Damaso Enio Luis, Rolnik Daniel Lober, Cavalli Ricardo de Carvalho, Quintana Silvana Maria, Duarte Geraldo, da Silva Costa Fabricio, Marcolin Alessandra
Department of Gynecology and Obstetrics, Ribeirao Preto Medical School, University of Sao Paulo, Ribeirao Preto, Sao Paulo, Brazil.
Department of Obstetrics and Gynaecology, Monash University, Melbourne, Victoria, Australia.
J Pregnancy. 2019 Sep 25;2019:4395217. doi: 10.1155/2019/4395217. eCollection 2019.
The aim of this study was to assess the performance of a previously published algorithm for first-trimester prediction of spontaneous preterm birth (PTB) in a cohort of Brazilian women.
This was a retrospective cohort study of women undergoing routine antenatal care. Maternal characteristics and medical history were obtained. The data were inserted in the Fetal Medicine Foundation (FMF) online calculator to estimate the individual risk of PTB. Univariate and multivariate logistic regression analyses were performed to determine the effects of maternal characteristics on the occurrence of PTB. A receiver-operating characteristics (ROC) curve was used to determine the detection rates and false-positive rates of the FMF algorithm in predicting PTB <34 weeks of gestation in our population.
In total, 1,323 women were included. Of those, 23 (1.7%) had a spontaneous PTB before 34 weeks of gestation, 87 (6.6%) had a preterm birth between 34 and 37 weeks, and 1,197 (91.7%) had a term delivery. Smoking and a previous history of recurrent PTB between 16 and 30 weeks of gestation without prior term pregnancy were significantly more common among women who delivered before 34 weeks of gestation compared to those who delivered at term were (39.1% vs. 12.0%, = 0.001 and 8.7% vs. 0%, < 0.001, respectively). Smoking and history of spontaneous PTB remained significantly associated with spontaneous PTB in the multivariate logistic regression analysis. Significant prediction of PTB <34 weeks of gestation was provided by the FMF algorithm (area under the ROC curve 0.67, 95% CI 0.56-0.78, = 0.005), but the detection rates for fixed false-positive rates of 10% and 20% were poor (26.1% and 34.8%, respectively).
Maternal characteristics and history in the first trimester can significantly predict the occurrence of spontaneous delivery before 34 weeks of gestation. Although the predictive algorithm performed similarly to previously published data, the detection rates are poor and research on new biomarkers to improve its performance is needed.
本研究旨在评估一种先前发表的算法在巴西女性队列中对孕早期自发性早产（PTB）的预测性能。
这是一项对接受常规产前检查的女性进行的回顾性队列研究。获取了产妇的特征和病史。将数据输入胎儿医学基金会（FMF）在线计算器以估计个体PTB风险。进行单因素和多因素逻辑回归分析以确定产妇特征对PTB发生的影响。采用受试者工作特征（ROC）曲线来确定FMF算法在预测我们人群中妊娠<34周的PTB时的检测率和假阳性率。
总共纳入了1323名女性。其中，23名（1.7%）在妊娠34周前发生自发性PTB，87名（6.6%）在34至37周之间早产，1197名（91.7%）足月分娩。与足月分娩的女性相比，妊娠34周前分娩的女性中，吸烟以及既往有16至30周复发性PTB且无足月妊娠史的情况明显更为常见（分别为39.1%对12.0%，P = 0.001；8.7%对0%，P < 0.001）。在多因素逻辑回归分析中，吸烟和自发性PTB病史仍与自发性PTB显著相关。FMF算法对妊娠<34周的PTB有显著预测作用（ROC曲线下面积为0.67，95%可信区间为0.56 - 0.78，P = 0.005），但固定假阳性率为10%和20%时的检测率较低（分别为26.1%和34.8%）。
孕早期的产妇特征和病史可显著预测妊娠34周前自发性分娩的发生。尽管该预测算法的表现与先前发表的数据相似，但检测率较低，需要对新的生物标志物进行研究以提高其性能。
