Cross-Calibration of Prodigy and Horizon A Densitometers and Precision of the Horizon A Densitometer

Reitshamer, Elise; Barrett, Kelsey; Shea, Kyla; Dawson‐Hughes, Bess

doi:10.1016/j.jocd.2021.02.003

Cited by 15 publications

(11 citation statements)

References 18 publications

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“…We found that the precision error of TBS measures decreased with increasing age, and by adolescence it approached that found in older adults on Hologic machines, 2.05% versus 1.21% to 1.9%. ( 27‐29 ) Notably, the %CV for TBS was two‐ to threefold greater than for BMAD and aBMD. Some studies in adults also found the %CV of TBS to be 1.3‐ to 2.4‐fold greater than for BMD measured with Hologic machines, ( 14,27,28 ) whereas others have reported roughly similar precision for TBS and BMD measured with GE Lunar machines.…”

Section: Discussionmentioning

confidence: 99%

“…( 27‐29 ) Notably, the %CV for TBS was two‐ to threefold greater than for BMAD and aBMD. Some studies in adults also found the %CV of TBS to be 1.3‐ to 2.4‐fold greater than for BMD measured with Hologic machines, ( 14,27,28 ) whereas others have reported roughly similar precision for TBS and BMD measured with GE Lunar machines. ( 30 ) We illustrated the effect of precision error on interpretation of TBS results by incorporating the RMSE to select TBS values and seeing how the Z ‐score changed.…”

Section: Discussionmentioning

confidence: 99%

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Trabecular Bone Score Reference Values for Children and Adolescents According to Age, Sex, and Ancestry

Kalkwarf

Shepherd

Hans

et al. 2020

Journal of Bone and Mineral Research

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Trabecular bone score (TBS) is used for fracture prediction in adults, but its utility in children is limited by absence of appropriate reference values. We aimed to develop reference ranges for TBS by age, sex, and population ancestry for youth ages 5 to 20 years. We also investigated the association between height, body mass index (BMI), and TBS, agreement between TBS and lumbar spine areal bone mineral density (aBMD) and bone mineral apparent density (BMAD) Z‐scores, tracking of TBS Z‐scores over time, and precision of TBS measurements. We performed secondary analysis of spine dual‐energy X‐ray absorptiometry (DXA) scans from the Bone Mineral Density in Childhood Study (BMDCS), a mixed longitudinal cohort of healthy children (n = 2014) evaluated at five US centers. TBS was derived using a dedicated TBS algorithm accounting for tissue thickness rather than BMI. TBS increased only during ages corresponding to pubertal development with an earlier increase in females than males. There were no differences in TBS between African Americans and non‐African Americans. We provide sex‐specific TBS reference ranges and LMS values for calculation of TBS Z‐scores by age and means and SD for calculation of Z‐scores by pubertal stage. TBS Z‐scores were positively associated with height Z‐scores at some ages. TBS Z‐scores explained only 27% and 17% of the variance of spine aBMD and BMAD Z‐scores. Tracking of TBS Z‐scores over 6 years was lower (r = 0.47) than for aBMD or BMAD Z‐scores (r = 0.74 to 0.79), and precision error of TBS (2.87%) was greater than for aBMD (0.85%) and BMAD (1.22%). In sum, TBS Z‐scores provide information distinct from spine aBMD and BMAD Z‐scores. Our robust reference ranges for TBS in a well‐characterized pediatric cohort and precision error estimates provide essential tools for clinical assessment using TBS and determination of its value in predicting bone fragility in childhood and adolescence. © 2022 American Society for Bone and Mineral Research (ASBMR).

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Trabecular Bone Score Reference Values for Children and Adolescents According to Age, Sex, and Ancestry

Kalkwarf

Shepherd

Hans

et al. 2020

Journal of Bone and Mineral Research

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“…Importantly, participants were representative of the populations usually imaged at these two research facilities meaning that the corrections should be applicable in our context. While studies have been published detailing the cross-calibration of different modalities in hospital or research settings, to date these have been from high income settings [4,8,13,14,[19][20][21][22][23][24][25][26]. To the best of our knowledge, this represents the first such study in sub-Saharan Africa, where research sites with dedicated bone imaging facilities are few, and hospitals have limited access to standard clinical modalities such as DXA.…”

