摘要
目的探讨妊娠期糖尿病(GDM)血糖控制水平与新生儿高胆红素血症(NHB)发病的相关性。方法选取2017年3月至2021年3月淮安市第二人民医院收治的GDM孕妇100例为研究对象,纳入GDM组;另选取同期在院产检分娩的妊娠期无GDM,分娩孕周、年龄匹配的孕妇100例为对照组。将GDM组分为NHB组(n=30)与无NHB组(n=70)。比较GDM组与对照组并发症的发生情况,分析血糖控制水平与NHB发生的关系。使用多因素Logistic回归分析GDM孕妇分娩的新生儿发生NHB的影响因素。应用受试者工作特征(ROC)曲线下面积(AUC)评估血糖控制水平对NHB发生的诊断效能。使用Python 3.6基于Softmax策略构建人工神经网络模型,并对模型进行评价。结果GDM组NHB、巨大儿、新生儿窘迫并发症发生率均明显高于对照组,差异均有统计学意义(χ2值分别为9.765、5.207、7.236,P<0.05)。在GDM组中,有21例孕妇空腹血糖>7.8mmol/L,其中11例新生儿发生NHB,占52.4%;有37例孕妇空腹血糖为6.1~7.8mmol/L,其中14例新生儿发生NHB,占37.8%;有42例孕妇空腹血糖<6.1mmol/L,其中5例新生儿发生NHB,占11.9%。单因素分析显示:与NHB的发生有关的因素包括:孕妇产前空腹血糖和新生儿出生身长(t值分别为2.243、2.368),以及新生儿Apgar 5min评分<7分、空腹血糖控制不佳、缺乏母乳、胎膜早破、胎儿窒息、新生儿感染、首次胎便排出时间>24h、早产儿、开奶时间>24h(χ2值分别为9.580、11.758、8.129、20.003、24.536、30.014、5.563、7.233、6.994),NHB组与无NHB组比较差异均有统计学意义(P<0.05)。多因素Logistic回归分析显示,胎膜早破、空腹血糖控制不佳、胎儿窒息、新生儿感染、产前空腹血糖、新生儿Apgar 5min评分<7分、早产儿、首次胎便排出时间>24h、缺乏母乳、开奶时间>24h均是发生NHB的独立影响因素(P<0.05)。ROC曲线分析显示,空腹血糖控制水平预测新生儿发生NHB的AUC为0.876,灵敏度为86.9%,特异度为78.3%。人工神经网络模型显示,胎膜早破、空腹血糖控制不佳、胎儿窒息、新生儿感染所占权重均较高;ROC曲线分析显示,AUC为0.869(95%CI:0.795~0.947),临界值为0.627时预测GDM孕妇分娩的新生儿发生NHB的灵敏度为89.5%,特异度为98.3%。结论GDM会增加NHB、巨大儿、新生儿窘迫并发症发生率。GDM孕妇血糖控制越佳,NHB发生率越低。胎膜早破、空腹血糖控制不佳、胎儿窒息、新生儿感染、产前空腹血糖、新生儿Apgar 5min评分<7分、早产儿、首次胎便排出时间>24h、缺乏母乳、开奶时间>24h均是新生儿发生NHB的独立影响因素。人工神经网络模型对预测GDM孕妇分娩的新生儿发生NHB具有较高的效能,可为临床降低NHB发生风险提供依据。
Objective To explore the relationship between blood glucose control level of gestational diabetes mellitus(GDM)and the incidence of neonatal hyperbilirubinemia(NHB).Methods A total of 100 pregnant women with GDM treated in The Second People's Hospital of Huai′an from March 2017 to March 2021 were selected as the study subjects and included in the GDM group.Another 100 pregnant women without GDM during the same period,matched for gestational age and age at delivery,were selected into the control group.The GDM group was divided into the NHB group(n=30)and non-NHB group(n=70).The occurrence of complications was compared between the GDM group and the control group,and the relationship between blood glucose control level and the occurrence of NHB were analyzed.Multiple Logistic regression was used to analyze factors influencing the occurrence of NHB in newborns delivered by pregnant women with GDM.The diagnostic efficacy of blood glucose control level in the diagnosis of NHB was assessed using the area under the curve(AUC)of the receiver operating characteristic(ROC).Using Python 3.6 to build an artificial neural network model based on Softmax strategy,and evaluate the model.Results The incidence rates of NHB,macrosomia and neonatal distress complications in the GDM group were significantly higher than those in the control group,and the differences were statistically significant(χ2 values were 9.765,5.207 and 7.236,respectively,P<0.05).In the GDM group,21 pregnant women had fasting blood glucose levels>7.8mmol/L,among whom 11 newborns had NHB,accounting for 52.4%,37 pregnant women had fasting