures from current clinical practice guidelines. We also critically assess recently published literature in relation to exercise interventions to mitigate frailty and fragility fractures.

RECENT FINDINGS

Most guidelines presented similar recommendations that included the prescription of individually tailored, multicomponent exercise programs, discouragement of prolonged sitting and inactivity, and combining exercise with optimal nutrition. To target frailty, guidelines recommend supervised progressive resistance training (PRT). For osteoporosis and fragility fractures, exercise should include weight-bearing impact activities and PRT to target bone mineral density (BMD) at the hip and spine, and also incorporate balance and mobility training, posture exercises, and functional exercise relevant to activities of daily living to reduce falls risk. Walking as a singular intervention has limited benefits for frailty and fragility fracture prevention and management. Current evidence-based clinical practice guidelines for frailty, osteoporosis, and fracture prevention recommend a multifaceted and targeted approach to optimise muscle mass, strength, power, and functional mobility as well as BMD.

The efficacy of femoral augmentation for hip fracture prevention using ceramic-based cements: A preliminary experimentally-driven finite element investigation

Femoral fractures due to sideways falls continue to be a major cause of concern for the elderly. Existing approaches for the prevention of these injuries have limited efficacy. Prophylactic femoral augmentation systems, particularly those involving the injection of ceramic-based bone cements, are gaining more attention as a potential alternative preventative approach. We evaluated the mechanical effectiveness of three variations of a bone cement injection pattern (basic ellipsoid, hollow ellipsoid, small ellipsoid) utilizing finite element simulations of sideways fall impacts. The basic augmentation pattern was tested with both high- and low-strength ceramic-based cements. The cement patterns were added to the finite element models (FEMs) of five cadaveric femurs, which were then subject to simulated sideways falls at seven impact velocities ranging from 1.0 m/s to 4.0 m/s. Peak impact forces and peak acetabular forces were examined, and failure was evaluated using a strain-based criterion. We found that the basic HA ellipsoid provided the highest increases in both the force at the acetabulum of the impacted femur ("acetabular force", 55.0% ± 22.0%) and at the force plate ("impact force", 37.4% ± 15.8%). Changing the cement to a weaker material, brushite, resulted in reduced strengthening of the femur (45.2% ± 19.4% acetabular and 30.4% ± 13.0% impact). Using a hollow version of the ellipsoid appeared to have no effect on the fracture outcome and only a minor effect on the other metrics (54.1% ± 22.3% acetabular force increase and 35.3% ± 16.0% impact force increase). However, when the outer two layers of the ellipsoid were removed (small ellipsoid), the force increases that were achieved were only 9.8% ± 5.5% acetabular force and 8.2% ± 4.1% impact force. These results demonstrate the importance of supporting the femoral neck cortex to prevent femoral fractures in a sideways fall, and provide plausible options for prophylactic femoral augmentation. As this is a preliminary study, the surgical technique, the possible effects of trabecular bone damage during the augmentation process, and the effect on the blood supply to the femoral head must be assessed further.

A Review of CT-Based Fracture Risk Assessment with Finite Element Modeling and Machine Learning

PURPOSE OF REVIEW

We reviewed advances over the past 3 years in assessment of fracture risk based on CT scans, considering methods that use finite element models, machine learning, or a combination of both.

RECENT FINDINGS

Several studies have demonstrated that CT-based assessment of fracture risk, using finite element modeling or biomarkers derived from machine learning, is equivalent to currently used clinical tools. Phantomless calibration of CT scans for bone mineral density enables accurate measurements from routinely taken scans. This opportunistic use of CT scans for fracture risk assessment is facilitated by high-quality automated segmentation with deep learning, enabling workflows that do not require user intervention. Modeling of more realistic and diverse loading conditions, as well as improved modeling of fracture mechanisms, has shown promise to enhance our understanding of fracture processes and improve the assessment of fracture risk beyond the performance of current clinical tools. CT-based screening for fracture risk is effective and, by analyzing scans that were taken for other indications, could be used to expand the pool of people screened, therefore improving fracture prevention. Finite element modeling and machine learning both provide valuable tools for fracture risk assessment. Future approaches should focus on including more loading-related aspects of fracture risk.

The Influence of Fall Direction and Hip Protector on Fracture Risk: FE Model Predictions Driven by Experimental Data

Hip fractures in older adults, which often lead to lasting impairments and an increased risk of mortality, are a major public health concern. Hip fracture risk is multi-factorial, affected by the risk of falling, the load acting on the femur, and the load the femur can withstand. This study investigates the influence of impact direction on hip fracture risk and hip protector efficacy. We simulated falls for 4 subjects, in 7 different impact directions (15° and 30° anterior, lateral, and 15°, 30°, 60°, and 90° posterior) at two different impact velocities (2.1 and 3.1 m/s), all with and without hip protector, using previously validated biofidelic finite element models. We found the highest number of fractures and highest fragility ratios in lateral and 15° posterior impacts. The hip protector attenuated femur forces by 23–49 % for slim subjects under impact directions that resulted in fractures (30° anterior to 30° posterior). The hip protector prevented all fractures (6/6) for 2.1 m/s impacts, but only 10% of fractures for 3.1 m/s impacts. Our results provide evidence that, regarding hip fracture risk, posterior-lateral impacts are as dangerous as lateral impacts, and they support the efficacy of soft-shell hip protectors for anterior- and posterior-lateral impacts.

