未足月胎膜早破患者新生儿呼吸系统病变预测  

Prediction of neonatal respiratory diseases in patients with preterm premature rupture of membranes

作　　者：陈小斌 黄岭[1] 乔宠[1] Chen Xiaobin;Huang Ling;Qiao Chong(Department of Obstetrics and Gynecology,Shengjing Hospital,China Medical University,Shenyang 110004)

机构地区：[1]中国医科大学附属盛京医院妇产科,沈阳110004

出　　处：《现代妇产科进展》2018年第11期830-832,836,共4页Progress in Obstetrics and Gynecology

基　　金：国家重点研发计划"生殖健康及重大出生缺陷防控研究"重点项目(No:2016YFC1000404);国家自然科学基金资助项目(No:81771610);国家自然科学基金资助项目(No:81370735);盛京自由研究者基金(No:201706)

摘　　要：目的:建立未足月胎膜早破(PPROM)患者新生儿呼吸系统病变预测模型Nomogram图,为新生儿早期预防提供可靠依据。方法:回顾分析2015年11月～2018年2月于中国医科大学附属盛京医院住院分娩的孕28～36^(+6)周的胎膜早破(PROM)患者176例,其中并发呼吸系统病变者50例(病例组),未并发呼吸系统病变者126例(对照组)。采用SPSS 22.0建立logistics回归预测模型,采用ROC曲线检验其区分度、Hosmer-Lemeshow检验其校准度评估预测模型,利用R语言绘制Nomogram图。结果:PPROM新生儿呼吸系统病变预测模型:logit P=19.770+0.046~*Age-0.620~*Week-0.007~*Time+0.109~*WBC1+0.046~*NE1-0.011~*CRP1-0.120~*WBC2-0.060~*NE2+0.023~*CRP2。将预测模型预测概率建立ROC曲线,其AUC为0.831,表明该模型的区分度较好,诊断效果较好,Hosmer-Lemeshow检验发现该预测模型具有较好的校准能力。R语言建立Nomogram图,能迅速通过患者年龄、分娩孕周、破膜时间、入院时和分娩前24h内WBC计数、NE百分比和CRP水平评估新生儿呼吸系统病变的发生率。结论:联合PPROM患者年龄、分娩孕周、破膜时间、入院时和分娩前24h内WBC计数、NE百分比和CRP水平等指标建立有效预测模型并绘制Nomogram图,有助于对PPROM患者新生儿呼吸系统病变发生率进行早期诊断评估。Objective:To establish a Nomogram for a predictive model of neonatal respiratory disease of preterm premature rupture of membranes(PPROM),which can provide a reliable basis for early neonatal prevention.Methods:176 patients with premature rupture of membranes who met the standard of 28 to 36+6 weeks were retrospectively analyzed in the Department of Obstetrics and Gynecology,Shengjing Hospital of China Medical University from Nov.2015 to Feb.2018.According to whether the newborn had respiratory disease,50 cases with respiratory disease were included in the case group,and 126 cases without respiratory disease were included in the control group.Basic indicators of hospitalized pregnant women and laboratory indicators were included and detected,such as maternal age,Pregnancy week,time of membrane rupture time,the index of WBC count,NE percentage,and CRP level were integrated at admission time and 24 hours before delivery.The logistic regression model was established using in SPSS 22.0,and the ROC curve was used to test the discrimination of the prediction model.The Hosmer-Lemeshow test was used to evaluate the prediction model and the Nomogram was plotted in R language.Results:The predictive model of neonatal respiratory disease in PPROM is:logit P=19.770+0.046*Age-0.620*Week-0.007*Time+0.109*WBC1+0.046*NE1-0.011*CRP1-0.120*WBC2-0.060*NE2+0.023*CRP2.The ROC curve was established by predicting the prediction model's probabilities.The area under the curve was 0.831,indicating that the model had good discrimination and good diagnostic results.The Hosmer-Lemeshow test showed that the model had better calibration ability.R language to establish a Nomogram can quickly assess the incidence of histological chorioamnionitis through maternal age,membrane rupture time,the index of WBC count,NE percentage,and CRP level integrated at admission time and 24 hours before delivery.Conclusion:Establishing an effective predictive model and mapping a Nomogram for the combination of maternal age,Pregnancy week,time of membrane rupture,t

关 键 词：未足月胎膜早破 新生儿预后 Logistics回归模型 Nomogram图 

分 类 号：R71[医药卫生—妇产科学]