Identification of statistical critical area to discriminate osteoporotic hip fractures in women

Abstract

Osteoporotic hip fracture is a worldwide health problem, but its understanding is still out of reach. Finite Element models are often implemented to study the phenomenon, but the analysis of simulation's results is in discussion. The simple identification of maximum stress or strain might be misleading and only partially related to the development of the fracture. The aim of the present study is to identify regions with statistically significant differences between fractured and control patients using a rigorous methodology based on Random Field Theory. A cohort of 90 osteoporotic female subjects was used: 45 fractured and 45 controls. 3D FE models were built from Dual-energy X-ray Absorptiometry (DXA) acquisitions. The cohort included both neck and trochanteric fractures. Areas with statistical differences were selected through Random Field Theory. The suitability of the selected elements for the discrimination of a fracture event was validated through the area under the curve (AUC) methods, and binary logistic regression with leave-one-out validation. The FE models elements identified in such a way were below 7 % of the total elements. Major Principal Stress in the selected elements showed an AUC up to 0.95. Patients were classified with an accuracy of up to 84.2 %. The methodology explored focused the analysis on specific points. This approach not only allowed for reaching a relevant classification power, but also suggested a specific bone remodeling process, including reduction of variability and interacting behavior between cortical and trabecular bone. In conclusion, a novel approach to finite element model analysis is presented, showing good classification power and extraction of information about bone remodeling in osteoporotic subjects.

Keywords: 3D finite element analysis; Fracture discrimination; Hip fracture; Random Field Theory (RFT); Statistical Parametric Map (SPM).