Literature DB >> 27077380

Prediction of Hip Failure Load: In Vitro Study of 80 Femurs Using Three Imaging Methods and Finite Element Models-The European Fracture Study (EFFECT).

Pierre Pottecher1, Klaus Engelke1, Laure Duchemin1, Oleg Museyko1, Thomas Moser1, David Mitton1, Eric Vicaut1, Judith Adams1, Wafa Skalli1, Jean Denis Laredo1, Valérie Bousson1.   

Abstract

Purpose To evaluate the performance of three imaging methods (radiography, dual-energy x-ray absorptiometry [DXA], and quantitative computed tomography [CT]) and that of a numerical analysis with finite element modeling (FEM) in the prediction of failure load of the proximal femur and to identify the best densitometric or geometric predictors of hip failure load. Materials and Methods Institutional review board approval was obtained. A total of 40 pairs of excised cadaver femurs (mean patient age at time of death, 82 years ± 12 [standard deviation]) were examined with (a) radiography to measure geometric parameters (lengths, angles, and cortical thicknesses), (b) DXA (reference standard) to determine areal bone mineral densities (BMDs), and (c) quantitative CT with dedicated three-dimensional analysis software to determine volumetric BMDs and geometric parameters (neck axis length, cortical thicknesses, volumes, and moments of inertia), and (d) quantitative CT-based FEM to calculate a numerical value of failure load. The 80 femurs were fractured via mechanical testing, with random assignment of one femur from each pair to the single-limb stance configuration (hereafter, stance configuration) and assignment of the paired femur to the sideways fall configuration (hereafter, side configuration). Descriptive statistics, univariate correlations, and stepwise regression models were obtained for each imaging method and for FEM to enable us to predict failure load in both configurations. Results Statistics reported are for stance and side configurations, respectively. For radiography, the strongest correlation with mechanical failure load was obtained by using a geometric parameter combined with a cortical thickness (r(2) = 0.66, P < .001; r(2) = 0.65, P < .001). For DXA, the strongest correlation with mechanical failure load was obtained by using total BMD (r(2) = 0.73, P < .001) and trochanteric BMD (r(2) = 0.80, P < .001). For quantitative CT, in both configurations, the best model combined volumetric BMD and a moment of inertia (r(2) = 0.78, P < .001; r(2) = 0.85, P < .001). FEM explained 87% (P < .001) and 83% (P < .001) of bone strength, respectively. By combining (a) radiography and DXA and (b) quantitative CT and DXA, correlations with mechanical failure load increased to 0.82 (P < .001) and 0.84 (P < .001), respectively, for radiography and DXA and to 0.80 (P < .001) and 0.86 (P < .001) , respectively, for quantitative CT and DXA. Conclusion Quantitative CT-based FEM was the best method with which to predict the experimental failure load; however, combining quantitative CT and DXA yielded a performance as good as that attained with FEM. The quantitative CT DXA combination may be easier to use in fracture prediction, provided standardized software is developed. These findings also highlight the major influence on femoral failure load, particularly in the trochanteric region, of a densitometric parameter combined with a geometric parameter. (©) RSNA, 2016 Online supplemental material is available for this article.

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Year:  2016        PMID: 27077380     DOI: 10.1148/radiol.2016142796

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  13 in total

1.  Cost-effectiveness of Virtual Bone Strength Testing in Osteoporosis Screening Programs for Postmenopausal Women in the United States.

Authors:  Christoph A Agten; Austin J Ramme; Stella Kang; Stephen Honig; Gregory Chang
Journal:  Radiology       Date:  2017-06-14       Impact factor: 11.105

2.  MRI-based assessment of proximal femur strength compared to mechanical testing.

Authors:  Chamith S Rajapakse; Alexander R Farid; Daniel C Kargilis; Brandon C Jones; Jae S Lee; Alyssa J Johncola; Alexandra S Batzdorf; Snehal S Shetye; Michael W Hast; Gregory Chang
Journal:  Bone       Date:  2020-01-09       Impact factor: 4.398

Review 3.  On challenges in clinical assessment of hip fracture risk using image-based biomechanical modelling: a critical review.

Authors:  Yunhua Luo
Journal:  J Bone Miner Metab       Date:  2021-01-09       Impact factor: 2.626

Review 4.  Physical Activity for Strengthening Fracture Prone Regions of the Proximal Femur.

Authors:  Robyn K Fuchs; Mariana E Kersh; Julio Carballido-Gamio; William R Thompson; Joyce H Keyak; Stuart J Warden
Journal:  Curr Osteoporos Rep       Date:  2017-02       Impact factor: 5.096

5.  Method and Instrumented Fixture for Femoral Fracture Testing in a Sideways Fall-on-the-Hip Position.

Authors:  Dan Dragomir-Daescu; Asghar Rezaei; Timothy Rossman; Susheil Uthamaraj; Rachel Entwistle; Sean McEligot; Vincent Lambert; Hugo Giambini; Iwona Jasiuk; Michael J Yaszemski; Lichun Lu
Journal:  J Vis Exp       Date:  2017-08-17       Impact factor: 1.355

6.  Hip load capacity and yield load in men and women of all ages.

Authors:  J H Keyak; T S Kaneko; S Khosla; S Amin; E J Atkinson; T F Lang; J D Sibonga
Journal:  Bone       Date:  2020-03-14       Impact factor: 4.398

7.  Perspectives on the non-invasive evaluation of femoral strength in the assessment of hip fracture risk.

Authors:  M L Bouxsein; P Zysset; C C Glüer; M McClung; E Biver; D D Pierroz; S L Ferrari
Journal:  Osteoporos Int       Date:  2020-01-03       Impact factor: 4.507

8.  Childhood growth predicts higher bone mass and greater bone area in early old age: findings among a subgroup of women from the Helsinki Birth Cohort Study.

Authors:  T M Mikkola; M B von Bonsdorff; C Osmond; M K Salonen; E Kajantie; C Cooper; M J Välimäki; J G Eriksson
Journal:  Osteoporos Int       Date:  2017-04-25       Impact factor: 4.507

9.  Non-invasive prediction of the mouse tibia mechanical properties from microCT images: comparison between different finite element models.

Authors:  S Oliviero; M Roberts; R Owen; G C Reilly; I Bellantuono; E Dall'Ara
Journal:  Biomech Model Mechanobiol       Date:  2021-02-01

Review 10.  Are CT-Based Finite Element Model Predictions of Femoral Bone Strength Clinically Useful?

Authors:  Marco Viceconti; Muhammad Qasim; Pinaki Bhattacharya; Xinshan Li
Journal:  Curr Osteoporos Rep       Date:  2018-06       Impact factor: 5.096

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