摘要
【目的】通过分析双胎妊娠分娩者的相关资料,构建Logistic回归模型,以对双胎妊娠早产的高危因素进行筛选和预测,降低双胎妊娠早产的风险。【方法】回顾性分析本院分娩的215例双胎妊娠产妇的临床资料,按照分娩时间不同分为早产组(102例)与足月组(113例)。采用单因素分析早产的相关因素,将有统计学差异的因素纳入Logistic回归分析,建立双胎妊娠早产的Logistic回归模型,并验证其预测效能。【结果】单因素分析显示,两组产妇的体重指数(BMI)、文化程度、孕次,妊娠期高血压、妊娠期糖尿病发生率,孕中期子宫颈长度和胎膜早破发生率比较,差异均有统计学意义(P<0.05)。Logistic回归分析显示,BMI高、孕次少、文化程度低、胎膜早破、有妊娠期高血压和妊娠期糖尿病、孕中期子宫颈长度短是双胎妊娠早产的独立危险因素。受试者工作特征(ROC)曲线分析显示,预测模型的灵敏度是80.4%,特异度是92.0%,曲线下面积为0.940,95%CI为0.910~0.970。【结论】本研究建立了一个可有效预测双胎妊娠早产风险的Logistic回归模型,可对双胎妊娠孕妇孕期及围产期情况进行评估,确定早产的高危因素,对于双胎妊娠早产风险的预防具有重要意义。
【Objective】By analyzing the relevant data of twin pregnancy delivery patients,a logistic regression model is constructed to screen and predict high-risk factors for premature birth in twin pregnancy,and to reduce the risk of premature birth in twin pregnancy.【Methods】A retrospective analysis was conducted on the clinical data of 215 twin pregnant women delivered in our hospital.They were divided into premature delivery group(102 cases)and full-term group(113 cases)according to different delivery times.A single factor analysis was used to identify factors related to preterm birth,and factors with statistical differences were included in the logistic regression analysis.A logistic regression model for twin pregnancy preterm birth was established,and its predictive performance was verified.【Results】Univariate analysis showed that there were significant differences between the two groups in body mass index(BMI),education level,pregnancy times,the incidence of pregnancy hypertension,pregnancy diabetes,the length of cervix in the second trimester and the incidence of premature rupture of membranes(P<0.05).Logistic regression showed that high BMI,low pregnancy times,low education level,premature rupture of membranes,pregnancy hypertension and pregnancy diabetes,and low cervical length in the second trimester were independent risk factors for preterm twin pregnancy.The receiver operating characteristic(ROC)curve analysis showed that the sensitivity of the predictive model was 80.4%,the specificity was 92.0%,the area under the curve was 0.940,and the 95%CI was 0.910-0.970.【Conclusion】This study established a logistic regression model that can effectively predict the risk of premature birth in twin pregnancies.It can evaluate the pregnancy and perinatal conditions of twin pregnant women,identify high-risk factors for premature birth,and is of great significance for the prevention of premature birth risk in twin pregnancies.
作者
赵佳文
赵予颖
常艳玲
ZHAO Jiawen;ZHAO Yuying;CHANG Yanling(Department of Obstetrics,The Third Affiliated Hospital of Zhengzhou University,Zhengzhou Henan 450000)
出处
《医学临床研究》
CAS
2024年第4期558-561,共4页
Journal of Clinical Research
