Wildner Manfred, Peters Andrea, Raghuvanshi Vibhavendra S, Hohnloser Jörg, Siebert Uwe
Bavarian Public Health Research Center and Institute for Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilian University, Munich, Germany.
Osteoporos Int. 2003 Nov;14(11):950-6. doi: 10.1007/s00198-003-1487-z. Epub 2003 Sep 16.
Identification of women at risk for osteoporosis is of great importance for the prevention of osteoporotic fractures. Routine BMD measurement of all women is not feasible for most populations, hence identification of a high-risk subset of women is an important element of effective preventive strategies.
We identified 959 postmenopausal non-Hispanic women aged 51 years and above from the NHANES III study to assess the relative contribution of risk predictors for low BMD at the whole proximal femur and the femoral neck regions. Based on recognized risk factors for osteoporosis identified by a systematic literature search, we ran several multiple linear regression models based on the results of preceding bivariate analyses. We show several models based on their explanatory ability assessed by adjusted r(2), ROC, and C-value analyses rather than on the coefficients and P values. We furthermore examined the sensitivity, specificity, and predictive values of our preferred models for various cutoff T-scores-the choice of which will vary depending on different study goals and population characteristics.
Age and weight were by far the most informative predictors for low bone mineral density out of a list of 20 candidate risk predictors. Our preferred prediction models for the two regions hence contained only two variables: i.e., age and measured weight. The resulting parsimonious model to predict BMD at whole proximal femur had an adjusted r(2) of 0.43, an area under the ROC curve of 0.85, and a C-value of 0.70. Similarly, prediction for BMD at the femoral neck had adjusted r(2), area under the curve, and C-value of 0.39, 0.83, and 0.66, respectively.
The model equations, predicted T-score = -1.332-0.0404 x (age) + 0.0386 x (measured weight) and predicted T-score = -1.318-0.0360 x (age) + 0.0314 x (measured weight) for whole proximal femur and femoral neck, respectively, can be used in field conditions for screening purposes. More complex prediction equations add little explanatory power. Based on the study goals and the population characteristics, specific cutoff T-scores have to be decided before using these equations.
识别骨质疏松风险女性对于预防骨质疏松性骨折至关重要。对大多数人群而言，对所有女性进行常规骨密度测量并不可行，因此识别高风险女性亚组是有效预防策略的重要组成部分。
我们从美国国家健康和营养检查调查（NHANES）III研究中识别出959名年龄在51岁及以上的绝经后非西班牙裔女性，以评估全股骨近端和股骨颈区域低骨密度风险预测因素的相对贡献。基于系统文献检索确定的公认骨质疏松风险因素，我们根据先前双变量分析的结果运行了多个多元线性回归模型。我们展示了几个基于调整后r²、ROC曲线下面积和C值分析评估的解释能力的模型，而非基于系数和P值。此外，我们还检查了我们首选模型对于不同截断T值的敏感性、特异性和预测值——截断T值的选择将根据不同的研究目标和人群特征而有所不同。
在20个候选风险预测因素中，年龄和体重是迄今为止低骨密度最具信息量的预测因素。因此，我们针对这两个区域的首选预测模型仅包含两个变量，即年龄和实测体重。用于预测全股骨近端骨密度的简约模型调整后r²为0.43，ROC曲线下面积为0.85，C值为0.70。同样，股骨颈骨密度预测模型的调整后r²、曲线下面积和C值分别为0.39、0.83和0.66。
预测全股骨近端和股骨颈骨密度的模型方程分别为预测T值 = -1.332 - 0.04x（年龄）+ 0.0386x（实测体重）和预测T值 = -1.318 - 0.0360x（年龄）+ 0.0314x（实测体重），可在现场条件下用于筛查目的。更复杂的预测方程增加的解释力很小。根据研究目标和人群特征，在使用这些方程之前必须确定特定的截断T值。
