A novel model enhances HbA1c-based diabetes screening using simple anthropometric, anamnestic, and demographic information.

摘要: Background The sensitivity of HbA1c is not optimal for the screening patients with latent diabetes. We hypothesize that simple healthcare information could improve accuracy. Methods We retrospectively analyzed data, including HbA1c, from multiple years National Health and Nutrition Examination Survey (NHANES) database (2005–2010). The data were used to create a logistic regression classification model purposes. Results The study evaluated 5381 participants, 404 undiagnosed supplemented about age, waist circumference, physical activity in HbA1c+ model. Alone, alone had receiver operating characteristics (ROC) curve area under (AUC) 0.808 (95% confidence interval [CI] 0.792–0.834). an ROC AUC 0.851 CI 0.843–0.872). There was significant difference between our using without supplementary (P < 0.05). Conclusions We have developed novel help type 2 diabetes HbA1c. It seems beneficial systematically add additional patient process HbA1c. 摘要 背景：对于筛查隐性糖尿病患者来说HbA1c的敏感性并不是最佳的。我们假设简单的医疗信息可以改善筛查的精确度。 方法：我们进行了回顾性的数据分析，其中包括来自全国健康与营养调查研究（National Survey，NHANES）数据库多年（2005–2010）的HbA1c结果。为了筛查的目的使用这些数据建立了一个对数回归分类模型。 结果：这项研究评估了5381名参与者的数据，包括404名未被诊断的糖尿病患者。在HbA1c+模型中，除了HbA1c筛查数据外还要加上有关年龄、腰围以及体力活动的信息。仅包括HbA1c的模型的受试者工作特征（receiver characteristics，ROC）曲线其曲线下面积（area curve，AUC）为0.808（95% CI：0.792–0.834）。HbA1c+模型的ROC AUC为0.851（95% CI：0.843–0.872）。我们的模型与仅使用HbA1c而未添加补充信息的模型相比，其AUC具有显著性的差异（P < 0.05）。 结论：我们已经在HbA1c的基础上制定出了一个新的筛查模型，它可能有助于改善2型糖尿病的筛查。在使用HbA1c进行筛查时系统地加入患者的医疗信息可能是有益的。