Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Article
  • Published:
Osteoporosis International Aims and scope Submit manuscript

Abstract

Summary

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Data Availability

The datasets analyzed during the current study are not publicly available. Requests for access to these datasets should be directed to the corresponding authors.

References

  1. Richards JB, Zheng H-F, Spector TD (2012) Genetics of osteoporosis from genome-wide association studies: advances and challenges. Nat Rev Genet 13:576–588

    Article  CAS  PubMed  Google Scholar 

  2. Recker R, Kimmel D (1991) Changes in trabecular microstructure in osteoporosis occur with normal bone remodeling dynamics. J Bone Miner Res 6:S225

    Google Scholar 

  3. Yang T-L, Chen X-D, Guo Y, Lei S-F, Wang J-T, Zhou Q, Pan F, Chen Y, Zhang Z-X, Dong S-S (2008) Genome-wide copy-number-variation study identified a susceptibility gene, UGT2B17, for osteoporosis. The American Journal of Human Genetics 83:663–674

    Article  CAS  PubMed  Google Scholar 

  4. Genant H, Engelke K, Prevrhal S (2008) Advanced CT bone imaging in osteoporosis. Rheumatology 47:iv9–iv16

    Article  PubMed  PubMed Central  Google Scholar 

  5. Chang G, Honig S, Brown R, Deniz CM, Egol KA, Babb JS, Regatte RR, Rajapakse CS (2014) Finite element analysis applied to 3-T MR imaging of proximal femur microarchitecture: lower bone strength in patients with fragility fractures compared with control subjects. Radiology 272:464

    Article  PubMed  Google Scholar 

  6. Carballido-Gamio J, Bonaretti S, Saeed I, Harnish R, Recker R, Burghardt AJ, Keyak JH, Harris T, Khosla S, Lang TF (2015) Automatic multi-parametric quantification of the proximal femur with quantitative computed tomography. Quant Imaging Med Surg 5:552

    PubMed  PubMed Central  Google Scholar 

  7. Keyak J, Koyama A, LeBlanc A, Lu Y, Lang T (2009) Reduction in proximal femoral strength due to long-duration spaceflight. Bone 44:449–453

    Article  CAS  PubMed  Google Scholar 

  8. Sibonga J, Spector E, Keyak J, Zwart S, Smith S, Lang T (2020) Use of quantitative computed tomography to assess for clinically-relevant skeletal effects of prolonged spaceflight on astronaut hips. J Clin Densitom 23(2):155–164

    Article  PubMed  Google Scholar 

  9. Keyak J, Sigurdsson S, Karlsdottir G, Oskarsdottir D, Sigmarsdottir A, Zhao S, Kornak J, Harris T, Sigurdsson G, Jonsson B (2011) Male–female differences in the association between incident hip fracture and proximal femoral strength: a finite element analysis study. Bone 48:1239–1245

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Orwoll ES, Marshall LM, Nielson CM, Cummings SR, Lapidus J, Cauley JA, Ensrud K, Lane N, Hoffmann PR, Kopperdahl DL (2009) Finite element analysis of the proximal femur and hip fracture risk in older men. J Bone Miner Res 24:475–483

    Article  PubMed  Google Scholar 

  11. Keyak J, Sigurdsson S, Karlsdottir G, Oskarsdottir D, Sigmarsdottir A, Kornak J, Harris T, Sigurdsson G, Jonsson B, Siggeirsdottir K (2013) Effect of finite element model loading condition on fracture risk assessment in men and women: the AGES-Reykjavik study. Bone 57:18–29

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Jolliffe IT, Cadima J (2016) Principal component analysis: a review and recent developments. Phil Trans R Soc A 374:20150202

    Article  PubMed  PubMed Central  Google Scholar 

  13. Sarker IH (2021) Machine learning: algorithms, real-world applications and research directions. SN Computer Science 2:1–21

    Article  Google Scholar 

  14. Harris TB, Launer LJ, Eiriksdottir G, Kjartansson O, Jonsson PV, Sigurdsson G, Thorgeirsson G, Aspelund T, Garcia ME, Cotch MF (2007) Age, gene/environment susceptibility–Reykjavik study: multidisciplinary applied phenomics. Am J Epidemiol 165:1076–1087

    Article  PubMed  Google Scholar 

  15. Keyak J, Kaneko T, Khosla S, Amin S, Atkinson E, Lang T, Sibonga J (2020) Hip load capacity and yield load in men and women of all ages. Bone 137:115321

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Keyak JH, Kaneko TS, Tehranzadeh J, Skinner HB (2005) Predicting proximal femoral strength using structural engineering models. Clin Orthop Relat Res 1976–2007(437):219–228

