Enhancing Hip Fracture Risk Prediction in Older Adults by Accounting for the Femur’s Shape and Bone Mineral Density Distribution

Description

In this presentation, Fatemeh Jazinizadeh discusses her research focused on improving the prediction of hip fracture risk in older adults by considering the shape of the femur and the distribution of bone mineral density (BMD). She highlights that as people age, bones become more porous, leading to conditions such as osteoporosis, which significantly increase the likelihood of fragility fractures, particularly in the hip. Fatemeh emphasizes the importance of accurate prediction tools to allow for early diagnosis and preventive measures to mitigate risk and impact on mobility and mortality.



The current standard practice for diagnosing osteoporosis involves DXA imaging, which assesses average BMD but fails to account for variations in bone geometry and density distribution. Fatemeh suggests that enhancing hip fracture risk prediction can be achieved through advanced modeling techniques. She proposes the use of two-dimensional statistical shape and appearance modeling (2D SSAM) to analyze and categorize femoral shape data and BMD distribution, minimizing dimensionality through principal component analysis and utilizing logistic regression to evaluate fracture risks.



The study collaborates with the Canadian multicenter osteoporosis study, analyzing data from 192 subjects who had their DXA scans taken at baseline and followed for at least five years. Preliminary findings indicate that the new method can identify high-risk patients with double the accuracy compared to existing methods, showcasing potential for predicting both fracture risk and imminent fractures. Fatemeh concludes by expressing her hope that implementing this technique in clinical settings will enhance patient outcomes and quality of life by preventing painful fractures.

DOI: 10.1302/3114-220768

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