Dataset Information

The application of an isotropic crushable foam model to predict the femoral fracture risk.

ABSTRACT: For biomechanical simulations of orthopaedic interventions, it is imperative to implement a material model that can realistically reproduce the nonlinear behavior of the bone structure. However, a proper material model that adequately combines the trabecular and cortical bone response is not yet widely identified. The current paper aims to investigate the possibility of using an isotropic crushable foam (ICF) model dependent on local bone mineral density (BMD) for simulating the femoral fracture risk. The elastoplastic properties of fifty-nine human femoral trabecular cadaveric bone samples were determined and combined with existing cortical bone properties to characterize two forms of the ICF model, a continuous and discontinuous model. Subsequently, the appropriateness of this combined material model was evaluated by simulating femoral fracture experiments, and a comparison with earlier published results of a softening Von-Mises (sVM) material model was made. The obtained mechanical properties of the trabecular bone specimens were comparable to previous findings. Furthermore, the ultimate failure load predicted by the simulations of femoral fractures was on average 79% and 90% for the continuous and discontinuous forms of the ICF model and 82% of the experimental value for the sVM material model. Also, the fracture locations predicted by ICF models were comparable to the experiments. In conclusion, a nonlinear material model dependent on BMD was characterized for human femoral bone. Our findings indicate that the ICF model could predict the femoral bone strength and reproduce the variable fracture locations in the experiments.

SUBMITTER: Soltanihafshejani N

PROVIDER: S-EPMC10374151 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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The application of an isotropic crushable foam model to predict the femoral fracture risk.

Soltanihafshejani Navid N Peroni Federica F Toniutti Sara S Bitter Thom T Tanck Esther E Eggermont Florieke F Verdonschot Nico N Janssen Dennis D

PloS one 20230727 7

For biomechanical simulations of orthopaedic interventions, it is imperative to implement a material model that can realistically reproduce the nonlinear behavior of the bone structure. However, a proper material model that adequately combines the trabecular and cortical bone response is not yet widely identified. The current paper aims to investigate the possibility of using an isotropic crushable foam (ICF) model dependent on local bone mineral density (BMD) for simulating the femoral fracture ...[more]

PMID: 37498946

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Project description:Atypical femoral fractures (AFFs) occurring during the course of osteoporosis treatment usually lead to discontinuation of anti-resorptive (AR) drugs. However, the risk of fracture after an AFF is unknown. We conducted a follow-up study of patients with AFF matched 1:3 for age and gender with patients with a peripheral major osteoporotic fracture (pMOF), in the setting of a fracture liaison service, to investigate the incidence of subsequent low-trauma fractures. Fifty-five patients with AFF (95% women, age [mean ± standard deviation] 75 ± 10 years, 89% exposed to AR drugs), followed for 6.2 ± 3.7 years, were compared to 165 matched controls with a pMOF (hip 85%) followed for 4.3 ± 2.6 years. During the follow-up, 38% of patients in the AFF group and 16% in the pMOF group received AR therapies. Continuation of AR drugs after an AFF was associated with contralateral AFF in 27% of subjects. The risks of new low-trauma, major osteoporotic and imminent (within 2 years) fractures, were similar between the two groups: incidence rate ratio (95% confidence interval [CI]) of subsequent fracture following AFF relative to pMOF, 1.30 (95% CI, 0.82-2.04), 1.28 (95% CI, 0.74-2.15), and 1.11 (95% CI, 0.54-2.15), respectively. Moreover, the risk of sustaining multiple fractures per participant was significantly increased among patients with AFF compared to pMOF (hazard ratio 1.48 [95% CI, 1.00-2.19]; p = 0.049). When taking mortality into account, the risk of subsequent fractures tended to be higher in the AFF group (sub-hazard ratio 1.42 [95% CI, 0.95-2.12]). In conclusion, patients who sustained an AFF are at high risk of subsequent fragility fractures, at least equal or even greater to the risk observed after a pMOF. However, continuation of AR drugs increases the risk of contralateral AFF. Therefore, optimal modalities for secondary fracture prevention after AFF require further evaluation. © 2021 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Project description:BackgroundHip fracture occurs when an applied force exceeds the force that the proximal femur can support (the fracture load or "strength") and can have devastating consequences with poor functional outcomes. Proximal femoral strengths for specific loading conditions can be computed by subject-specific finite element analysis (FEA) using quantitative computerized tomography (QCT) images. However, the radiation and availability of QCT limit its clinical usability. Alternative low-dose and widely available measurements, such as dual energy X-ray absorptiometry (DXA) and genetic factors, would be preferable for bone strength assessment. The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion.ResultsWe developed new models using multi-view variational autoencoder (MVAE) for feature representation learning and a product of expert (PoE) model for multi-view information fusion. We applied the proposed models to an in-house Louisiana Osteoporosis Study (LOS) cohort with 931 male subjects, including 345 African Americans and 586 Caucasians. We performed genome-wide association studies (GWAS) to select 256 genetic variants with the lowest p-values for each proximal femoral strength and integrated whole genome sequence (WGS) features and DXA-derived imaging features to predict proximal femoral strength. The best prediction model for fall fracture load was acquired by integrating WGS features and DXA-derived imaging features. The designed models achieved the mean absolute percentage error of 18.04%, 6.84% and 7.95% for predicting proximal femoral fracture loads using linear models of fall loading, nonlinear models of fall loading, and nonlinear models of stance loading, respectively.ConclusionThe proposed models are capable of predicting proximal femoral strength using WGS features and DXA-derived imaging features. Though this tool is not a substitute for predicting FEA using QCT images, it would make improved assessment of hip fracture risk more widely available while avoiding the increased radiation exposure from QCT.

