A novel model enhances HbA1c‐based diabetes screening using simple anthropometric, anamnestic, and demographic information 一个新颖的使用简单人体测量学参数、既往史以及人口统计学信息增强基于HbA1c检查效力的糖尿病筛查模型

Background The sensitivity of HbA1c is not optimal for the screening of patients with latent diabetes. We hypothesize that simple healthcare information could improve accuracy. Methods We retrospectively analyzed data, including HbA1c, from multiple years from the National Health and Nutrition Examination Survey (NHANES) database (2005–2010). The data were used to create a logistic regression classification model for screening purposes. Results The study evaluated data for 5381 participants, including 404 with undiagnosed diabetes. The HbA1c screening data were supplemented with information about age, waist circumference, and physical activity in the HbA1c+ model. Alone, HbA1c alone had a receiver operating characteristics (ROC) curve for the area under the curve (AUC) of 0.808 (95% confidence interval [CI] 0.792–0.834). The HbA1c+ model had an ROC AUC of 0.851 (95% CI 0.843–0.872). There was a significant difference in the AUC between our model and using HbA1c without supplementary information (P < 0.05). Conclusions We have developed a novel screening model that could help improve screening for type 2 diabetes with HbA1c. It seems beneficial to systematically add additional patient healthcare information in the process of screening with 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 Health and Nutrition Examination 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 operating characteristics，ROC）曲线其曲线下面积（area under the 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。