blood glucose levels between 6.1-7.8mmol/L,among whom 14 newborns had NHB,accounting for 37.8%,42 pregnant women had fasting blood glucose levels<6.1mmol/L,among whom 5 newborns had NHB,accounting for 11.9%.Univariate analysis showed that factors related to the occurrence of NHB included:pregnant women's fasting blood glucose before delivery and neonatal birth length(t values were 2.243 and 2.368,respectively),as well as neonatal Apgar 5-minute score<7 points,poor fasting blood glucose control,lack of breastfeeding,premature rupture of membranes,fetal asphyxia,neonatal infection,first meconium passage time>24 hours,premature infants,and initiation of breastfeeding>24 hours(χ2 values were 9.580,11.758,8.129,20.003,24.536,30.014,5.563,7.233 and 6.994,respectively).The differences between the NHB group and the non-NHB group were statistically significant(P<0.05).Multivariate Logistic regression analysis showed that premature rupture of membranes,poor fasting blood glucose control,fetal asphyxia,neonatal infection,prenatal fasting blood glucose,neonatal Apgar 5min score<7 points,premature infants,first meconium passage time>24 hours,lack of breastfeeding,and initiation of breastfeeding>24 hours were all independent risk factors for NHB.ROC curve analysis showed that the AUC of predicting neonatal NHB based on fasting blood glucose control level was 0.876,with a sensitivity of 86.9%and specificity of 78.3%.The artificial neural network model showed that premature rupture of membranes,poor fasting blood glucose control,fetal asphyxia and neonatal infection had relatively high weights.ROC curve analysis showed that AUC was 0.869(95%CI:0.795-0.947),with a sensitivity of 89.5%and specificity of 98.3%when the critical value was set at 0.627 for predicting NHB occurrence in newborns delivered by GDM pregnant women.Conclusion GDM increases the incidence of NHB,macrosomia and neonatal distress complications.The better the blood glucose control in pregnant women with GDM,the lower the incidence of NHB.Premature rupture of membranes,poor fasting blood glucose control,fetal asphyxia,neonatal infection,prenatal fasting blood glucose,neonatal Apgar 5-minute score<7,premature infants,first meconium passage time>24 hours,lack of breastfeeding,and initiation of breastfeeding>24 hours are independent influencing factors of NHB.The artificial neural network model has a high efficacy in predicting the occurrence of NHB in newborns delivered by GDM pregnant women,providing a basis for clinically reducing the risk of NHB.
作者
程丽
刘琼娜
蒋晶晶
CHENG Li;LIU Qiongna;JIANG Jingjing(Department of Gynecology and Obstetrics,The Second People's Hospital of Huai′an,Jiangsu Huai′an 223002,China)
出处
《中国妇幼健康研究》
2024年第2期64-73,共10页
Chinese Journal of Woman and Child Health Research
关键词
妊娠期糖尿病
血糖水平
新生儿
高胆红素血症
发病情况
gestational diabetes mellitus
blood glucose level
newborn
hyperbilirubinemia
morbidity
