妊娠期糖尿病母亲所娩新生儿低血糖的危险因素分析及风险模型构建

陈黎慧; 徐超英; 吴婷婷; 房玥; 曹冰林; 孙钰

doi:10.13898/j.cnki.issn.1000-2200.2023.10.025

妊娠期糖尿病母亲所娩新生儿低血糖的危险因素分析及风险模型构建

Analysis on the risk factors and risk model construction of neonatal hypoglycemia delivered by pregnants with gestational diabetes mellitus

摘要

摘要:
目的探讨妊娠期糖尿病(GDM)母亲所娩新生儿低血糖的影响因素并构建风险预测模型。
方法回顾性选取479例GDM母亲及其所娩新生儿作为研究对象。按照GDM母亲所娩新生儿是否发生低血糖分为低血糖组(n=71)和正常组(n=408)。对比2组的临床资料, 采用多因素logistic回归, 分析GDM母亲所娩新生儿低血糖的影响因素。采用R3.4.3软件绘制列线图模型, 采用受试者工作特征(ROC)曲线评估列线图模型的区分度。采用重复采样法(Bootstrap)自1000次抽样验证列线图模型的预测效能。
结果低血糖组与正常组在孕周、产前BMI、胎儿体质量、产前培训、分娩期血糖情况、分娩方式的比较, 差异均有统计学意义(P < 0.05~P < 0.01)。多因素logistic回归分析显示, 产妇产前BMI高(OR=1.887, 95%CI: 1.215~3.454)、分娩期血糖控制状态欠佳(OR=2.581, 95%CI: 1.762~5.283)、分娩方式为剖宫产(OR=1.889, 95%CI: 1.274~3.548)是GDM母亲所娩新生儿低血糖的危险因素(P < 0.01);产妇孕周长(OR=0.488, 95%CI: 0.274~0.892)、预估胎儿体质量较重(OR=0.472, 95%CI: 0.171~0.895)、产妇产前培训多(OR=0.558, 95%CI: 0.215~0.781)为GDM母亲所娩新生儿低血糖的保护因素(P < 0.05~P < 0.01)。基于上述6个指标构建列线图预测模型。ROC曲线分析显示, 列线图模型预测GDM母亲所娩新生儿低血糖风险的AUC面积为0.869(95%CI: 0.815~0.906), 说明模型区分能力较好。Bootstrap法检验显示, 偏差校准曲线的MAE为0.015, 说明偏差校准曲线与理想曲线贴合良好。
结论以孕周、产前BMI、胎儿体质量、产前培训、分娩期血糖情况及分娩方式来构建GDM母亲所娩新生儿低血糖风险的预测模型有一定价值。

Abstract:
ObjectiveTo explore the influencing factors of hypoglycemia newborns delivered by pregnants with gestational diabetes mellitus(GDM), and construct a risk prediction model.
MethodsThe clinical data of 479 GDM pregnants with regular obstetric check-ups and their newborns were retrospectively analyzed.According to the blood glucose level of newborns delivered by GDM pregnants, the newborns were divided into the hypoglycemia group(n=71) and normal group(n=408).The clinical data between two groups were compared.Multivariate logistic regression was used to analyze the influencing factors of hypoglycemia in newborns delivered by GDM pregnants with.The nomogram model was drawn using R3.4.3 software, and the receiver operating characteristic(ROC) curve was used to evaluate the discrimination of the nomogram model.The predictive performance of the nomogram model in 1 000 samples was verified using the repeated sampling method(Bootstrap).
ResultsThe differences of the gestational week, prenatal BMI, fetal body mass, prenatal training, blood glucose during delivery and delivery mode between two groups were statistically significant(P < 0.05 to P < 0.01).The results of multivariate logistic regression analysis showed that the pregnant women with high prenatal BMI(OR=1.887, 95%CI: 1.215-3.454), poor blood glucose control during delivery(OR=2.581, 95%CI: 1.762-5.283) and cesarean section(OR=1.889, 95%CI: 1.274-3.548) were the risk factors of neonatal hypoglycemia in GDM mothers(P < 0.01).The long gestational week of puerpura(OR=0.488, 95%CI: 0.274-0.892), estimating the heavy fetal body mass(OR=0.472, 95%CI: 0.171-0.895) and more maternal prenatal training(OR=0.558, 95%CI: 0.215-0.781) were the protective factorsof neonatal hypoglycemia delivered by GDM mothers(P < 0.05 to P < 0.01).Based on the above six indicators, the nomogram prediction model was constructed.The results of ROC curve analysis showed that the AUC area of the nomogram model predicting the risk of hypoglycemia in newborns delivered by GDM mothers was 0.869(95%CI: 0.815-0.906), which indicated that the discrimination ability of model was good.The results of Bootstrap test shows that the MAE of deviation calibration curve was 0.015, which indicated that the deviation calibration curve fit well with the ideal curve.
ConclusionsIt is of certain value to construct a predictive model of hypoglycemia risk of neonates delivered by GDM pregnants based on gestational age, prenatal BMI, fetal weight, prenatal training, blood glucose during delivery and delivery methods.

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