Assessment Of Hip Fracture Risk Research Articles

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

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

DXA-based statistical models of shape and intensity outperform aBMD hip fracture prediction: A retrospective study

Areal bone mineral density (aBMD) currently represents the clinical gold standard for hip fracture risk assessment. Nevertheless, it is characterised by a limited prediction accuracy, as about half of the people experiencing a fracture are not classified as at being at risk by aBMD. In the context of a progressively ageing population, the identification of accurate predictive tools would be pivotal to implement preventive actions. In this study, DXA-based statistical models of the proximal femur shape, intensity (i.e., density) and their combination were developed and employed to predict hip fracture on a retrospective cohort of post-menopausal women. Proximal femur shape and pixel-by-pixel aBMD values were extracted from DXA images and partial least square (PLS) algorithm adopted to extract corresponding modes and components. Subsequently, logistic regression models were built employing the first three shape, intensity and shape-intensity PLS components, and their ability to predict hip fracture tested according to a 10-fold cross-validation procedure. The area under the ROC curves (AUC) for the shape, intensity, and shape-intensity-based predictive models were 0.59 (95%CI 0.47–0.69), 0.80 (95%CI 0.70–0.90) and 0.83 (95%CI 0.73–0.90), with the first being significantly lower than the latter two. aBMD yielded an AUC of 0.72 (95%CI 0.59–0.82), found to be significantly lower than the shape-intensity-based predictive model. In conclusion, a methodology to assess hip fracture risk uniquely based on the clinically available imaging technique, DXA, is proposed. Our study results show that hip fracture risk prediction could be enhanced by taking advantage of the full set of information DXA contains.

QCT-based 3D finite element modeling to assess patient-specific hip fracture risk and risk factors

Early assessment of hip fracture risk may play a critical role in designing preventive mechanisms to reduce the occurrence of hip fracture in geriatric people. The loading direction, clinical, and morphological variables play a vital role in hip fracture. Analyzing the effects of these variables helps predict fractures risk more accurately; thereby suggesting the critical variable that needs to be considered. Hence, this work considered the fall postures by varying the loading direction on the coronal plane (α) and on the transverse plane (β) along with the clinical variables—age, sex, weight, and bone mineral density, and morphological variables—femoral neck axis length, femoral neck width, femoral neck angle, and true moment arm. The strain distribution obtained via finite element analysis (FEA) shows that the angle of adduction (α) during a fall increases the risk of fracture at the greater trochanter and femoral neck, whereas with an increased angle of rotation (β) during the fall, the FRI increases by ∼1.35 folds. The statistical analysis of clinical, morphological, and loading variables (αandβ) delineates that the consideration of only one variable is not enough to realistically predict the possibility of fracture as the correlation between individual variables and FRI is less than 0.1, even though they are shown to be significant (p<0.01). On the contrary, the correlation (R2=0.48) increases as all variables are considered, suggesting the need for considering different variables fork predicting FRI. However, the effect of each variable is different. While loading, clinical, and morphological variables are considered together, the loading direction on transverse plane (β) has high significance, and the anatomical variabilities have no significance.

Fracture Toughness: Bridging the Gap Between Hip Fracture and Fracture Risk Assessment.

This review surveys recent literature related to cortical bone fracture mechanics and its application towards understanding bone fragility and hip fractures. Current clinical tools for hip fracture risk assessment have been shown to be insensitive in some cases of elevated fracture risk leading to the question of what other factors account for fracture risk. The emergence of cortical bone fracture mechanics has thrown light on other factors at the tissue level that are important to bone fracture resistance and therefore assessment of fracture risk. Recent cortical bone fracture toughness studies have shown contributions from the microstructure and composition towards cortical bone fracture resistance. A key component currently overlooked in the clinical evaluation of fracture risk is the importance of the organic phase and water to irreversible deformation mechanisms that enhance the fracture resistance of cortical bone. Despite recent findings, there is an incomplete understanding of which mechanisms lead to the diminished contribution of the organic phase and water to the fracture toughness in aging and bone-degrading diseases. Notably, studies of the fracture resistance of cortical bone from the hip (specifically the femoral neck) are few, and those that exist are mostly consistent with studies of bone tissue from the femoral diaphysis. Cortical bone fracture mechanics highlights that there are multiple determinants of bone quality and therefore fracture risk and its assessment. There is still much more to learn concerning the tissue-level mechanisms of bone fragility. An improved understanding of these mechanisms will allow for the development of better diagnostic tools and therapeutic measures for bone fragility and fracture.

Multi-view information fusion using multi-view variational autoencoder to predict proximal femoral fracture load.

Hip 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. We 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. The 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.

