Singh指数法的临床应用探讨

目的 探讨Singh指数法在骨质疏松症诊断及预测骨折风险方面的临床意义。方法 取260例绝经后妇女的前后位骨盆X线片，根据有无椎体压缩变形分成两组：非椎体压缩组（209例，46－80岁）和椎体压缩组（51例）按Singh指数法的分度标准，将股骨近段骨质丢失的情况由低到 高分成连续性的Ⅶ度。结果 非椎体压缩组有96．6％的对象Singh指数异常，Ⅴ度至Ⅵ度84．1％，Ⅳ度及Ⅳ度以下12．5％；椎体压缩组，所有病例的Singh指数均显示异常，Ⅴ度3．9％，Ⅳ度9．8％，Ⅲ度及Ⅲ度以下86．3％。两组病例的Singh指数与年龄呈负相关（非椎体压缩组r＝－0．637，椎体压缩组r＝－0．654，P＜0．01）；两组病例在SinghⅣ度显示最大的交汇，并以Ⅳ度为界，非椎体压缩组的Singh指数主要分布在Ⅳ度以上区域，而椎体压缩组则主要分布在Ⅳ度以下区域。结论 Singh指数法是一种经济、方便和有效的骨密度测定方法，结合临床和有关生化检查，可用于骨质疏松症的诊断和骨折风险的预测，应作为诊断临床骨质疏松症最基本的手段之一。

Assessment of value of Singh index in diagnosing and treating osteoporosis

Objective\ To assess the value of Singh index in diagnosing postmenopausal osteoporosis. Methods\ 260 cases were divided into two groups:non vertebral compression group(n=209,aged 46-89)and vertebral compression group(n=51,aged 46-89)with spontaneous compression of at least one vertebra.The trabecular patterns of femoral neck were interpreted with the Singh index.Osteoporosis of femoral neck was divided into seven successive grades of increasing severity. Results\ 96 6% of the subjects in the non vertebral compression group showed abnormal Singh index,84.1% with grades 5 to 6,12.5% with grades 4 to 1.The Singh index was abnormal in all patients with vertebral compression,3.9% with grade 5,9.8% with grade 4,and 86.3% with grades 3 to 1.The Singh index lowered with increasing age in each group(r=-0.637 in non compression group and r=-0.654 in compression group, P <0.001).Most of subjects were over the grade 4 in the non vertebral compression group and below the grade 4 in vertebral compression group.The bulk was in the grade 4. Conclusion\ The Singh index is simple,inexpensive and reliable for assessing the degree of osteoporosis.By combining Singh index with clinical history,spinal radiograph and laboratory biochemical markers,the diagnosis of osteoporosis can be made and fracture risk predicted.Singh index should be used as a one of basic methods to diagnose osteoporosis and discriminate those at greater risk of developing osteoporotic fracture from those at lesser risk.