Steno Diabetes Center A/S, Niels Steensens vej 2-4, 2820 Gentofte, Denmark.
Diabetes Res Clin Pract. 2012 Mar;95(3):432-8. doi: 10.1016/j.diabres.2011.11.011. Epub 2011 Dec 7.
Current risk scores for undiagnosed diabetes are additive in structure. We sought to derive a globally applicable screening model based on established non-invasive risk factors for diabetes but with a more flexible structure.
Data from the DETECT-2 study were used, including 102,058 participants from 38 studies covering 8 geographical regions worldwide. A global screening model for undiagnosed diabetes was identified through tree-structured regression analysis. The performance of the global screening model was evaluated in each of the geographical regions by receiver operating characteristic (ROC) analysis.
The global screening model included age, height, body mass index, waist circumference and systolic- and diastolic blood pressure. Area under the ROC curve ranged between 0.64 in North America and 0.76 in Australia and New Zealand. Overall, to identify 75% of the undiagnosed diabetes cases, 49% required further diagnostic testing.
We identified a globally applicable screening model to detect individuals at high risk of undiagnosed diabetes. The model performed well in most geographical regions, is simple and requires no calculations. This global screening model may be particularly helpful in developing countries with no population based data with which to develop own screening models.
目前用于诊断未确诊糖尿病的风险评分结构是可累加的。我们试图基于已建立的针对糖尿病的非侵入性风险因素，开发一种具有更灵活结构的全球适用的筛查模型。
使用 DETECT-2 研究的数据，该数据来自全球 8 个地理区域的 38 项研究，共纳入 102058 名参与者。通过树状回归分析确定了全球未确诊糖尿病筛查模型。通过接受者操作特征（ROC）分析评估了该全球筛查模型在每个地理区域的性能。
全球筛查模型包括年龄、身高、体重指数、腰围以及收缩压和舒张压。ROC 曲线下面积在北美为 0.64，在澳大利亚和新西兰为 0.76。总体而言，要识别 75%的未确诊糖尿病病例，需要进一步进行 49%的诊断性检测。
我们确定了一种可用于全球范围内筛查高危未确诊糖尿病患者的模型。该模型在大多数地理区域表现良好，简单易用，无需计算。这种全球筛查模型可能对没有基于人群的筛查模型开发数据的发展中国家特别有帮助。
