
NEW YORK (Reuters Health), Aug 1 - Four previously validated decision rules are useful in clinical practice for identifying postmenopausal women likely to have osteoporosis who should undergo bone mineral density (BMD) screening, Spanish researchers report in the June issue of the Journal of Rheumatology.
Mass screening using dual energy x-ray absorptiometry (DEXA), the gold standard for BMD evaluation, is not recommended routinely due to its cost, the authors explain, but there are no clear criteria to decide which women should undergo DEXA testing.
Dr. Joan M. Nolla and colleagues from Hospital Universitari de Bellvitge, Barcelona, evaluated the utility of the Osteoporosis Risk Assessment Instrument (ORAI), Osteoporosis Self-Assessment Tool (OST), Osteoporosis Index of Risk (OSIRIS), and Body Weight Criterion (BWC) in 665 postmenopausal women referred to their bone densitometry unit.
On the basis of WHO criteria, the team found that similar percentages of women would be recommended for BMD testing using ORAI (45%), OST (46%), OSIRIS (37%), and BWC (70%), the authors report.
The overall frequency of osteoporosis in the group was 17.6%
Sensitivity for selecting women with osteoporosis was highest for BWC (83.8%) and lower but similar for the other three decision rules (58.1%-69.2%), the report indicates, whereas specificity was highest for OSIRIS (67.9%) and lowest for BWC (33.4%).
Sensitivity for all four decision rules increased with increasing patient age, whereas specificity decreased with increasing patient age. As a result, positive predictive values were highest in the 60-69 years age group, and negative predictive values were highest in the 40-49 years age group.
"Our data indicate that in younger postmenopausal women, decision rules would be useful as a screening method to rule out the presence of the disease and the need for BMD scanning," Dr. Nolla and colleagues conclude. "A population-based study would be valuable to assure the scientific reliability of our findings."
However, "The relevance of these decision rules could decrease in the future," the researchers note. "It seems that there is a progressive tendency to recommend the identification of individuals based on fracture risk rather than BMD status."
Last Updated: 2007-07-31 12:24:24 -0400 (Reuters Health)
J Rheumatol 2007;34:1307-1312.
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![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=100&q=70&w=100)





![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)