Section: Discussionmentioning

confidence: 99%

Cross-Calibration of iDXA and pQCT Scanners at Rural and Urban Research Sites in The Gambia, West Africa

et al. 2023

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Between-scanner differences in measures of bone and body composition can obscure or exaggerate physiological differences in multi-site studies or the magnitude of changes in longitudinal studies. We conducted a cross-calibration study at two bone imaging centres in The Gambia, West Africa where DXA (dual-energy X-ray absorptiometry) and pQCT (peripheral Quantitative-Computed Tomography) are routinely used. Repeat scans were obtained from 64 Gambian adults (58% Male) aged Mean(SD) 30.9 (13.5) years with Mean(SD) body mass index (BMI) 21.7 (4.0) kg/m2, using DXA (GE Lunar iDXA, whole body [WB], total hip [TH], lumbar spine [LS]) and pQCT (Stratec XCT2000L/XCT2000, tibia 4%, 50% sites). Between-scanner differences were tested using paired t tests (p < 0.05). Between-scanner correlation was explored with linear regression, and cross-calibration equations derived. Bland–Altman analysis investigated machine trend/bias. When differences were detected (p < 0.05), cross-calibration equations were applied to urban values, with t tests and Bland Altman analysis repeated. Between-scanner differences exceeded the predefined level of statistical significance (p < 0.05) for WB aBMD and BA; all pQCT measures vBMD, BMC, cortical cross-sectional area (CSA) and stress–strain index (SSI). Between-scanner correlation was high (R2:0.92–0.99), except pQCT Mu.Den (R2 = 0.51). Bland Altman plots indicated bias increased with increasing BMD. Cross-calibration equations attenuated all between-scanner differences and systematic bias. Cross-calibration, particularly of pQCT scanners, is an important consideration in multi-site studies particularly where between population comparisons are intended. Our experiences and findings may be generalisable to other resource-limited settings where the logistics of sourcing parts and in-country repair may result in lengthy scanner downtime.

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“…For one of the studies, 41 DXA scanning instrumentation was manufactured by Hologic, Inc. For the other 2 studies, 42,43 Lunar scanning instrumentation from GE HealthCare was used. Because the 2 instrument sources do not yield identical results, [44][45][46] quantitative readings obtained from the Lunar instrument were converted to the equivalent Hologic readings based on studies in which DXA scanning was performed on the same individuals using both Lunar and Hologic instrumentation. 44 Using the regression coefficients generated from the above analysis of the NHANES 2003-2006 group, the independent anthropometric variables in the ELS subjects were used to calculate PTBF for the individual ELS participants.…”

Section: Methodsmentioning

confidence: 99%

“…As described above, quantitative readings obtained from the Lunar instrument were converted to the equivalent Hologic readings. 44 Predicted TBF for the individual subjects was calculated using the independent variable values (age, height, weight, waist circumference) together with the regression coefficients generated from the above analysis of the NHANES 2003-2006 group.…”

Section: Validation Data Set: the Stop-it Studymentioning

confidence: 99%

Estimation of Absolute and Relative Body Fat Content Using Noninvasive Surrogates: Can DXA Be Bypassed?

Greenblatt,

Bruno,

Harmatz

et al. 2023

The Journal of Clinical Pharma

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Dual‐energy x‐ray absorptiometry (DXA) scanning is used for objective determination of body composition, but instrumentation is expensive and not generally available in customary clinical practice. Anthropometric surrogates are often substituted as anticipated correlates of absolute and relative body fat content in the clinical management of obesity and its associated medical risks. DXA and anthropometric data from a cohort of 9230 randomly selected American subjects, available through the ongoing National Health and Nutrition Examination Survey, was used to evaluate combinations of surrogates (age, height, total weight, waist circumference) as predictors of DXA‐determined absolute and relative body fat content. Multiple regression analysis yielded linear combinations of the 4 surrogates that were closely predictive of DXA‐determined absolute fat content (R2 = 0.93 and 0.96 for male and female subjects). Accuracy of the new algorithm was improved over customary surrogate‐based predictors such as body mass index. However prediction of relative body fat was less robust (R2 less than 0.75), probably due to the nonlinear relation between degree of obesity (based on body mass index) and relative body fat. The paradigm was validated using an independent cohort from the National Health and Nutrition Examination Survey, as well as two independent external subject groups. The described regression‐based algorithm is likely to be a sufficiently accurate predictor of absolute body fat (but not relative body fat) to substitute for DXA scanning in many clinical situations. Further work is needed to assess algorithm validity for subgroups of individuals with “atypical” body construction.

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Cross-Calibration of Prodigy and Horizon A Densitometers and Precision of the Horizon A Densitometer

Cited by 15 publications

References 18 publications

Trabecular Bone Score Reference Values for Children and Adolescents According to Age, Sex, and Ancestry

Trabecular Bone Score Reference Values for Children and Adolescents According to Age, Sex, and Ancestry

Cross-Calibration of iDXA and pQCT Scanners at Rural and Urban Research Sites in The Gambia, West Africa

Estimation of Absolute and Relative Body Fat Content Using Noninvasive Surrogates: Can DXA Be Bypassed?

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