Clinical Performance of the Updated Trabecular Bone Score (TBS) Algorithm, Which Accounts for the Soft Tissue Thickness: The OsteoLaus Study
ABSTRACT Regional soft tissue may have a noise effect on trabecular bone score (TBS) and eventually alter its estimate. The current TBS software (TBS iNsight®) is based on an algorithm accounting for body mass index (BMI) (TBSv3.03). We aimed to explore the updated TBS algorithm that accounts for so...
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| Published in | Journal of bone and mineral research Vol. 34; no. 12; pp. 2229 - 2237 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken, USA
John Wiley & Sons, Inc
01.12.2019
Oxford University Press |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0884-0431 1523-4681 1523-4681 |
| DOI | 10.1002/jbmr.3851 |
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| Abstract | ABSTRACT
Regional soft tissue may have a noise effect on trabecular bone score (TBS) and eventually alter its estimate. The current TBS software (TBS iNsight®) is based on an algorithm accounting for body mass index (BMI) (TBSv3.03). We aimed to explore the updated TBS algorithm that accounts for soft tissue thickness (TBSv4.0). This study was embedded in the OsteoLaus cohort of women in Lausanne, Switzerland. Hip and lumbar spine (LS) dual‐energy X‐ray absorptiometry (DXA) scans were performed using Discovery A System (Hologic). The incident major osteoporotic fractures (MOFs) were assessed from vertebral fracture assessments using Genant's method (vertebral MOF) or questionnaires (nonvertebral MOF). We assessed the correlations of bone mineral density (BMD) or TBS with body composition parameters; MOF prediction ability of both versions of TBS; and the differences between Fracture Risk Assessment Tool (FRAX) adjusted for TBSv3.03 or TBSv4.0. In total, 1362 women with mean ± SD age 64.4 ± 7.5 years and mean ± SD BMI 25.9 ± 4.5 kg/m2 were followed for 4.4 years and 132 experienced an MOF. All the anthropometric measurements of our interest were positively correlated with LS, femoral neck, or hip BMD and TBSv4.0; whereas with TBSv3.03 their correlations were negative. In the models adjusted for age, soft tissue thickness, osteoporotic treatment, and LS‐BMD, for each SD decline in TBSv3.03, there was a 43% (OR 1.43; 95% CI, 1.12 to 1.83) increase in the odds of having MOF; whereas for each SD decline in TBSv4.0, there was a 54% (OR 1.54; 95% CI, 1.18 to 2.00) increase in the odds of having an MOF. Both FRAXs were very strongly correlated and the mild differences were present in the already high‐risk women for MOF. This study shows that TBSv4.0 overcomes the debatable residual negative correlation of the current TBS with body size and composition parameters, postulating itself as free from the previously acknowledged technical limitation of TBS. © 2019 American Society for Bone and Mineral Research. |
|---|---|
| AbstractList | Regional soft tissue may have a noise effect on trabecular bone score (TBS) and eventually alter its estimate. The current TBS software (TBS iNsight®) is based on an algorithm accounting for body mass index (BMI) (TBSv3.03 ). We aimed to explore the updated TBS algorithm that accounts for soft tissue thickness (TBSv4.0 ). This study was embedded in the OsteoLaus cohort of women in Lausanne, Switzerland. Hip and lumbar spine (LS) dual-energy X-ray absorptiometry (DXA) scans were performed using Discovery A System (Hologic). The incident major osteoporotic fractures (MOFs) were assessed from vertebral fracture assessments using Genant's method (vertebral MOF) or questionnaires (nonvertebral MOF). We assessed the correlations of bone mineral density (BMD) or TBS with body composition parameters; MOF prediction ability of both versions of TBS; and the differences between Fracture Risk Assessment Tool (FRAX) adjusted for TBSv3.03 or TBSv4.0 . In total, 1362 women with mean ± SD age 64.4 ± 7.5 years and mean ± SD BMI 25.9 ± 4.5 kg/m2 were followed for 4.4 years and 132 experienced an MOF. All the anthropometric measurements of our interest were positively correlated with LS, femoral neck, or hip BMD and TBSv4.0 ; whereas with TBSv3.03 their correlations were negative. In the models adjusted for age, soft tissue thickness, osteoporotic treatment, and LS-BMD, for each SD decline in TBSv3.03 , there was a 43% (OR 1.43; 95% CI, 1.12 to 1.83) increase in the odds of having MOF; whereas for each SD decline in TBSv4.0 , there was a 54% (OR 1.54; 95% CI, 1.18 to 2.00) increase in the odds of having an MOF. Both FRAXs were very strongly correlated and the mild differences were present in the already high-risk women for MOF. This study shows that TBSv4.0 overcomes the debatable residual negative correlation of the current TBS with body size and composition parameters, postulating itself as free from the previously