Top

Osteoporosis International

Published in:

Open Access 01-09-2016 | Original Article

Patient-specific finite element estimated femur strength as a predictor of the risk of hip fracture: the effect of methodological determinants

Authors: M. Qasim, G. Farinella, J. Zhang, X. Li, L. Yang, R. Eastell, M. Viceconti

Published in: Osteoporosis International | Issue 9/2016

Abstract

Summary

A finite element modelling pipeline was adopted to predict femur strength in a retrospective cohort of 100 women. The effects of the imaging protocol and the meshing technique on the ability of the femur strength to classify the fracture and the control groups were analysed.

Introduction

The clinical standard to estimate the risk of osteoporotic hip fracture is based on the areal bone mineral density (aBMD). A few retrospective studies have concluded that finite element (FE)-based femoral strength is a better classifier of fracture and control groups than the aBMD, while others could not find significant differences. We investigated the effect of the imaging protocol and of the FE modelling techniques on the discriminatory power of femoral strength.

Methods

A retrospective cohort of 100 post-menopausal women (50 with hip fracture, 50 controls) was examined. Each subject received a dual-energy absorptiometry (DXA) exam and a computed tomography (CT) scan of the proximal femur region. Each case was modelled a number of times, using different modelling pipelines, and the results were compared in terms of accuracy in discriminating the fracture and the control cases. The baseline pipeline involved local anatomical orientation and mesh morphing. Revised pipelines involved global anatomical orientation using a full-femur atlas registration and an optimised meshing algorithm. Minimum physiological (MPhyS) and pathological (MPatS) strengths were estimated for each subject. Area under the receiver operating characteristic (ROC) curve (AUC) was calculated to compare the ability of MPhyS, MPatS and aBMD to classify the control and the cases.

Results

Differences in the modelling protocol were found to considerably affect the accuracy of the FE predictors. For the most optimised protocol, logistic regression showed aBMD_Neck, MPhyS and MPatS to be significantly associated with the facture status, with AUC of 0.75, 0.75 and 0.79, respectively.

Conclusion

The study emphasized the necessity of modelling the whole femur anatomy to develop a robust FE-based tool for hip fracture risk assessment. FE-strength performed only slightly better than the aBMD in discriminating the fracture and control cases. Differences between the published studies can be explained in terms of differences in the modelling protocol and cohort design.

Schuit SC, van der Klift M, Weel AE, de Laet CE, Burger H, Seeman E, Hofman A, Uitterlinden AG, van Leeuwen JP, Pols HA (2004) Fracture incidence and association with bone mineral density in elderly men and women: the Rotterdam Study. Bone 34:195–202CrossRefPubMed

Johnell O, Kanis JA, Oden A et al (2005) Predictive value of BMD for hip and other fractures. J Bone Miner Res 20:1185–1194CrossRefPubMed

Holroyd C, Cooper C, Dennison E (2008) Epidemiology of osteoporosis. Best Pract Res Clin Endocrinol Metab 22:671–685CrossRefPubMed

Keyak JH, Kaneko TS, Tehranzadeh J, Skinner HB (2005) Predicting proximal femoral strength using structural engineering models. Clin Orthop Relat Res 219-228

Bessho M, Ohnishi I, Matsuyama J, Matsumoto T, Imai K, Nakamura K (2007) Prediction of strength and strain of the proximal femur by a CT-based finite element method. J Biomech 40:1745–1753CrossRefPubMed

Schileo E, Taddei F, Cristofolini L, Viceconti M (2008) Subject-specific finite element models implementing a maximum principal strain criterion are able to estimate failure risk and fracture location on human femurs tested in vitro. J Biomech 41:356–367CrossRefPubMed

Trabelsi N, Yosibash Z (2011) Patient-specific finite-element analyses of the proximal femur with orthotropic material properties validated by experiments. J Biomech Eng 133:061001CrossRefPubMed

Orwoll ES, Marshall LM, Nielson CM et al (2009) Finite element analysis of the proximal femur and hip fracture risk in older men. J Bone Miner Res 24:475–483CrossRefPubMed

Falcinelli C, Schileo E, Balistreri L et al (2014) Multiple loading conditions analysis can improve the association between finite element bone strength estimates and proximal femur fractures: a preliminary study in elderly women. Bone 67:71–80CrossRefPubMed

10.

Keyak JH, Sigurdsson S, Karlsdottir G et al (2011) Male-female differences in the association between incident hip fracture and proximal femoral strength: a finite element analysis study. Bone 48:1239–1245CrossRefPubMedPubMedCentral

11.

Nishiyama KK, Ito M, Harada A, Boyd SK (2014) Classification of women with and without hip fracture based on quantitative computed tomography and finite element analysis. Osteoporos Int 25:619–626CrossRefPubMed

12.

Grassi L, Hraiech N, Schileo E, Ansaloni M, Rochette M, Viceconti M (2011) Evaluation of the generality and accuracy of a new mesh morphing procedure for the human femur. Med Eng Phys 33:112–120CrossRefPubMed

13.