Finite element derived femoral strength is a better predictor of hip fracture risk than aBMD in the AGES Reykjavik study cohort

Hip fractures associated with a high economic burden, loss of independence, and a high rate of post-fracture mortality, are a major health concern for modern societies. Areal bone mineral density is the current clinical metric of choice when assessing an individual's future risk of fracture. However, this metric has been shown to lack sensitivity and specificity in the targeted selection of individuals for preventive interventions. Although femoral strength derived from computed tomography based finite element models has been proposed as an alternative based on its superior femoral strength prediction ex vivo, such predictions have only shown marginal or no improvement for assessing hip fracture risk. This study compares finite element derived femoral strength to aBMD as a metric for hip fracture risk assessment in subjects (N = 601) from the AGES Reykjavik Study cohort and analyses the dependence of femoral strength predictions and classification accuracy on the material model and femoral loading alignment. We found hip fracture classification based on finite element derived femoral strength to be significantly improved compared to aBMD. Finite element models with non-linear material models performed better at classifying hip fractures compared to finite element models with linear material models and loading alignments with low internal rotation and adduction, which do not correspond to weak femur alignments, were found to be most suitable for hip fracture classification.

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.

Prophylactic augmentation implants in the proximal femur for hip fracture prevention: An in silico investigation of simulated sideways fall impacts

Femoral fractures from sideways falls in the elderly are associated with significant rates of morbidity and mortality. Approaches to prevent these catastrophic injuries include pharmacological treatments, which have limited efficacy. Prophylactic femoral augmentation systems are a promising alternative that are gaining prominence by addressing the most debilitating osteoporosis-related fracture. We have developed finite element models (FEMs) of a novel experimental sideways fall simulator for cadavers. By virtue of the range of specimens and injury outcomes, these FEMs are well-suited to the evaluation of such implants. The purpose of this study was to use the FEMs to evaluate the mechanical effectiveness of three different prophylactic femoral augmentation systems. Models of the Y-Strut® (Hyprevention®, Pessac, France), Gamma Nail® (Stryker, Kalamazoo, USA), and a simple lag screw femoral fracture implant systems were placed into FEMs of five cadaveric pelvis-femur constructs embedded in a soft tissue surrogate, which were then subject to simulated sideways falls at seven impact velocities. Femur-only FEMs were also evaluated. Peak impact forces and peak acetabular forces were examined, and failure was evaluated using a strain-based criterion. We found that the femoral augmentation systems increased the peak forces prior to fracture, but were unable to prevent fracture for severe impacts. The Gamma Nail® system consistently produced the largest strength increases relative to the unaugmented femur for all five specimens in both the pendulum-drop FEMs and the femur-only simulations. In some cases, the same implant appeared to cause fractures in the acetabulum. The femur-only FEMs showed larger force increases than the pendulum-drop simulations, which suggests that the results of the femur-only simulations may not represent sideways falls as accurately as the soft tissue-embedded pendulum-drop simulations. The results from this study demonstrate the ability to simulate a high energy phenomenon and the effect of implants in an in silico environment. The results also suggest that implants could increase the force applied to the proximal femur during impact. Fracture outcomes from the tested implants can be used to inform the design of future devices, which reaffirms the value of modelling with biofidelic considerations in the implant design process. To the authors' knowledge, this is the first paper to use more complex biofidelic FEMs to assess prophylactic femoral augmentation methods.

Shape morphing technique can accurately predict pelvic bone landmarks

Diffeomorphic shape registration allows for the seamless geometric alignment of shapes. In this study, we demonstrated the use of a registration algorithm to automatically seed anthropological landmarks on the CT images of the pelvis. We found a high correlation between manually and automatically seeded landmarks. The registration algorithm makes it possible to achieve a high degree of automation with the potential to reduce operator errors in the seeding of anthropological landmarks. The results of this study represent a promising step forward in effectively defining the anthropological measures of the human skeleton.

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.

Risk Assessment of Hip Fracture Based on Machine Learning

Identifying patients with high risk of hip fracture is a great challenge in osteoporosis clinical assessment. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold standard in osteoporosis clinical assessment. However, its classification accuracy is only around 65%. In order to improve this accuracy, this paper proposes the use of Machine Learning (ML) models trained with data from a biomechanical model that simulates a sideways-fall. Machine Learning (ML) models are models able to learn and to make predictions from data. During a training process, ML models learn a function that maps inputs and outputs without previous knowledge of the problem. The main advantage of ML models is that once the mapping function is constructed, they can make predictions for complex biomechanical behaviours in real time. However, despite the increasing popularity of Machine Learning (ML) models and their wide application to many fields of medicine, their use as hip fracture predictors is still limited. This paper proposes the use of ML models to assess and predict hip fracture risk. Clinical, geometric, and biomechanical variables from the finite element simulation of a side fall are used as independent variables to train the models. Among the different tested models, Random Forest stands out, showing its capability to outperform BMD-DXA, achieving an accuracy over 87%, with specificity over 92% and sensitivity over 83%.

Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning

A great challenge in osteoporosis clinical assessment is identifying patients at higher risk of hip fracture. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold-standard, but its classification accuracy is limited to 65%. DXA-based Finite Element (FE) models have been developed to predict the mechanical failure of the bone. Yet, their contribution has been modest. In this study, supervised machine learning (ML) is applied in conjunction with clinical and computationally driven mechanical attributes. Through this multi-technique approach, we aimed to obtain a predictive model that outperforms BMD and other clinical data alone, as well as to identify the best-learned ML classifier within a group of suitable algorithms. A total number of 137 postmenopausal women (81.4 ± 6.95 years) were included in the study and separated into a fracture group (n = 89) and a control group (n = 48). A semi-automatic and patient-specific DXA-based FE model was used to generate mechanical attributes, describing the geometry, the impact force, bone structure and mechanical response of the bone after a sideways-fall. After preprocessing the whole dataset, 19 attributes were selected as predictors. Support Vector Machine (SVM) with radial basis function (RBF), Logistic Regression, Shallow Neural Networks and Random Forest were tested through a comprehensive validation procedure to compare their predictive performance. Clinical attributes were used alone in another experimental setup for the sake of comparison. SVM was confirmed to generate the best-learned algorithm for both experimental setups, including 19 attributes and only clinical attributes. The first, generated the best-learned model and outperformed BMD by 14pp. The results suggests that this approach could be easily integrated for effective prediction of hip fracture without interrupting the actual clinical workflow.

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.

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.

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.

Explicit Finite Element Models Accurately Predict Subject-Specific and Velocity-Dependent Kinetics of Sideways Fall Impact

The majority of hip fractures in the elderly are the result of a fall from standing or from a lower height. Current injury models focus mostly on femur strength while neglecting subject-specific loading. This article presents an injury modeling strategy for hip fractures related to sideways falls that takes subject-specific impact loading into account. Finite element models (FEMs) of the human body were used to predict the experienced load and the femoral strength in a single model. We validated these models for their predicted peak force, effective pelvic stiffness, and fracture status against matching ex vivo sideways fall impacts (n = 11) with a trochanter velocity of 3.1 m/s. Furthermore, they were compared to sideways impacts of volunteers with lower impact velocities that were previously conducted by other groups. Good agreement was found between the ex vivo experiments and the FEMs with respect to peak force (root mean square error [RMSE] = 10.7%, R² = 0.85) and effective pelvic stiffness (R² = 0.92, RMSE = 12.9%). The FEMs were predictive of the fracture status for 10 out of 11 specimens. Compared to the volunteer experiments from low height, the FEMs overestimated the peak force by 25% for low BMI subjects and 8% for high BMI subjects. The effective pelvic stiffness values that were derived from the FEMs were comparable to those derived from impacts with volunteers. The force attenuation from the impact surface to the femur ranged between 27% and 54% and was highly dependent on soft tissue thickness (R² = 0.86). The energy balance in the FEMS showed that at the time of peak force 79% to 93% of the total energy is either kinetic or was transformed to soft tissue deformation. The presented FEMs allow for direct discrimination between fracture and nonfracture outcome for sideways falls and bridge the gap between impact testing with volunteers and impact conditions representative of real life falls. © 2019 American Society for Bone and Mineral Research.

The effect of boundary and loading conditions on patient classification using finite element predicted risk of fracture

BACKGROUND

Osteoporotic proximal femoral fractures associated to falls are a major health burden in the ageing society. Recently, bone strength estimated by finite element models emerged as a feasible alternative to areal bone mineral density as a predictor of fracture risk. However, previous studies showed that the accuracy of patients' classification under their risk of fracture using finite element strength when simulating posterolateral falls is only marginally better than that of areal bone mineral density. Patients tend to fall in various directions: since the predicted strength is sensitive to the fall direction, a prediction based on certain fall directions might not be fully representative of the physical event. Hence, side fall boundary conditions may not be completely representing the physical event.

METHODS

The effect of different side fall boundary and loading conditions on a retrospective cohort of 98 postmenopausal women was evaluated to test models' ability to discriminate fracture and control cases. Three different boundary conditions (Linear, Multi-point constraints and Contact model) were investigated under various anterolateral and posterolateral falls.

FINDINGS

The stratification power estimated by the area under the receiver operating characteristic curve was highest for Contact model (0.82), followed by Multi-point constraints and Linear models with 0.80. Both Contact and MPC models predicted high strains in various locations of the proximal femur including the greater trochanter, which has rarely reported previously.

INTERPRETATION

A full range of fall directions and less restrictive displacement constraints can improve the finite element strength ability to classify patients under their risk of fracture.