Prediction of Preterm Birth by Maternal Characteristics and Medical History in the Brazilian Population

Objectives. 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. Methods. This was a retrospective cohort study of women undergoing routine antenatal care. Maternal ch...

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Main Authors: Enio Luis Damaso, Daniel Lober Rolnik, Ricardo de Carvalho Cavalli, Silvana Maria Quintana, Geraldo Duarte, Fabricio da Silva Costa, Alessandra Marcolin
Format: Article
Language:English
Published: Wiley 2019-01-01
Series:Journal of Pregnancy
Online Access:http://dx.doi.org/10.1155/2019/4395217
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author Enio Luis Damaso
Daniel Lober Rolnik
Ricardo de Carvalho Cavalli
Silvana Maria Quintana
Geraldo Duarte
Fabricio da Silva Costa
Alessandra Marcolin
author_facet Enio Luis Damaso
Daniel Lober Rolnik
Ricardo de Carvalho Cavalli
Silvana Maria Quintana
Geraldo Duarte
Fabricio da Silva Costa
Alessandra Marcolin
author_sort Enio Luis Damaso
collection DOAJ
description Objectives. 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. Methods. 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. Results. 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%, p=0.001 and 8.7% vs. 0%, p<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, p=0.005), but the detection rates for fixed false-positive rates of 10% and 20% were poor (26.1% and 34.8%, respectively). Conclusions. 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.
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spelling doaj-art-fe786bd1f831491e9f3564c7e9e64a2b2025-02-03T06:11:20ZengWileyJournal of Pregnancy2090-27272090-27352019-01-01201910.1155/2019/43952174395217Prediction of Preterm Birth by Maternal Characteristics and Medical History in the Brazilian PopulationEnio Luis Damaso0Daniel Lober Rolnik1Ricardo de Carvalho Cavalli2Silvana Maria Quintana3Geraldo Duarte4Fabricio da Silva Costa5Alessandra Marcolin6Department of Gynecology and Obstetrics, Ribeirao Preto Medical School, University of Sao Paulo, Ribeirao Preto, Sao Paulo, BrazilDepartment of Obstetrics and Gynaecology, Monash University, Melbourne, Victoria, AustraliaDepartment of Gynecology and Obstetrics, Ribeirao Preto Medical School, University of Sao Paulo, Ribeirao Preto, Sao Paulo, BrazilDepartment of Gynecology and Obstetrics, Ribeirao Preto Medical School, University of Sao Paulo, Ribeirao Preto, Sao Paulo, BrazilDepartment of Gynecology and Obstetrics, Ribeirao Preto Medical School, University of Sao Paulo, Ribeirao Preto, Sao Paulo, BrazilDepartment of Gynecology and Obstetrics, Ribeirao Preto Medical School, University of Sao Paulo, Ribeirao Preto, Sao Paulo, BrazilDepartment of Gynecology and Obstetrics, Ribeirao Preto Medical School, University of Sao Paulo, Ribeirao Preto, Sao Paulo, BrazilObjectives. 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. Methods. 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. Results. 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%, p=0.001 and 8.7% vs. 0%, p<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, p=0.005), but the detection rates for fixed false-positive rates of 10% and 20% were poor (26.1% and 34.8%, respectively). Conclusions. 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.http://dx.doi.org/10.1155/2019/4395217
spellingShingle Enio Luis Damaso
Daniel Lober Rolnik
Ricardo de Carvalho Cavalli
Silvana Maria Quintana
Geraldo Duarte
Fabricio da Silva Costa
Alessandra Marcolin
Prediction of Preterm Birth by Maternal Characteristics and Medical History in the Brazilian Population
Journal of Pregnancy
title Prediction of Preterm Birth by Maternal Characteristics and Medical History in the Brazilian Population
title_full Prediction of Preterm Birth by Maternal Characteristics and Medical History in the Brazilian Population
title_fullStr Prediction of Preterm Birth by Maternal Characteristics and Medical History in the Brazilian Population
title_full_unstemmed Prediction of Preterm Birth by Maternal Characteristics and Medical History in the Brazilian Population
title_short Prediction of Preterm Birth by Maternal Characteristics and Medical History in the Brazilian Population
title_sort prediction of preterm birth by maternal characteristics and medical history in the brazilian population
url http://dx.doi.org/10.1155/2019/4395217
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