Yang L, Udall WJ, McCloskey EV, Eastell R (2014) Distribution of bone density and cortical thickness in the proximal femur and their association with hip fracture in postmenopausal women: a quantitative computed tomography study. Osteoporos Int 25:251–263CrossRefPubMed

14.

Kalender WA, Wolf H, Suess C, Gies M, Greess H, Bautz WA (1999) Dose reduction in CT by on-line tube current control: principles and validation on phantoms and cadavers. Eur Radiol 9:323–328CrossRefPubMed

15.

Helgason B, Taddei F, Pálsson H, Schileo E, Cristofolini L, Viceconti M, Brynjólfsson S (2008) A modified method for assigning material properties to FE models of bones. Med Eng Phys 30:444–453CrossRefPubMed

16.

Morgan EF, Bayraktar HH, Keaveny TM (2003) Trabecular bone modulus-density relationships depend on anatomic site. J Biomech 36:897–904CrossRefPubMed

17.

Taddei F, Schileo E, Helgason B, Cristofolini L, Viceconti M (2007) The material mapping strategy influences the accuracy of CT-based finite element models of bones: an evaluation against experimental measurements. Med Eng Phys 29:973–979CrossRefPubMed

18.

Schileo E, Dall’ara E, Taddei F, Malandrino A, Schotkamp T, Baleani M, Viceconti M (2008) An accurate estimation of bone density improves the accuracy of subject-specific finite element models. J Biomech 41:2483–2491CrossRefPubMed

19.

Zhang J, Sorby H, Clement J, Thomas CD L, Hunter P, Nielsen P, Lloyd D, Taylor M, Besier T (2014) The MAP Client: user-friendly musculoskeletal modelling workflows. In: Bello F, Cotin S (eds) Biomedical simulation., pp 182–192

20.

Zhang J, Malcolm D, Hislop-Jambrich J, Thomas CD L, Nielsen P (2014) An anatomical region-based statistical shape model of the human femur. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2:176–185

21.

Zhang J, Malcolm D, Hislop-Jambrich J, Thomas CD L, Nielsen P (2012) Automatic meshing of femur cortical surfaces from clinical CT images. In: Levine JA, Paulsen RR, Zhang Y (eds) Mesh processing in medical image analysis., pp 40–48

22.

Besl PJ, McKay ND (1992) Method for registration of 3-d shapes. International Society for Optics and Photonics., pp 586–606

23.

Bergmann G, Deuretzbacher G, Heller M, Graichen F, Rohlmann A, Strauss J, Duda GN (2001) Hip contact forces and gait patterns from routine activities. J Biomech 34:859–871CrossRefPubMed

24.

Schileo E, Balistreri L, Grassi L, Cristofolini L, Taddei F (2014) To what extent can linear finite element models of human femora predict failure under stance and fall loading configurations? J Biomech 47:3531–3538CrossRefPubMed

25.

Bayraktar HH, Morgan EF, Niebur GL, Morris GE, Wong EK, Keaveny TM (2004) Comparison of the elastic and yield properties of human femoral trabecular and cortical bone tissue. J Biomech 37:27–35CrossRefPubMed

26.

Cummings SR, Bates D, Black DM (2002) Clinical use of bone densitometry: scientific review. JAMA 288:1889–1897CrossRefPubMed

27.

Kanis JA, Johnell O, Oden A, Dawson A, De Laet C, Jonsson B (2001) Ten year probabilities of osteoporotic fractures according to BMD and diagnostic thresholds. Osteoporos Int 12:989–995CrossRefPubMed

28.

Nemes S, Jonasson JM, Genell A, Steineck G (2009) Bias in odds ratios by logistic regression modelling and sample size. BMC Med Res Methodol 9:56CrossRefPubMedPubMedCentral

29.

Sterne JA, Gavaghan D, Egger M (2000) Publication and related bias in meta-analysis: power of statistical tests and prevalence in the literature. J Clin Epidemiol 53:1119–1129CrossRefPubMed

Title: Patient-specific finite element estimated femur strength as a predictor of the risk of hip fracture: the effect of methodological determinants
Authors: M. Qasim
G. Farinella
J. Zhang
X. Li
L. Yang
R. Eastell
M. Viceconti
Publication date: 01-09-2016
Publisher: Springer London
Published in: Osteoporosis International / Issue 9/2016
Print ISSN: 0937-941X
Electronic ISSN: 1433-2965
DOI: https://doi.org/10.1007/s00198-016-3597-4

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Patient-specific finite element estimated femur strength as a predictor of the risk of hip fracture: the effect of methodological determinants

Abstract

Summary

Introduction

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Summary

Introduction

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 9/2016

A retrospective analysis of nonresponse to daily teriparatide treatment

Bone microarchitecture in Rett syndrome and treatment with teriparatide: a case report

Risk of fractures and diabetes medications: a nationwide cohort study

Body mass index, risk of allogeneic red blood cell transfusion, and mortality in elderly patients undergoing hip fracture surgery

Stiffness and strength of bone in osteoporotic patients treated with varying durations of oral bisphosphonates

The effect of prolonged breast-feeding on the development of postmenopausal osteoporosis in population with insufficient calcium intake and vitamin D level