acknowledged technical limitation of TBS. © 2019 American Society for Bone and Mineral Research.Regional soft tissue may have a noise effect on trabecular bone score (TBS) and eventually alter its estimate. The current TBS software (TBS iNsight®) is based on an algorithm accounting for body mass index (BMI) (TBSv3.03 ). We aimed to explore the updated TBS algorithm that accounts for soft tissue thickness (TBSv4.0 ). This study was embedded in the OsteoLaus cohort of women in Lausanne, Switzerland. Hip and lumbar spine (LS) dual-energy X-ray absorptiometry (DXA) scans were performed using Discovery A System (Hologic). The incident major osteoporotic fractures (MOFs) were assessed from vertebral fracture assessments using Genant's method (vertebral MOF) or questionnaires (nonvertebral MOF). We assessed the correlations of bone mineral density (BMD) or TBS with body composition parameters; MOF prediction ability of both versions of TBS; and the differences between Fracture Risk Assessment Tool (FRAX) adjusted for TBSv3.03 or TBSv4.0 . In total, 1362 women with mean ± SD age 64.4 ± 7.5 years and mean ± SD BMI 25.9 ± 4.5 kg/m2 were followed for 4.4 years and 132 experienced an MOF. All the anthropometric measurements of our interest were positively correlated with LS, femoral neck, or hip BMD and TBSv4.0 ; whereas with TBSv3.03 their correlations were negative. In the models adjusted for age, soft tissue thickness, osteoporotic treatment, and LS-BMD, for each SD decline in TBSv3.03 , there was a 43% (OR 1.43; 95% CI, 1.12 to 1.83) increase in the odds of having MOF; whereas for each SD decline in TBSv4.0 , there was a 54% (OR 1.54; 95% CI, 1.18 to 2.00) increase in the odds of having an MOF. Both FRAXs were very strongly correlated and the mild differences were present in the already high-risk women for MOF. This study shows that TBSv4.0 overcomes the debatable residual negative correlation of the current TBS with body size and composition parameters, postulating itself as free from the previously acknowledged technical limitation of TBS. © 2019 American Society for Bone and Mineral Research. Regional soft tissue may have a noise effect on trabecular bone score (TBS) and eventually alter its estimate. The current TBS software (TBS iNsight®) is based on an algorithm accounting for body mass index (BMI) (TBSv3.03). We aimed to explore the updated TBS algorithm that accounts for soft tissue thickness (TBSv4.0). This study was embedded in the OsteoLaus cohort of women in Lausanne, Switzerland. Hip and lumbar spine (LS) dual-energy X-ray absorptiometry (DXA) scans were performed using Discovery A System (Hologic). The incident major osteoporotic fractures (MOFs) were assessed from vertebral fracture assessments using Genant's method (vertebral MOF) or questionnaires (nonvertebral MOF). We assessed the correlations of bone mineral density (BMD) or TBS with body composition parameters; MOF prediction ability of both versions of TBS; and the differences between Fracture Risk Assessment Tool (FRAX) adjusted for TBSv3.03 or TBSv4.0. In total, 1362 women with mean ± SD age 64.4 ± 7.5 years and mean ± SD BMI 25.9 ± 4.5 kg/m2 were followed for 4.4 years and 132 experienced an MOF. All the anthropometric measurements of our interest were positively correlated with LS, femoral neck, or hip BMD and TBSv4.0; whereas with TBSv3.03 their correlations were negative. In the models adjusted for age, soft tissue thickness, osteoporotic treatment, and LS-BMD, for each SD decline in TBSv3.03, there was a 43% (OR 1.43; 95% CI, 1.12 to 1.83) increase in the odds of having MOF; whereas for each SD decline in TBSv4.0, there was a 54% (OR 1.54; 95% CI, 1.18 to 2.00) increase in the odds of having an MOF. Both FRAXs were very strongly correlated and the mild differences were present in the already high-risk women for MOF. This study shows that TBSv4.0 overcomes the debatable residual negative correlation of the current TBS with body size and composition parameters, postulating itself as free from the previously acknowledged technical limitation of TBS. © 2019 American Society for Bone and Mineral Research. ABSTRACT Regional soft tissue may have a noise effect on trabecular bone score (TBS) and eventually alter its estimate. The current TBS software (TBS iNsight®) is based on an algorithm accounting for body mass index (BMI) (TBSv3.03). We aimed to explore the updated TBS algorithm that accounts for soft tissue thickness (TBSv4.0). This study was embedded in the OsteoLaus cohort of women in Lausanne, Switzerland. Hip and lumbar spine (LS) dual‐energy X‐ray absorptiometry (DXA) scans were performed using Discovery A System (Hologic). The incident major osteoporotic fractures (MOFs) were assessed from vertebral fracture assessments using Genant's method (vertebral MOF) or questionnaires (nonvertebral MOF). We assessed