Wang Yanmei, Ge Zhijuan, Chen Lei, Hu Jun, Zhou Wenting, Shen Shanmei, Zhu Dalong, Bi Yan
Department of Endocrinology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321, Zhongshan Road, Nanjing, 210008, China.
Department of Endocrinology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou, China.
Diabetes Ther. 2021 Jun;12(6):1721-1734. doi: 10.1007/s13300-021-01066-2. Epub 2021 May 15.
Gestational diabetes mellitus (GDM) is associated with adverse perinatal outcomes. Accurate models for early prediction of GDM are lacking. This study aimed to explore an early risk prediction model to identify women at high risk of GDM through a risk scoring system.
This was a retrospective cohort study of 785 control pregnancies and 855 women with GDM. Maternal clinical characteristics and biochemical measures were extracted from the medical records. Logistic regression analysis was used to obtain coefficients of selected predictors for GDM in the training cohort. The discrimination and calibration of the risk scores were evaluated by the receiver-operating characteristic (ROC) curve and a Hosmer-Lemeshow test in the internal and external validation cohort, respectively.
In the training cohort (total = 1640), two risk scores were developed, one including predictors collected at the first antenatal care visit for early prediction of GDM, such as age, height, pre-pregnancy body mass index, educational background, family history of diabetes, menstrual history, history of cesarean delivery, GDM, polycystic ovary syndrome, hypertension, and fasting blood glucose (FBG), and the total risk score also including FBG and triglyceride values during 14-20 gestational weeks. Our total risk score yielded an area under the curve (AUC) of 0.845 (95% CI = 0.805-0.884). This performed better in an external validation cohort, with an AUC of 0.886 (95% CI = 0.856-0.916).
The GDM risk score, which incorporates several potential clinical features with routine biochemical measures of GDM, appears to be a sensitive and reliable screening tool for earlier detection of GDM risk.
妊娠期糖尿病（GDM）与不良围产期结局相关。目前缺乏用于早期预测GDM的准确模型。本研究旨在探索一种早期风险预测模型，通过风险评分系统识别GDM高危女性。
这是一项回顾性队列研究，纳入785例对照妊娠和855例GDM女性。从病历中提取孕产妇临床特征和生化指标。在训练队列中，采用逻辑回归分析获得GDM选定预测因素的系数。分别通过受试者工作特征（ROC）曲线和Hosmer-Lemeshow检验在内部和外部验证队列中评估风险评分的辨别力和校准度。
在训练队列（共1640例）中，开发了两个风险评分，一个包括首次产前检查时收集的用于早期预测GDM的预测因素，如年龄、身高、孕前体重指数、教育背景、糖尿病家族史、月经史、剖宫产史、GDM、多囊卵巢综合征、高血压和空腹血糖（FBG），总风险评分还包括孕14 - 20周期间的FBG和甘油三酯值。我们的总风险评分曲线下面积（AUC）为0.845（95%CI = 0.805 - 0.884）。在外部验证队列中表现更好，AUC为0.886（95%CI = 0.856 - 0.916）。
结合多种潜在临床特征与GDM常规生化指标的GDM风险评分，似乎是早期检测GDM风险的敏感且可靠的筛查工具。
