Assessing forearm fracture risk in postmenopausal women

Melton, L. Joseph; Christen, D. K.; Riggs, B. Lawrence; Achenbach, Sara J.; Müller, Ralph; Lenthe, G. Harry van; Amin, Shreyasee; Atkinson, Elizabeth J.; Khosla, Sundeep

doi:10.1007/s00198-009-1047-2

Cited by 104 publications

(96 citation statements)

References 47 publications

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“…In a matched case-control study, significantly lower volumetric BMD values were found in patients with fractures (23). In a recent study on forearm fracture risk and a second study on fracture prediction in postmenopausal women, the discriminatory power of HR-pQCT was similar as for patients with preterminal renal failure (24,25). A remarkable difference between these studies and the data presented here is that the heterogeneity of trabecular structures was markedly higher in dialysis patients with fracture history compared with dialysis patients without a history of fracture, especially in women.…”

Section: Discussionmentioning

confidence: 89%

Bone Microarchitecture in Hemodialysis Patients Assessed by HR-pQCT

Cejka

Patsch²,

Weber³

et al. 2011

Clinical Journal of the American Society of Nephrology

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SummaryBackground and objectives Dialysis patients are at high risk for low-trauma bone fracture. Bone density measurements using dual-energy x-ray absorptiometry (DXA) do not reliably differentiate between patients with and without fractures. The aim of this study was to identify differences in bone microarchitecture between patients with and without a history of fracture using high-resolution peripheral quantitative computed tomography (HR-pQCT).Design, setting, participants, & measurements Seventy-four prevalent hemodialysis patients were recruited for measurements of areal bone mineral density (aBMD) by DXA and bone microarchitecture by HR-pQCT. Patients with a history of trauma-related fracture were excluded. Forty healthy volunteers served as controls. Blood levels of parathyroid hormone, vitamin D, and markers of bone turnover were determined.Results Dialysis patients, particularly women, had markedly impaired bone microarchitecture. Patients with fractures had significantly reduced cortical and trabecular microarchitecture compared with patients without fractures. aBMD tended to be lower in patients with fractures, but differences were statistically not significant. The strongest determinant of fracture was the HR-pQCT-measured trabecular density of the tibia, which also had the highest discriminatory power to differentiate patients according to fracture status. Radial DXA had a lower discriminatory power than trabecular density. ConclusionsBone microarchitecture is severely impaired in dialysis patients and even more so in patients with a history of fracture. HR-pQCT can identify dialysis patients with a history of low-trauma fracture.

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Section: Discussionmentioning

confidence: 89%

Bone Microarchitecture in Hemodialysis Patients Assessed by HR-pQCT

Cejka

Patsch²,

Weber³

et al. 2011

Clinical Journal of the American Society of Nephrology

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“…In contrast, owing to bone's capability of adapting its mass to mechanical loading, a significant difference in the second set is expected. The data were collected from a prior study [38] of 100 postmenopausal women with a distal forearm fracture who were compared with 105 age-matched control women without fracture. The Mayo Clinic's Institutional Review Board approved the study, and written informed consent was obtained from all subjects; the present analysis was based on de-identified data.…”

Section: Sensitivitymentioning

confidence: 99%

Voxel size dependency, reproducibility and sensitivity of an in vivo bone loading estimation algorithm

Christen

Schulte

Zwahlen

et al. 2016

J. R. Soc. Interface.

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ResearchCite this article: Christen P et al. 2016 Voxel size dependency, reproducibility and sensitivity of an in vivo bone loading estimation algorithm. J. R. Soc. A bone loading estimation algorithm was previously developed that provides in vivo loading conditions required for in vivo bone remodelling simulations. The algorithm derives a bone's loading history from its microstructure as assessed by high-resolution (HR) computed tomography (CT). This reverse engineering approach showed accurate and realistic results based on micro-CT and HR-peripheral quantitative CT images. However, its voxel size dependency, reproducibility and sensitivity still need to be investigated, which is the purpose of this study. Voxel size dependency was tested on cadaveric distal radii with micro-CT images scanned at 25 mm and downscaled to 50, 61, 75, 82, 100, 125 and 150 mm. Reproducibility was calculated with repeated in vitro as well as in vivo HR-pQCT measurements at 82 mm. Sensitivity was defined using HR-pQCT images from women with fracture versus non-fracture, and low versus high bone volume fraction, expecting similar and different loading histories, respectively. Our results indicate that the algorithm is voxel size independent within an average (maximum) error of 8.2% (32.9%) at 61 mm, but that the dependency increases considerably at voxel sizes bigger than 82 mm. In vitro and in vivo reproducibility are up to 4.5% and 10.2%, respectively, which is comparable to other in vitro studies and slightly higher than in other in vivo studies. Subjects with different bone volume fraction were clearly distinguished but not subjects with and without fracture. This is in agreement with bone adapting to customary loading but not to fall loads. We conclude that the in vivo bone loading estimation algorithm provides reproducible, sensitive and fairly voxel size independent results at up to 82 mm, but that smaller voxel sizes would be advantageous.

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“…Bone microarchitecture parameters measured by HR-pQCT have been found to be associated with prevalent fracture in postmenopausal women and older men independently of areal bone mineral density (aBMD) [1][2][3][4][5]. Bone strength, estimated using micro-finite element analysis (μFEA), was also associated with prevalent fracture [6][7][8][9][10][11][12][13][14]. Moreover, HR-pQCT has been used to assess age-related bone loss [15][16][17][18][19] and to monitor variations in microarchitecture parameters during osteoporosis treatments [20][21][22][23][24][25][26].…”

Section: T R a C T A R T I C L E I N F O Introductionmentioning

confidence: 99%