Osteoporotic Hip Fracture Prediction: Is T-Score-Based Criterion Enough? A Hip Structural Analysis-Based Model

Area of Science:

• Biomedical Engineering
• Orthopedics
• Radiology

Background:

• Current osteoporosis diagnosis relies on bone mineral density (BMD), which has limited accuracy in predicting fracture risk.
• Hip structural analysis (HSA) variables offer geometric insights into the proximal femur, potentially enhancing fracture prediction.

Purpose of the Study:

• To evaluate the effectiveness of HSA parameters as supplementary predictors of hip fracture risk.
• To determine if HSA variables can improve upon BMD-based fracture risk assessment.

Main Methods:

• Creation of 28 patient-specific 3D models of the proximal femur using CT images.
• Simulation of sideways falls using finite element (FE) analysis to calculate a risk factor (RF) based on principal strains.
• Multivariate linear regression analysis to assess the predictive power of HSA variable combinations, optimized using the Akaike information criterion (AIC).

Main Results:

• The optimal regression model identified two key HSA variables: buckling ratio (BR) and neck-shaft angle (NSA).
• Validation on two additional patients who experienced hip fractures confirmed the model's ability to classify them as high-risk.
• The combination of BR and NSA demonstrated significant predictive power for hip fracture risk.

Conclusions:

• HSA parameters, specifically BR and NSA, are valuable additions to fracture risk assessment in osteoporosis.
• These geometric variables enhance the prediction of hip fracture risk beyond traditional BMD measurements.
• The developed FE analysis model shows promise for clinical application in identifying at-risk individuals.