Project description:UNLABELLED: The present study estimated the 10-year probability using the Japanese version of WHO fracture risk assessment tool (FRAX) in order to determine fracture probabilities that correspond to intervention thresholds currently used in Japan and to resolve some issues for its use in Japan. INTRODUCTION: The objective of the present study was to evaluate a Japanese version of the WHO fracture risk assessment (FRAX) tool to compute 10-year probabilities of osteoporotic fracture in Japanese men and women. Since lumbar spine bone mineral density (BMD) is used preferentially as a site for assessment, and densitometers use Japanese reference data, a second aim was to investigate the suitability and impact of this practice in Japan. METHODS: Fracture probabilities were computed from published data on the fracture and death hazards in Japan. Probabilities took account of age, sex, the presence of clinical risk factors and femoral neck BMD. Fracture probabilities were determined that were equivalent to intervention thresholds currently used in Japan. The difference between T-scores derived from international reference data and that using Japanese-specific normal ranges was estimated from published sources. The gradient of risk of BMD for fracture in Japan was compared to that for BMD at the lumbar spine in the Hiroshima cohort. RESULTS: The 10-year probabilities of a major osteoporosis-related fracture that corresponded to current intervention thresholds ranged from approximately 5% at the age of 50 years to more than 20% at the age of 80 years. The use of femoral neck BMD predicts fracture as well as or better than BMD tests at the lumbar spine. There were small differences in T-scores between those used for the model and those derived from a Japanese reference population. CONCLUSIONS: The FRAX mark tool has been used to determine possible thresholds for therapeutic intervention, based on equivalence of risk with current guidelines. The approach will need to be supported by appropriate health economic analyses. Femoral neck BMD is suitable for the prediction of fracture risk among Japanese. However, when applying the FRAX model to Japan, T-scores and Z-scores should be converted to those derived from the international reference.

Project description:BackgroundThe absolute number of femoral neck fractures (FNFs) is increasing; however, the prediction of traumatic femoral head necrosis remains difficult. Machine learning algorithms have the potential to be superior to traditional prediction methods for the prediction of traumatic femoral head necrosis.ObjectiveThe aim of this study is to use machine learning to construct a model for the analysis of risk factors and prediction of osteonecrosis of the femoral head (ONFH) in patients with FNF after internal fixation.MethodsWe retrospectively collected preoperative, intraoperative, and postoperative clinical data of patients with FNF in 4 hospitals in Shanghai and followed up the patients for more than 2.5 years. A total of 259 patients with 43 variables were included in the study. The data were randomly divided into a training set (181/259, 69.8%) and a validation set (78/259, 30.1%). External data (n=376) were obtained from a retrospective cohort study of patients with FNF in 3 other hospitals. Least absolute shrinkage and selection operator regression and the support vector machine algorithm were used for variable selection. Logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting (XGBoost) were used to develop the model on the training set. The validation set was used to tune the model hyperparameters to determine the final prediction model, and the external data were used to compare and evaluate the model performance. We compared the accuracy, discrimination, and calibration of the models to identify the best machine learning algorithm for predicting ONFH. Shapley additive explanations and local interpretable model-agnostic explanations were used to determine the interpretability of the black box model.ResultsA total of 11 variables were selected for the models. The XGBoost model performed best on the validation set and external data. The accuracy, sensitivity, and area under the receiver operating characteristic curve of the model on the validation set were 0.987, 0.929, and 0.992, respectively. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the model on the external data were 0.907, 0.807, 0.935, and 0.933, respectively, and the log-loss was 0.279. The calibration curve demonstrated good agreement between the predicted probability and actual risk. The interpretability of the features and individual predictions were realized using the Shapley additive explanations and local interpretable model-agnostic explanations algorithms. In addition, the XGBoost model was translated into a self-made web-based risk calculator to estimate an individual's probability of ONFH.ConclusionsMachine learning performs well in predicting ONFH after internal fixation of FNF. The 6-variable XGBoost model predicted the risk of ONFH well and had good generalization ability on the external data, which can be used for the clinical prediction of ONFH after internal fixation of FNF.

Dataset Information

The application of an isotropic crushable foam model to predict the femoral fracture risk.

Publications

The application of an isotropic crushable foam model to predict the femoral fracture risk.

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