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. 2024 May;35(5):785-794.
doi: 10.1007/s00198-024-07015-6. Epub 2024 Jan 22.

A new hip fracture risk index derived from FEA-computed proximal femur fracture loads and energies-to-failure

Xuewei Cao #  1 , Joyce H Keyak #  2 , Sigurdur Sigurdsson  3 , Chen Zhao  4 , Weihua Zhou  4 , Anqi Liu  5 , Thomas F Lang  6 , Hong-Wen Deng  5 , Vilmundur Gudnason  7   8 , Qiuying Sha  9
Affiliations

A new hip fracture risk index derived from FEA-computed proximal femur fracture loads and energies-to-failure

Xuewei Cao et al. Osteoporos Int. 2024 May.

Abstract

Hip fracture risk assessment is an important but challenging task. Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur. We developed a global FEA-computed fracture risk index to increase the prediction accuracy of hip fracture incidence.

Purpose: Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur to compute the force (fracture load) and energy necessary to break the proximal femur in a particular loading condition. The fracture loads and energies-to-failure are individually associated with incident hip fracture, and provide different structural information about the proximal femur.

Methods: We used principal component analysis (PCA) to develop a global FEA-computed fracture risk index that incorporates the FEA-computed yield and ultimate failure loads and energies-to-failure in four loading conditions of 110 hip fracture subjects and 235 age- and sex-matched control subjects from the AGES-Reykjavik study. Using a logistic regression model, we compared the prediction performance for hip fracture based on the stratified resampling.

Results: We referred the first principal component (PC1) of the FE parameters as the global FEA-computed fracture risk index, which was the significant predictor of hip fracture (p-value < 0.001). The area under the receiver operating characteristic curve (AUC) using PC1 (0.776) was higher than that using all FE parameters combined (0.737) in the males (p-value < 0.001).

Conclusions: The global FEA-computed fracture risk index increased hip fracture risk prediction accuracy in males.

Keywords: Bone strength; Finite element analysis; Hip fracture risk; Osteoporosis; Principal component analysis.

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Conflict of interest statement

Conflicts of interest None.

Figures

Fig. 1
Fig. 1
The predictive performance of logistic and PLS based on stratified resampling. The values over the lines indicated the p-values obtained from the one-sided Student’s t-test. The values on top of the boxes indicated the average AUCs by using PC1 along with aBMDCT and covariates (PC1 + aBMDCT + Cov) versus using aBMDCT and covariates (aBMDCT + Cov) or FE parameters combined with aBMDCT and covariates (FE combined + aBMDCT + Cov). Notes: The abbreviations of FE parameters are yield strength (force at onset of fracture) calculated during single-limb stance (Sy) and impact from a fall onto the posterior (Py), posterolateral (PLy), and lateral (Ly) aspects of the greater trochanter; ultimate strength (failure load capacity) calculated during single-limb stance (Su) and impact from a fall onto the posterior (Pu), posterolateral (PLu), and lateral (Lu) aspects of the greater trochanter; energy-to-failure calculated during single-limb stance (Senergy) and impact from a fall onto the posterior (Penergy), posterolateral (PLenergy), and lateral (Lenergy) aspects of the greater trochanter
Fig. 2
Fig. 2
The predictive performance of PLS using each FE parameters compared with that of using PC1. The values over the lines indicated the p-values were obtained from the one-sided Student’s t-test. The values on top of the boxes indicated the average AUCs by using PC1 along with aBMDCT and covariates (PC1 + aBMDCT + Cov) versus using each of FE parameters along with aBMDCT and covariates (* + aBMDCT + Cov)
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