Yang Jie-Mei, Wang Li-Zhi, Zhao Xiang-Feng, Ye Qian-Cheng, Wan Cui, Li Xiu-Zhen, Huang Rui-Fen, Peng Rong-Rong, Liu Cai-Xia
Department of Obstetrics, Zhuhai Center For Maternal and Child Health Care, Zhuhai, Guangdong, 519001, China.
Department of Nursing, Zhuhai Center For Maternal and Child Health Care, Zhuhai, Guangdong, 519001, China.
BMC Pregnancy Childbirth. 2025 May 26;25(1):613. doi: 10.1186/s12884-025-07740-8.
We explored the prevalence and determinants of hypoglycemia in patients with gestational diabetes mellitus (GDM), and we developed and validated a nomogram prediction model.
We extracted data from the clinical records of 475 patients with GDM attending the tertiary class A specialized hospital in Zhuhai City between December 2021 and June 2023 for a modeling group, and we used data of another cohort of 204 GDM cases for a validation group. We conducted a logistic regression analysis to identify factors associated with hypoglycemia in patients with GDM and generated a risk prediction model presented as a nomogram. The model was validated using data from the patients in the validation group.
The prevalence of hypoglycemia in the study population was 25.5%. Our risk prediction model incorporated four predictors, including a fasting oral glucose tolerance test (OGTT) value, the number of fetuses, the presence or absence of intrahepatic cholestasis of pregnancy (ICP), and the blood glucose level self-monitoring frequency. The area under the receiver operating characteristic (ROC) curve was 0.786 for the modeling set and 0.742 for the validation set. The Brier score was 0.155, and the calibration slope was 0.750, demonstrating satisfactory clinical usefulness of the model. Moreover, a decision curve analysis further supported our model's clinical relevance.
The prevalence of hypoglycemia in patients with GDM is considerable. Our nomogram prediction model demonstrated good performance for identifying high-risk individuals. The model could serve as a valuable tool for screening and managing hypoglycemia among patients with GDM.
我们探讨了妊娠期糖尿病（GDM）患者低血糖的患病率及其决定因素，并开发并验证了一种列线图预测模型。
我们从2021年12月至2023年6月期间在珠海市某三级甲等专科医院就诊的475例GDM患者的临床记录中提取数据作为建模组，另外选取204例GDM病例的数据作为验证组。我们进行了逻辑回归分析，以确定与GDM患者低血糖相关的因素，并生成了一个以列线图表示的风险预测模型。使用验证组患者的数据对该模型进行验证。
研究人群中低血糖的患病率为25.5%。我们的风险预测模型纳入了四个预测因素，包括空腹口服葡萄糖耐量试验（OGTT）值、胎儿数量、是否存在妊娠期肝内胆汁淤积症（ICP）以及血糖自我监测频率。建模集的受试者工作特征（ROC）曲线下面积为0.786，验证集为0.742。Brier评分为0.155，校准斜率为0.750，表明该模型具有令人满意的临床实用性。此外，决策曲线分析进一步支持了我们模型的临床相关性。
GDM患者低血糖的患病率相当高。我们的列线图预测模型在识别高危个体方面表现良好。该模型可作为筛查和管理GDM患者低血糖的有价值工具。
