Xin Zhong, Hua Lin, Wang Xu-Hong, Zhao Dong, Yu Cai-Guo, Ma Ya-Hong, Zhao Lei, Cao Xi, Yang Jin-Kui
Department of Endocrinology, Beijing Tongren Hospital, Capital Medical University, 1 Dong Jiao Min Xiang, Beijing 100730, China.
Beijing Key Laboratory of Diabetes Prevention and Research, 1 Dong Jiao Min Xiang, Beijing 100730, China.
Int J Endocrinol. 2017;2017:3894870. doi: 10.1155/2017/3894870. Epub 2017 May 30.
We reanalyzed previous data to develop a more simplified decision tree model as a screening tool for unrecognized diabetes, using basic information in Beijing community health records. Then, the model was validated in another rural town. Only three non-laboratory-based risk factors (age, BMI, and presence of hypertension) with fewer branches were used in the new model. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) for detecting diabetes were calculated. The AUC values in internal and external validation groups were 0.708 and 0.629, respectively. Subjects with high risk of diabetes had significantly higher HOMA-IR, but no significant difference in HOMA-B was observed. This simple tool will help general practitioners and residents assess the risk of diabetes quickly and easily. This study also validates the strong associations of insulin resistance and early stage of diabetes, suggesting that more attention should be paid to the current model in rural Chinese adult populations.
我们重新分析了之前的数据，利用北京社区健康记录中的基本信息，开发了一个更简化的决策树模型作为未识别糖尿病的筛查工具。然后，该模型在另一个乡村小镇进行了验证。新模型仅使用了三个基于非实验室的风险因素（年龄、体重指数和高血压的存在），且分支较少。计算了检测糖尿病的灵敏度、特异度、阳性预测值、阴性预测值和曲线下面积（AUC）。内部和外部验证组的AUC值分别为0.708和0.629。糖尿病高危受试者的HOMA-IR显著更高，但HOMA-B未观察到显著差异。这个简单的工具将有助于全科医生和居民快速、轻松地评估糖尿病风险。本研究还验证了胰岛素抵抗与糖尿病早期的强关联，表明在中国农村成年人群中应更多关注当前模型。
