高龄孕妇无创性唐氏综合征筛查模型及评分系统的建立

 目的  建立高龄孕妇 (advanced maternal age,AMA)无创性唐氏综合征 (Down′s syndrome,DS)的筛查模型及评分系统。方法  收集2009年1月至2014年1月期间在复旦大学附属妇产科医院建卡产检并分娩且病历资料及随访结果完整的AMA (分娩年龄≥35周岁)1 192例。记录孕妇年龄、有无不良孕产史、有无孕早期药物等不良因素暴露史、孕早期妊娠相关的血浆蛋白A (pregnancy-associated plasma protein A,PAPP-A)和人绒毛膜促性腺激素β-亚单位 (β-human chorionic gonadotropin, β-HCG)检测及超声测定颈项透明层 (nuchal translucency,NT)厚度、孕中期唐氏三联血清学筛查及孕中期遗传学超声检查结果，并随访妊娠结局。通过Logistic单因素及多因素回归分析得出对AMA唐氏阳性有影响的因素,据此建立AMA无创性DS产前筛查模型及评分系统。应用受试者工作特征曲线 (ROC曲线)及其曲线下面积 (AUC)显示自建模型预测唐氏风险的准确性。结果  1 192例AMA中唐氏患儿有6例。对1 192例AMA进行单因素及多因素Logistic回归分析,筛选出7个对DS疾病预测有意义的指标:孕妇年龄、孕早期药物等不良因素暴露史、胎儿侧脑室增宽、颈后皮肤皱褶（nuchal fold,NF）增厚、股骨短小、肱骨短小、心脏畸形。自建模型:Logit (P)=-24.595+0.474×年龄+3.045×孕早期药物等不良因素暴露史+2.864×胎儿侧脑室增宽+1.081×NF增厚+2.823×股骨短小-0.602×肱骨短小+3.654×心脏畸形（P:患病风险）。自建模型对AMA唐氏风险预测的AUC达到了0.8以上,显示出自建模型对AMA唐氏风险预测有较好的价值。结论  自建模型对AMA唐氏风险有一定的预测价值。

The establishment of a non-invasive screening model and scoring system for Down′s syndrome of advanced maternal age

Objective  To establish a prenatal screening model and scoring system for Down′s syndrome (DS) of advanced maternal age (AMA).Methods  A retrospective analysis was made in 1 192 cases of AMA who made prenatal examination and delivered in Obstetrics and Gynecology Hospital,Fudan University from Jan.,2009 to Jan.,2014.Maternal age,adverse pregnancy history,exposure history of drugs and othe adverse factors during the early pregnancy,DS screening result in the first trimester including pregnancy-associated plasma protein A (PAPP-A) and β-human chorionic gonadotropin (β-HCG) testing and ultrasonic testing of nuchal translucency (NT),second-trimester DS triple serological screening results and second-trimester genetic sonography results were recorded,and pregnancy outcomes were also registered.The possible influential factors assciated to DS positive result of AMA were selected by univariate and multivariate Logistic regression analysis,and then non-invasive prenatal screening model and scoring system for DS was established.Receiver operating characteristics curve (ROC curve) and the area under ROC curve (AUC) were used to test the accuracy of the self-built model for the predicting the risk of DS. Results  Six cases of DS were diagnosed in 1 192 cases of AMA.By univariate and multivariate Logistic regression analysis in 1 192 cases of AMA,7 meaningful indicators were filtered out for prediction of DS:maternal age,exposure history of drugs and other adverse factors during the first trimester,fetal ventriculomegaly,thickened nuchal fold (NF),shortened femur,shortened humerus and heart malformations.The self-built model was:Logit (P)=-24.595+0.474×age+3.045×exposure history of drugs and other adverse factors during the first trimester+2.864×fetal ventriculomegaly+1.081×thickened NF+2.823×shortened femur-0.602×shortened humerus+3.654×heart malformations (P:Probability of having disease).The AUC of self-built model for predicting the risk of DS in AMA was over 0.8,indicating that self-built model have good predictive value for predicting the risk of DS in AMA. Conclusions  The self-built model has some predictive value for the risk of DS in AMA.