the correlations of bone mineral density (BMD) or TBS with body composition parameters; MOF prediction ability of both versions of TBS; and the differences between Fracture Risk Assessment Tool (FRAX) adjusted for TBSv3.03 or TBSv4.0. In total, 1362 women with mean ± SD age 64.4 ± 7.5 years and mean ± SD BMI 25.9 ± 4.5 kg/m2 were followed for 4.4 years and 132 experienced an MOF. All the anthropometric measurements of our interest were positively correlated with LS, femoral neck, or hip BMD and TBSv4.0; whereas with TBSv3.03 their correlations were negative. In the models adjusted for age, soft tissue thickness, osteoporotic treatment, and LS‐BMD, for each SD decline in TBSv3.03, there was a 43% (OR 1.43; 95% CI, 1.12 to 1.83) increase in the odds of having MOF; whereas for each SD decline in TBSv4.0, there was a 54% (OR 1.54; 95% CI, 1.18 to 2.00) increase in the odds of having an MOF. Both FRAXs were very strongly correlated and the mild differences were present in the already high‐risk women for MOF. This study shows that TBSv4.0 overcomes the debatable residual negative correlation of the current TBS with body size and composition parameters, postulating itself as free from the previously acknowledged technical limitation of TBS. © 2019 American Society for Bone and Mineral Research. Regional soft tissue may have a noise effect on trabecular bone score (TBS) and eventually alter its estimate. The current TBS software (TBS iNsight®) is based on an algorithm accounting for body mass index (BMI) (TBS ). We aimed to explore the updated TBS algorithm that accounts for soft tissue thickness (TBS ). This study was embedded in the OsteoLaus cohort of women in Lausanne, Switzerland. Hip and lumbar spine (LS) dual-energy X-ray absorptiometry (DXA) scans were performed using Discovery A System (Hologic). The incident major osteoporotic fractures (MOFs) were assessed from vertebral fracture assessments using Genant's method (vertebral MOF) or questionnaires (nonvertebral MOF). We assessed the correlations of bone mineral density (BMD) or TBS with body composition parameters; MOF prediction ability of both versions of TBS; and the differences between Fracture Risk Assessment Tool (FRAX) adjusted for TBS or TBS . In total, 1362 women with mean ± SD age 64.4 ± 7.5 years and mean ± SD BMI 25.9 ± 4.5 kg/m were followed for 4.4 years and 132 experienced an MOF. All the anthropometric measurements of our interest were positively correlated with LS, femoral neck, or hip BMD and TBS ; whereas with TBS their correlations were negative. In the models adjusted for age, soft tissue thickness, osteoporotic treatment, and LS-BMD, for each SD decline in TBS , there was a 43% (OR 1.43; 95% CI, 1.12 to 1.83) increase in the odds of having MOF; whereas for each SD decline in TBS , there was a 54% (OR 1.54; 95% CI, 1.18 to 2.00) increase in the odds of having an MOF. Both FRAXs were very strongly correlated and the mild differences were present in the already high-risk women for MOF. This study shows that TBS overcomes the debatable residual negative correlation of the current TBS with body size and composition parameters, postulating itself as free from the previously acknowledged technical limitation of TBS. © 2019 American Society for Bone and Mineral Research. |
| Author | Rodriguez, Elena Gonzalez Aubry‐Rozier, Bérengère Shevroja, Enisa Stoll, Delphine Hans, Gabriel Lamy, Olivier Hans, Didier |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31419331$$D View this record in MEDLINE/PubMed |
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| Keywords | DXA FRACTURE RISK ASSESSMENT MENOPAUSE FRACTURE PREVENTION OSTEOPOROSIS |
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article-title: A meta-analysis of trabecular bone score in fracture risk prediction and its relationship to FRAX publication-title: J Bone Miner Res doi: 10.1002/jbmr.2734 |
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Regional soft tissue may have a noise effect on trabecular bone score (TBS) and eventually alter its estimate. The current TBS software (TBS iNsight®)... Regional soft tissue may have a noise effect on trabecular bone score (TBS) and eventually alter its estimate. The current TBS software (TBS iNsight®) is based... |
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| SubjectTerms | Algorithms Body composition Body mass index Body size Bone composition Bone mineral density Cancellous bone Dual energy X-ray absorptiometry DXA FRACTURE PREVENTION FRACTURE RISK ASSESSMENT Fractures Hip MENOPAUSE OSTEOPOROSIS Risk assessment Spine (lumbar) Vertebrae |
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| Title | Clinical Performance of the Updated Trabecular Bone Score (TBS) Algorithm, Which Accounts for the Soft Tissue Thickness: The OsteoLaus Study |
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