Prediction of hip fracture can be significantly improved by a single biomedical indicator.

Femoral neck fractures are a relevant clinical and social problem. The aim of this study was to improve the prediction of patients at-risk of femoral neck fracture with respect to the current densitometric-based methods. In particular, finite element models were used to assess the prediction accuracy obtained by combining together data from the bone density distribution, the proximal femur anatomy, and the fall-related loading conditions. Two-dimensional finite element models were developed based on dual energy x-ray absorptiometry data. A population of 93 elder Caucasian women (half of them reporting a femoral neck fracture) were retrospectively classified both using the standard clinical protocol and Bayes' linear classifiers. This study showed that the bone mineral density in the femoral neck region dominated the fracture event (65% accuracy). Adding the subject's height and the neck-shaft angle to the bone density increased the accuracy to 77%. The classification accuracy was further improved to 82% by including the peak principal tensile strain obtained from the finite element analyses. This research demonstrated that adding one single biomechanical indicator to the standard clinical measurements improves the identification of patients at-risk of femoral neck fracture.