Accuracy of the Fracture Risk Assessment Tool for judging pharmacotherapy initiation for primary osteoporosis.

This study aimed to determine whether the Fracture Risk Assessment Tool (FRAX®) is useful in assessing the criteria for the initiation of pharmacotherapy for primary osteoporosis based on the current diagnostic criteria in Japan. We enrolled 614 patients aged ≥ 40years (average, 77.0years) who were eligible for primary osteoporosis evaluation. Bone mineral density measurements of the lumbar spine, total hip, and femoral neck using ALPHYS LF (FUJIFILM, Tokyo, Japan) and imaging studies involving the lumbar spine were obtained and the FRAX® scores of each patient were calculated with and without the T-score of the femoral neck. The receiver operating characteristic curve analysis method was used to calculate the cut-off FRAX® scores with reference to the criteria for initiating pharmacotherapy for osteoporosis; the accuracies of both FRAX® scores were compared. The FRAX® score calculated with the T-score was more accurate for hip fracture risk assessment [cut-off value 5.5%; the area under the curve (AUC) 0.946] than for major osteoporotic fracture risk assessment (cut-off value 17.0%; AUC 0.924) in judging the criteria (p = 0.001). Conversely, the FRAX® score calculated without the T-score was equally accurate for hip fracture risk assessment (AUC 0.796) and major osteoporotic fracture risk assessment (AUC 0.806) (p = 0.23). The FRAX® score can accurately assess the criteria for initiating pharmacotherapy for primary osteoporosis based on the current Japanese diagnostic criteria, especially when the T-score is used.

Muscle density is an independent risk factor of second hip fracture: a prospective cohort study.

BackgroundPatients with a first hip fracture are at high risk of fracturing their other hip. Despite this, preventive therapy is often not given. Because little is known about specific risk factors of a second hip fracture, we investigated the association with areal bone mineral density (aBMD), muscle size, and density. We also investigated whether muscle parameters predict the risk of a contralateral fracture independently of aBMD.MethodsThree groups were included, one without hip fracture (a subcohort of the China Action on Spine and Hip Status study), one with a first, and one with a second hip fracture. Subjects with fractures were recruited from the longitudinal Chinese Second Hip Fracture Evaluation (CSHFE). Computed tomography scans of CSHFE patients, which were obtained immediately following their first fracture, were used to measure cross‐sectional area and density of the gluteus maximus (G.MaxM) and gluteus medius and minimus (G.Med/MinM) muscles. Computed tomography X‐ray absorptiometry was used to measure aBMD of the contralateral femur. Median follow‐up time to second fracture was 4.5 years. Cox proportional hazards models were used to compute hazard ratios (HR) of second hip fracture risk in subjects with a first hip fracture. Multivariate logistic regressions were used to compare odds ratios (OR) for the risk of a first and second hip fracture.ResultsThree hundred and one participants (68.4 ± 6.1 years, 64% female) without and 302 participants (74.6 ± 9.9 years, 71% female) with a first hip fracture were included in the analysis. Among the latter, 45 (79.2 ± 7.1 years) sustained a second hip fracture. ORs for first hip fracture were significant for aBMD and muscle size and density. ORs for a second fracture were smaller by a factor of 3 to 4 and no longer significant for femoral neck (FN) aBMD. HRs for predicting second hip fracture confirmed the results. G.Med/MinM density (HR, 1.68; CI, 1.20–2.35) and intertrochanter aBMD (HR, 1.62; CI, 1.13–2.31) were the most significant. FN aBMD was not significant. G.Med/MinM density remained significant for predicting second hip fracture after adjustment for FN (HR, 1.66; Cl, 1.18–2.30) or total hip aBMD (HR, 1.50; 95% Cl, 1.04–2.15).ConclusionsDensity of the G.Med/MinM muscle is an aBMD independent predictor of the risk of second hip fracture. Intertrochanteric aBMD is a better predictor of second hip fracture than FN and total hip aBMD. These results may trigger a paradigm shift in the assessment of second hip fracture risk and prevention strategies.

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.

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.

Osteoporosis and Hip-Fracture Risk Assessment Using Different Tools in the First Year After Stroke

Osteoporosis and Hip-Fracture Risk Assessment Using Different Tools in the First Year After Stroke

Variation in fracture risk estimation among East Asian women.

