At present, the current gold-standard for osteoporosis diagnosis is based on bone mineral density (BMD) measurement, which, however, has been demonstrated to poorly estimate fracture risk. Further parameters in the hands of the clinicians are represented by the hip structural analysis (HSA) variables, which include geometric information of the proximal femur cross section. The purpose of this study was to investigate the suitability of HSA parameters as additional hip fracture risk predictors. With this aim, twenty-eight three-dimensional patient-specific models of the proximal femur were built from computed tomography (CT) images and a sideways fall condition was reproduced by finite element (FE) analyses. A tensile or compressive predominance based on minimum and maximum principal strains was determined at each volume element and a risk factor (RF) was calculated. The power of HSA variables combinations to predict the maximum superficial RF values was assessed by multivariate linear regression analysis. The optimal regression model, identified through the Akaike information criterion (AIC), only comprises two variables: the buckling ratio (BR) and the neck-shaft angle (NSA). In order to validate the study, the model was tested on two additional patients who suffered a hip fracture after a fall. The results classified the patients in the high risk level, confirming the prediction power of the adopted model.
Prediction of Incident Hip Fracture Risk by Femur Geometry Variables Measured by Hip Structural Analysis in the Study of Osteoporotic Fractures
