Department of Obstetrics and Gynecology, Shanxi Bethune Hospital & Shanxi Academy of Medical Sciences, Shanxi Bethune Hospital affiliated to Shanxi Medical University, Taiyuan, China.
J Matern Fetal Neonatal Med. 2023 Dec;36(2):2232072. doi: 10.1080/14767058.2023.2232072.
To establish and verify a nomogram model that can predict the risk of macrosomia in patients with gestational diabetes mellitus (GDM).
Data of patients with GDM who delivered their babies in Shanxi Bethune Hospital between November 2020 and February 2022 were analyzed. Multifactor logistic regression analysis was used to screen the independent risk factors for macrosomia. The model was constructed by R software. The area under the receiver operating characteristic curve (AUC) and goodness-of-fit analysis were used to evaluate its efficiency and accuracy. The clinical application value was evaluated using the decision curve analysis (DCA).
A total of 991 patients with GDM were enrolled for modeling. Multigravida, pre-pregnancy body mass index, family history of hypertension, abdominal circumference, and biparietal diameter were independent risk factors for macrosomia, and the prediction model was established. The AUC in the training and test set were 0.93 (0.89-0.97) and 0.90 (0.84-0.96), respectively, and the difference was not statistically significant. The DCA suggested that the model has a high clinical application value.
The nomogram model for predicting macrosomia in patients with GDM was established. The model has certain accuracy and is expected to be a quantitative tool to guide clinical decision of delivery timing, individualized labor monitoring, and delivery mode.
建立并验证一个预测妊娠期糖尿病（GDM）患者发生巨大儿风险的列线图模型。
分析 2020 年 11 月至 2022 年 2 月在山西白求恩医院分娩的 GDM 患者的数据。采用多因素 logistic 回归分析筛选发生巨大儿的独立危险因素。使用 R 软件构建模型。通过受试者工作特征曲线（ROC）下面积（AUC）和拟合优度分析评估其效能和准确性。采用决策曲线分析（DCA）评估其临床应用价值。
共纳入 991 例 GDM 患者进行建模。多胎妊娠、孕前体质量指数、高血压家族史、腹围和双顶径是巨大儿的独立危险因素，建立了预测模型。训练集和验证集的 AUC 分别为 0.93（0.89-0.97）和 0.90（0.84-0.96），差异无统计学意义。DCA 提示该模型具有较高的临床应用价值。
建立了预测 GDM 患者发生巨大儿的列线图模型。该模型具有一定的准确性，有望成为指导临床分娩时机、个体化产程监测和分娩方式选择的定量工具。
