A population study of the screening potential of assessment of trabecular pattern of the femoral neck (Singh index): the Chingford Study
- 1 April 1995
- journal article
- Published by Oxford University Press (OUP) in The British Journal of Radiology
- Vol. 68 (808), 389-393
- https://doi.org/10.1259/0007-1285-68-808-389
Abstract
The trabecular pattern of the femoral neck (Singh index) has been used as a measure of osteopenia and fracture risk but the value of this method is controversial. This study assessed the usefulness of the Singh index (SI) by using dual energy X-ray absorptiometry (DXA) as the “gold standard”. 659 Caucasian women (45–70 years) from an age–sex register of a general practice had their femoral neck and lumbar spine bone densities measured by DXA and had antero–posterior hip X-rays performed which were then categorized into six osteopenia grades using the SI method. The intraobserver and interobserver reproducibility of this method was good (kappa = 0.64 and 0.61, respectively). The SI grades correlated significantly with body mass index (r = 0.35) and age (r = 0.17) (p < 0.001). The mean femoral neck and lumbar spine bone densities were significantly higher with increasing SI grade even after adjustment for age and body mass index (p < 0.001). The proportion of subjects below the fracture threshold (2 SD below mean peak bone mass) decreased with increasing SI grade, ranging from 100% in SI grade 2 to 16.8% in SI grade 6. There was, however, wide overlap of bone densities between the grades. Using the criteria “osteoporosis ≤ SI grade 4”, the sensitivity and specificity of the SI method diagnosing low bone mass was 35.1% and 90.0%, respectively. These data suggest that the SI is a reproducible tool which may detect differences in bone mass between populations or subgroups within populations, although caution should be used in classifying individual patients because of the wide variation in bone density. The method has a low sensitivity but a relatively high specificity in diagnosing low bone mass.Keywords
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