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脊柱曲率不规则指数可独立识别椎体骨折。

The spinal curvature irregularity index independently identifies vertebral fractures.

作者信息

Maalouf G, Maalouf N M, Schaaf N, Zebaze R M, Nehme A, Tannous Z, Wehbe J, Adib G, Gannagé-Yared M-H, Seeman E

机构信息

Department of Orthopaedics, St George Hospital, P.O. Box 166378, Achrafieh-Beirut 11002807, Lebanon.

出版信息

Osteoporos Int. 2007 Mar;18(3):279-83. doi: 10.1007/s00198-006-0235-6. Epub 2006 Oct 5.

Abstract

INTRODUCTION AND HYPOTHESIS

The spinal curvature irregularity index (SCII) is a quantitative measure of the irregularity of the spinal curvature. We evaluated the predictive ability of SCII to identify subjects with vertebral fractures (VF).

METHODS

Vertebral heights were measured by quantitative vertebral morphometry in 461 Lebanese women 20-89 years of age and VFs were ascertained by the grade 1 Eastell method. SCII scores were log-transformed and expressed as Z-SCII, the number of standard deviations above or below the mean ln(SCII) of young patients without VF. Univariate and multivariate binary logistic regression models were used to identify clinical predictors of VF.

RESULTS

Women with a higher SCII were more likely to have prevalent VF. A higher SCII was associated with a greater prevalence of VF within each category of femoral neck BMD (normal, osteopenia, osteoporosis). In univariate analysis, predictors of VF included Z-SCII (odds ratio, OR: 2.21, 95% CI: 1.80-2.71) and femoral neck T-score (OR: 1.35, 95% CI: 1.12-1.63). In multivariate analysis, predictors of VF were: Z-SCII (OR: 1.54, 95% CI: 1.02-2.32), femoral neck T-score (OR: 1.41, 95% CI: 1.11-1.78) and age(3) (OR: 1.40, 95% CI 1.10-1.82). At a cutoff SCII of 9.5%, the sensitivity and specificity of SCII for VF were 71 and 64% respectively, and higher SCII cutoffs identified VFs with greater specificity.

CONCLUSION

The SCII is a robust, simple and independent indicator of the presence of VFs.

摘要

引言与假设

脊柱曲率不规则指数(SCII)是对脊柱曲率不规则程度的一种定量测量方法。我们评估了SCII识别椎体骨折(VF)患者的预测能力。

方法

采用定量椎体形态测量法测量了461名年龄在20 - 89岁的黎巴嫩女性的椎体高度,并通过1级伊斯特尔方法确定椎体骨折情况。对SCII评分进行对数转换并表示为Z - SCII,即无椎体骨折的年轻患者平均ln(SCII)之上或之下的标准差数量。使用单变量和多变量二元逻辑回归模型来确定椎体骨折的临床预测因素。

结果

SCII较高的女性更有可能患有现患椎体骨折。在股骨颈骨密度的每个类别(正常、骨量减少、骨质疏松)中,较高的SCII与椎体骨折的更高患病率相关。在单变量分析中,椎体骨折的预测因素包括Z - SCII(比值比,OR:2.21,95%置信区间:1.80 - 2.71)和股骨颈T值(OR:1.35,95%置信区间:1.12 - 1.63)。在多变量分析中,椎体骨折的预测因素为:Z - SCII(OR:1.54,95%置信区间:1.02 - 2.32)、股骨颈T值(OR:1.41,95%置信区间:1.11 - 1.78)和年龄(OR:1.40,95%置信区间1.10 - 1.82)。在SCII临界值为9.5%时,SCII对椎体骨折的敏感性和特异性分别为71%和64%,较高的SCII临界值能更特异的识别椎体骨折。

结论

SCII是存在椎体骨折的一个可靠、简单且独立的指标。

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