Bone mineral density (BMD) testing and fracture risk calculation help clinicians assess fracture risk and counsel patients. However, predicted fracture risks and outcomes for US East Asian individuals remain understudied. Retrospective cohort study. Using standardized clinical profiles for East Asian women aged 70 years, fracture probabilities were estimated using the US Fracture Risk Assessment Tool (FRAX) version 4.1 and corresponding FRAX tools for East Asian countries. Next, clinical and BMD data from 3785 US Asian women aged 65 to 74 years were used to estimate 10-year hip fracture risk (US-Asian FRAX-v3.1) in comparison with actual observed 10-year hip fracture risk (Kaplan-Meier product limit estimate). For the same patient profile entered in the US-Asian FRAX and country-specific FRAX, the calculated 10-year hip fracture probability varied. Compared with the US-Asian FRAX calculator, the estimate was 2-fold higher using the Taiwan FRAX and Hong Kong FRAX, somewhat higher using the South Korea FRAX and Japan FRAX, and similar using the China FRAX. Among 3785 US Asian women (mean [SD] age, 69 [3] years), 23 experienced a hip fracture during a median follow-up of 6.8 years. Their observed 10-year hip fracture risk was 1.5% (95% CI, 0.8%-2.7%), and their median (interquartile range) predicted fracture probability (US-Asian FRAX-v3.1) was 1.1% (0.6%-2.0%). Country-specific FRAX estimates varied between the United States and East Asian countries. For US Asian women, the US FRAX-predicted hip fracture probabilities were in the lower range of observed risk. Although these findings support the use of the US-Asian FRAX for hip fracture risk assessment in US East Asian women, further studies are needed, including the examination of Asian subgroups.

Multiple Regression Analysis of Hip Fracture Risk Assessment Via Finite Element Analysis

Abstract An accurate assessment of hip fracture risk requires a proper consideration of parameters affecting the fracture. In general, hip fracture is assessed based on bone mineral density (BMD) and load amount, but hip fracture is an outcome of the interaction of the parameters such as loading directions, bone density, which are often disregarded. Assessing the effect of the parameters individually may not correctly reflect the root cause of the hip fracture. Hence, this research aims at analyzing the significance of parameters and their interaction in assessing the fracture risk. A multiple regression analysis has been conducted considering bone density (ρash), different loading directions during sideways fall, represented by load angle (α) about femoral shaft on the coronal plane and angle (β) about femoral neck (FN) axis on the transverse plane as independent parameters and fracture risk index (FRI) as a dependent parameter. The statistical results showing the significant value of 0.7321 for α, and 0.0001 for β and ρash indicate that the effect of loading direction (α) does not have impact on fracture risk in comparison with the loading direction (β) and ρash. Furthermore, the analysis of the interaction of parameters shows that the impact of β on fracture risk may depends more on bone density.

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

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

Hip Fracture Risk Assessment in Elderly and Diabetic Patients: Combining Autonomous Finite Element Analysis and Machine Learning.

Autonomous finite element analyses (AFE) based on CT scans predict the biomechanical response of femurs during stance and sidewise fall positions. We combine AFE with patient data via a machine learning (ML) algorithm to predict the risk of hip fracture. An opportunistic retrospective clinical study of CT scans is presented, aimed at developing a ML algorithm with AFE for hip fracture risk assessment in type 2 diabetic mellitus (T2DM) and non-T2DM patients. Abdominal/pelvis CT scans of patients who experienced a hip fracture within 2 years after an index CT scan were retrieved from a tertiary medical center database. A control group of patients without a known hip fracture for at least 5 years after an index CT scan was retrieved. Scans belonging to patients with/without T2DM were identified from coded diagnoses. All femurs underwent an AFE under three physiological loads. AFE results, patient's age, weight, and height were input to the ML algorithm (support vector machine [SVM]), trained by 80% of the known fracture outcomes, with cross-validation, and verified by the other 20%. In total, 45% of available abdominal/pelvic CT scans were appropriate for AFE (at least 1/4 of the proximal femur was visible in the scan). The AFE success rate in automatically analyzing CT scans was 91%: 836 femurs we successfully analyzed, and the results were processed by the SVM algorithm. A total of 282 T2DM femurs (118 intact and 164 fractured) and 554 non-T2DM (314 intact and 240 fractured) were identified. Among T2DM patients, the outcome was: Sensitivity 92%, Specificity 88% (cross-validation area under the curve [AUC] 0.92) and for the non-T2DM patients: Sensitivity 83%, Specificity 84% (cross-validation AUC 0.84). Combining AFE data with a ML algorithm provides an unprecedented prediction accuracy for the risk of hip fracture in T2DM and non-T2DM populations. The fully autonomous algorithm can be applied as an opportunistic process for hip fracture risk assessment. © 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).

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.

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 1.02mm for the shape and 0.06g/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.

Assessment of femoral neck bone metabolism using 18F-sodium fluoride PET/CT imaging.

