Research Article Prediction of Preterm Birth by Maternal Characteristics and Medical History in the Brazilian Population Enio Luis Damaso , 1 Daniel Lober Rolnik , 2,3 Ricardo de Carvalho Cavalli, 1 Silvana Maria Quintana, 1 Geraldo Duarte, 1 Fabricio da Silva Costa , 1,2 and Alessandra Marcolin 1 1 Department of Gynecology and Obstetrics, Ribeirao Preto Medical School, University of Sao Paulo, Ribeirao Preto, Sao Paulo, Brazil 2 Department of Obstetrics and Gynaecology, Monash University, Melbourne, Victoria, Australia 3 School of Clinical Sciences, Monash University, Melbourne, Victoria, Australia Correspondence should be addressed to Alessandra Marcolin; dralemar@uol.com.br Received 17 February 2019; Revised 19 July 2019; Accepted 28 July 2019; Published 25 September 2019 Academic Editor: Cláudia Saunders Copyright © 2019 Enio Luis Damaso et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Objectives. e 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. is was a retrospective cohort study of women undergoing routine antenatal care. Maternal characteristics and medical history were obtained. e 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%,  = 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). 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. 1. Introduction Preterm birth (PTB), defined as a delivery that occurs before 37 weeks of gestation regardless of the newborn’s weight [1], is a significant cause of death in children below the age of five and the leading cause of early neonatal morbidity and mor- tality, particularly in developing countries [2, 3]. Additionally, PTB accounts for more than half of the long‐term morbidity, especially among children born before 34 weeks of gestation [4, 5]. Although there are potential strategies for PTB prevention, such as cervical cerclage [6, 7] and administration of proges- terone to patients with short cervix and/or history of PTB [8–11], significant declines in its rates have not been observed, which could be partially explained by the multifactorial etiol- ogy of PTB and by the inadequate selection of patients at increased risk [12]. In recent years, Bayes’ theorem statistical models based on logistic regression or competing risks for the prediction of various pregnancy complications have been developed, Hindawi Journal of Pregnancy Volume 2019, Article ID 4395217, 6 pages https://doi.org/10.1155/2019/4395217