建立预测妊娠期高血糖患者分娩巨大儿风险的列线图模型

目的：构建并验证妊娠期高血糖（hyperglycemia in pregnancy,HIP）患者分娩巨大儿风险的列线图模型。方法：回顾性分析2020年11月—2022年2月在太原市妇幼保健院分娩的HIP患者资料。采用多因素Logistic回归分析筛选发生巨大儿的独立影响因素，R软件构建列线图模型，采用受试者工作特征曲线下面积对该模型的效能进行评估，决策曲线分析（decision curve analysis,DCA）评估模型的临床使用价值。结果：(1)纳入1 098例HIP患者进行建模，其中92例（8.38%）孕妇分娩巨大儿。按7∶3比例将所有患者随机分为训练集（761例）和测试集（337例）。(2)多因素Logistic回归分析发现，经产妇（OR=3.19,95%CI:1.58～6.54,P=0.001）、高血压家族史（OR=2.28,95%CI:1.06～4.90,P=0.034）、妊娠前体质量指数（OR=1.18,95%CI:1.08～1.30,P<0.001）、双顶径（OR=13.52,95%CI:4.04～48.38,P<0.001）、腹围（OR=2.83,95%...

Establishment of A Nomogram Model to Predict the Risk of Macrosomia in Patients with Hyperglycemia in Pregnancy

Objective: To establish and validate a nomogram model which can predict the risk of macrosomia in patients with hyperglycemia in pregnancy (HIP). Methods: A retrospective analysis was performed on the data of pregnant women with HIP who deliveried at Taiyuan Maternal and Child Health Care Hospital from November 2020 to February 2022. Multivariate logistic regression analysis was used to screen independent influencing factors for the occurrence of macrosomia, R software was used to construct the column line graph model, the area under the receiver operator characteristic curve (AUC) was used to assess the efficacy of the model, and decision curve analysis (DCA) was used to evaluate the clinical value of the model. Results: ①A total of 1 098 HIP medical records were included in the model, and 92 (8.38%) pregnant women gave birth macrosomia. All records were randomly divided into training set (761 cases) and test set (337 cases) according to 7∶3 ratio. ② The multivariate logistic regression analysis revealed that multiparous history (OR=3.19, 95%CI: 1.58-6.54, P=0.001), family history of hypertension (OR=2.28, 95%CI: 1.06-4.90, P=0.034), pre-pregnancy body mass index (OR=1.18, 95%CI: 1.08-1.30, P<0.001), biparietal diameter (OR=13.52, 95%CI: 4.04-48.38, P<0.001) and abdominal circumference (OR=2.83, 95%CI: 2.17-3.81, P<0.001) were independent risk factors for macrosomia and the column line graph model was developed accordingly. ③ The AUC on the training and test sets were 0.93 (95%CI: 0.90-0.97) and 0.92 (95%CI: 0.88-0.97), respectively. No significant difference was observed on the area of AUC (P=0.69). The results indicated that the model worked well in both the training and test sets. ④ DCA results showed that when the threshold probability (Pt) ≥7%, the use of this nomogram prediction model can improve the net benefits of pregnant women. That is, the model has certain clinical value. Conclusions: A nomogram model which could assess the risk of macrosomia in patients with HIP was preliminarily established. The model has certain accuracy and is expected to be a quantitative tool to guide clinical timing of delivery, individual labor process monitoring, and decision of delivery mode.