Standard of care metabolic bone disease assessment relies on changes to bone quantity, which can only be detected after structural changes occur. To investigate the usefulness of Bone Metabolism Score (BMS), derived from fluorine 18 labeled sodium fluoride (18F-NaF) PET/CT imaging as a biomarker of localized metabolic changes at the femoral neck. In this retrospective study, 139 participants (68 females and 71 males, ages 21-75years) that had undergone 18F-NaF PET/CT were included. BMS was calculated as the ratio of standard uptake value (SUV) in the bone region to that of the total region. Correlations and linear regressions of BMS with age, CT-derived bone mineral density (BMD), body mass index (BMI), height, and weight were conducted. Differences in BMS between women younger and older than the age of 50years were assessed. Inter- and intra-operator reproducibility was evaluated by coefficient of variation (CV) and intra-class correlation coefficient (ICC). Among females, age was negatively correlated with left and right whole BMS (5.61% and 4.90% drop in BMS per decade of life) and left and right cortical BMS (10.50% and 10.09% drop in BMS per decade of life). BMS of women older than 50years was lower than BMS of women younger than 50years (P<.0001). Among males, age was negatively correlated with left and right whole BMS (4.29% and 4.25% drop in BMS per decade of life) and left and right cortical BMS (9.13% and 10.30% drop in BMS per decade of life). BMD was positively correlated with whole (r=0.80, P<.0001) and cortical (r=0.92, P<.0001) BMS. BMS could provide functional insight regarding bone metabolism in the femoral neck to complement bone health status assessed through conventional structural imaging. The methodology described herein could be potentially useful for assessing hip fracture risk in individuals when BMD tests provide borderline determination of bone disease.

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

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. 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. 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. 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.

Secondary Prevention of Osteoporosis Through Assessment of Individual and Multiple Risk Factors.

Introduction:Osteoporosis is a chronic progressive bone disease where the bone tissue resorption exceeds its regenerative capacities. Such a process leads to the reduction of bone mineral density (BMD), and distortion of trabecular microarchitectonics, which creates the basis for an increased fracture risk on a “low trauma” for osteoporosis patients. The notion of low trauma implies a stressor that will not cause a fracture in a healthy person under normal circumstances. BMD is a strong predictor of future fractures. However, many fractures occur in persons with BMD values beyond the defined osteoporosis threshold, and BMD measurement only partially identifies the part of the population with increased fracture risk. Also, it is known that risk factors are influencing the bone mass reduction as predictors of future fractures, and their association may lead to an increased fracture risk irrespective of the bone mass and T-score.Aim:The 10-year individual risk assessment for osteoporotic fracture and the analysis of impact of individual and multiple osteoporosis risk factors on the degree of osteoporotic fracture risk.Methods:The research is a retrospective-prospective study which analyzed 120 patients divided into two groups: 1) asymptomatic patients with known risk factors for osteoporosis in the age group of 40-65 (n=60), 2) asymptomatic patients with known risk factors for osteoporosis in the age group of 65-90 (n=60). FRAX® algorithm was used as a tool for the 10-year hip fracture risk assessment, with prior approval of the Centre for Metabolic Bone Diseases, University of Sheffield from the United Kingdom. Fracture risk assessment was calculated using the online FRAX® calculator. High risk is defined as the hip fracture risk higher than 3% or the risk of a “big” osteoporotic fracture higher than 20%. Results are expressed as mean values with a standard deviation. A comparison between tested patient groups was made applying the student T-test.Results:32% of patients of average age of 65.8±12.6 years are under high hip fracture risk, 28% of patients are under the hip fracture risk higher than 3%, and the risk for 0.03% patients is higher than 20%. Patients with high fracture risk are of advanced age, female, with lower body weight and height values, lower bone mineral density (BMD) and T score values than patients who are not under a high fracture risk. A positive family anamnesis to osteoporosis and fractures, earlier fractures, smoking, rheumatoid arthritis, and use of glucocorticoids are risk factors that are more represented in patients with high fracture risk and osteoporosis. The impact of the majority of individual risk factors for osteoporosis and fracture is moderate, and their joint effect is significant. The contribution of individual risk factors to the overall 10-year fracture risk depends on the type, number and association of risk factors.Conclusion:This research is a contribution to the resolution of polemics among authors, i.e. a dilemma whether persons with multiple clinical risk factors for osteoporosis with T score values beyond the defined threshold for osteoporosis are candidates for therapy with bisphosphonates, and a dilemma whether persons without any clinical risk factors for osteoporosis with T score values within the defined osteoporosis threshold require therapy with bisphosphonates, or only monitoring is sufficient.

Nonliver Comorbidities in Patients With Chronic Hepatitis B.

Nonliver Comorbidities in Patients With Chronic Hepatitis B.