  Hip fracture risk assessment is an important but challenging task.
Quantitative CT-based patient specific finite element analysis (FEA) computes
the force (fracture load) to break the proximal femur in a particular loading
condition. It provides different structural information about the proximal
femur that can influence a subject overall fracture risk. To obtain a more
robust measure of fracture risk, we used principal component analysis (PCA) to
develop a global FEA computed fracture risk index that incorporates the
FEA-computed yield and ultimate failure loads and energies to failure in four
loading conditions (single-limb stance and impact from a fall onto the
posterior, posterolateral, and lateral aspects of the greater trochanter) of
110 hip fracture subjects and 235 age and sex matched control subjects from the
AGES-Reykjavik study. We found that the first PC (PC1) of the FE parameters was
the only significant predictor of hip fracture. Using a logistic regression
model, we determined if prediction performance for hip fracture using PC1
differed from that using FE parameters combined by stratified random resampling
with respect to hip fracture status. The results showed that the average of the
area under the receive operating characteristic curve (AUC) using PC1 was
always higher than that using all FE parameters combined in the male subjects.
The AUC of PC1 and AUC of the FE parameters combined were not significantly
different than that in the female subjects or in all subjects