    Article  Google Scholar 

  17. Kaneko T, Bell J, Pejcic M, Tehranzadeh J, Keyak J (2004) Mechanical properties, density and quantitative CT scan data of trabecular bone with and without metastases. J Biomech 37(4):523–530

    Article  PubMed  Google Scholar 

  18. Kaneko T, Pejcic M, Tehranzadeh J, Keyak J (2003) Relationships between material properties and CT scan data of cortical bone with and without metastatic lesions. Med Eng Phys 25(6):445–454

    Article  PubMed  Google Scholar 

  19. Keyak JH, Rossi SA, Jones KA, Skinner HB (1997) Prediction of femoral fracture load using automated finite element modeling. J Biomech 31:125–133

    Article  Google Scholar 

  20. Keyak JH (2001) Improved prediction of proximal femoral fracture load using nonlinear finite element models. Med Eng Phys 23:165–173

    Article  CAS  PubMed  Google Scholar 

  21. Kuhn M, Johnson K (2013) Applied predictive modeling. Springer

    Book  Google Scholar 

  22. Marshall D, Johnell O, Wedel H (1996) Meta-analysis of how well measures of bone mineral density predict occurrence of osteoporotic fractures. BMJ 312:1254–1259

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. LaCroix A, Beck T, Cauley J, Lewis C, Bassford T, Jackson R, Wu G, Chen Z (2010) Hip structural geometry and incidence of hip fracture in postmenopausal women: what does it add to conventional bone mineral density? Osteoporos Int 21:919–929

    Article  CAS  PubMed  Google Scholar 

  24. Leslie W, Luo Y, Yang S, Goertzen A, Ahmed S, Delubac I, Lix L (2019) Fracture risk indices from DXA-based finite element analysis predict incident fractures independently from FRAX: the Manitoba BMD Registry. J Clin Densitom 22(3):338–345

    Article  PubMed  Google Scholar 

  25. Leslie W, Lix LM, Morin S, Johansson H, Odén A, McCloskey E, Kanis J (2015) Hip axis length is a FRAX- and bone density-independent risk factor for hip fracture in women. J Clin Endocrinol Metab 100(5):2063–2070

    Article  CAS  PubMed  Google Scholar 

  26. Cody DD, Gross GJ, Hou FJ, Spencer HJ, Goldstein SA, Fyhrie DP (1999) Femoral strength is better predicted by finite element models than QCT and DXA. J Biomech 32:1013–1020

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The study was approved by the Icelandic National Bioethics Committee (VSN: 00-063) and the Data Protection Authority. The researchers are indebted to the participants for their willingness to participate in the study.

Funding

This study was supported by NIH/NIA R01AG028832 and NIH/NIAMS R01AR46197. The Age, Gene/Environment Susceptibility Reykjavik Study is funded by NIH contract N01-AG-12100, the NIA Intramural Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament). H-WD was partially supported by U19 AG055373 and R01 AR069055. XC was funded by the Michigan Technological University Health Research Institute Fellowship program and the Portage Health Foundation Graduate Assistantship.

Author information

Author notes

  1. Xuewei Cao and Joyce H. Keyak contributed equally.

Authors and Affiliations

  1. Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, 49931, USA

    Xuewei Cao & Qiuying Sha

  2. Department of Radiological Sciences, Department of Biomedical Engineering, and Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA

    Joyce H. Keyak

  3. Icelandic Heart Association Research Institute, Kopavogur, Iceland

    Sigurdur Sigurdsson & Vilmundur Gudnason

  4. Department of Applied Computing, Michigan Technological University, Houghton, MI, USA

    Chen Zhao & Weihua Zhou

  5. Center for Bioinformatics and Genomics, School of Medicine, Tulane University, New Orleans, LA, USA

    Anqi Liu & Hong-Wen Deng

  6. Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA

    Thomas F. Lang

  7. University of Iceland, Reykjavik, Iceland

    Vilmundur Gudnason

Authors
  1. Xuewei Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joyce H. Keyak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sigurdur Sigurdsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chen Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Weihua Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Anqi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Thomas F. Lang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hong-Wen Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Vilmundur Gudnason
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Qiuying Sha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Vilmundur Gudnason or Qiuying Sha.

Ethics declarations

Conflicts of interest

None.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cao, X., Keyak, J.H., Sigurdsson, S. et al. A new hip fracture risk index derived from FEA-computed proximal femur fracture loads and energies-to-failure. Osteoporos Int 35, 785–794 (2024). https://doi.org/10.1007/s00198-024-07015-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00198-024-07015-6

Keywords

Search

Navigation