Method and Instrumented Fixture for Femoral Fracture Testing in a Sideways Fall-on-the-Hip Position

Optimizing Accuracy of Proximal Femur Elastic Modulus Equations

Quantitative computed tomography-based finite element analysis (QCT/FEA) is a promising tool to predict femoral properties. One of the modeling parameters required as input for QCT/FEA is the elastic modulus, which varies with the location-dependent bone mineral density (ash density). The aim of this study was to develop optimized equations for the femoral elastic modulus. An inverse QCT/FEA method was employed, using an optimization process to minimize the error between the predicted femoral stiffness values and experimental values. We determined optimal coefficients of an elastic modulus equation that was a function of ash density only, and also optimal coefficients for several other equations that included along with ash density combinations of the variables sex and age. All of the optimized models were found to be more accurate than models from the literature. It was found that the addition of the variables sex and age to ash density made very minor improvements in stiffness predictions compared to the model with ash density alone. Even though the addition of age did not remarkably improve the statistical metrics, the effect of age was reflected in the elastic modulus equations as a decline of about 9% over a 60-year interval.

Factors associated with proximal femur fracture determined in a large cadaveric cohort

Many researchers have used cadaveric fracture tests to determine the relationship between proximal femur (hip) fracture strength and a multitude of possible explanatory variables, typically considered one or two at a time. These variables include subject-specific proximal femur variables such as femoral neck areal bone mineral density (aBMD), sex, age, and geometry, as well as physiological hip fracture event variables such as fall speed and angle of impact. However, to our knowledge, no study has included all of these variables simultaneously in the same experimental dataset. To address this gap, the present study simultaneously included all of these subject-specific and fracture event variables in multivariate models to understand their contributions to femoral strength and fracture type. The primary aim of this study was to determine not only whether each of these variables contributed to the prediction of femoral strength, but also to determine the relative importance of each variable in strength prediction. A secondary aim was to similarly characterize the importance of these variables for the prediction of fracture type. To accomplish these aims, we characterized 197 proximal femurs (covering a wide range of subject-specific variables) with DXA and CT scans, and then tested the femurs to fracture in a sideways fall on the hip configuration. Each femur was tested using one of three fall speed conditions and one of four angles of impact (bone orientations). During each test, we acquired measurements of relevant force and displacement data. We then reduced the test data to determine femoral strength, and we used post-fracture CT scans to classify the fracture type (e.g., trochanteric, cervical). Using these results, the explanatory variables were analyzed with mixed statistical models to explain variations in hip fracture strength and fracture type, respectively. Five explanatory variables were statistically significant in explaining the variability in femoral strength: aBMD, sex, age, fall speed, and neck-shaft angle (P ≤ 0.0135). These five variables, including significant interactions, explained 80% of the variability in hip fracture strength. Additionally, when only aBMD, sex, and age (P < 0.0001) were considered in the model, again including significant interactions, these three variables alone explained 79% of the variability in hip fracture strength. So while fall speed (P = 0.0135) and neck-shaft angle (P = 0.0041) were statistically significant, the inclusion of these variables did not appreciably improve the prediction of hip fracture strength compared to the model that considered only aBMD, sex and age. For the variables we included in this study, in the ranges we considered, our findings indicate that the clinically-available information of patient age, sex and aBMD are sufficient for femoral strength assessment. These findings also suggest that there is little value in the extra effort required to characterize the effect of femoral geometry on strength, or to account for the probabilistic nature of fall-related factors such as fall speed and angle of impact. For fracture type, the only explanatory variable found to be significant was aBMD (P ≤ 0.0099). We found that the odds of having intertrochanteric fractures increased by 47% when aBMD decreased by one standard deviation (0.2 g/cm2).

Are DXA/aBMD and QCT/FEA Stiffness and Strength Estimates Sensitive to Sex and Age?

Dual X-ray absorptiometry (DXA) measures areal bone mineral density (aBMD) by simplifying a complex 3D bone structure to a 2D projection and is not equally effective for explaining fracture strength in women and men. Unlike DXA, subject-specific quantitative computed tomography-based finite element analysis (QCT/FEA) estimates fracture strength using 3D bone mineral distribution and geometry. By using experimentally-measured femoral stiffness and strength from a one hundred sample cadaveric cohort that included variations in sex and age, we wanted to determine if QCT/FEA estimates were able to better predict the experimental variations than DXA/aBMD. For each femur, DXA/aBMD was assessed and a QCT/FEA model was developed to estimate femoral stiffness and strength. Then, the femur was mechanically tested to fracture in a sideways fall on the hip position to measure stiffness and strength. DXA/aBMD and QCT/FEA estimates were compared for their sensitivity to sex and age with multivariate statistical analyses. When comparing the measured data with DXA/aBMD predictions, both age and sex were significant (p ≤ 0.0398) for both femoral stiffness and strength. However, QCT/FEA predictions of stiffness and strength showed sex was insignificant (p ≥ 0.23). Age was still significant (p ≤ 0.0072). These results indicate that QCT/FEA, unlike DXA/aBMD, accounted for bone differences due to sex.