ArticlePloS one2021
Prediction of preterm birth in nulliparous women using logistic regression and machine learning.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06974188 (AI-Powered CURAᵀᴹ Application for Identifying At-Risk Pregnancies in Obstetric Management), which is not on this map. Cited by 30 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
AI-Powered CURAᵀᴹ Application for Identifying At-Risk Pregnancies in Obstetric Management: A Randomized Controlled Trial (CURAte)
Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Prognostic prediction models for adverse birth outcomes: A systematic review.Journal of global health · 2024Pooled it
- Reporting and risk of bias of prediction models based on machine learning methods in preterm birth: A systematic review.Acta obstetricia et gynecologica Scandinavica · 2023Pooled it
- Machine learning to predict adverse perinatal outcomes: a systematic review and meta-analysis.EClinicalMedicine · 2026Article
- Artificial Intelligence for Neonatal and Perinatal Mortality Prevention: A Systematic Review of Machine Learning and Deep Learning Applications.Healthcare (Basel, Switzerland) · 2026Review
- High-risk pregnancy prediction using Taguchi-optimized machine learning methods and TOPSIS-based model selection.Scientific reports · 2026Article
- Cost-Effectiveness of Fetal Fibronectin Testing in Women at Risk for Preterm Birth: A Retrospective Cohort Study.ClinicoEconomics and outcomes research : CEOR · 2026Article
- Early prediction of very and extreme preterm births using a one-class classification framework on electronic health records in UAE.Scientific reports · 2025Article
- Predicting the risk of preterm birth with machine learning and electronic health records in China.BMC medical informatics and decision making · 2025Article
- Multidimensional predictors of preterm birth risk among black and white primiparous women in the U.S.: insights from machine learning.BMC pregnancy and childbirth · 2025Article
- An AI method to predict pregnancy loss by extracting biological indicators from embryo ultrasound recordings in early pregnancy.Scientific reports · 2025Article
- Machine learning prediction of preterm birth in women under 35 using routine biomarkers in a retrospective cohort study.Scientific reports · 2025Article
- Prediction of Preterm Birth among Infants with Orofacial Cleft Defects.The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association · 2025Article
- Evaluating how different balancing data techniques impact on prediction of premature birth using machine learning models.PloS one · 2025Article
- Predicting preterm birth using electronic medical records from multiple prenatal visits.BMC pregnancy and childbirth · 2024Article
- Prediction of preterm birth using machine learning: a comprehensive analysis based on large-scale preschool children survey data in Shenzhen of China.BMC pregnancy and childbirth · 2024Article
- Developing a logistic regression model to predict spontaneous preterm birth from maternal socio-demographic and obstetric history at initial pregnancy registration.BMC pregnancy and childbirth · 2024Article
- Preterm birth risk stratification through longitudinal heart rate and HRV monitoring in daily life.Scientific reports · 2024Article
- Black-white differences in chronic stress exposures to predict preterm birth: interpretable, race/ethnicity-specific machine learning model.BMC pregnancy and childbirth · 2024Article
- Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms.Frontiers in big data · 2024Article
- Node embedding-based graph autoencoder outlier detection for adverse pregnancy outcomes.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
objectiveTo predict preterm birth in nulliparous women using logistic regression and machine learning.
designPopulation-based retrospective cohort.
participantsNulliparous women (N = 112,963) with a singleton gestation who gave birth between 20-42 weeks gestation in Ontario hospitals from April 1, 2012 to March 31, 2014.
methodsWe used data during the first and second trimesters to build logistic regression and machine learning models in a "training" sample to predict overall and spontaneous preterm birth. We assessed model performance using various measures of accuracy including sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) in an independent "validation" sample.
resultsDuring the first trimester, logistic regression identified 13 variables associated with preterm birth, of which the strongest predictors were diabetes (Type I: adjusted odds ratio (AOR): 4.21; 95% confidence interval (CI): 3.23-5.42; Type II: AOR: 2.68; 95% CI: 2.05-3.46) and abnormal pregnancy-associated plasma protein A concentration (AOR: 2.04; 95% CI: 1.80-2.30). During the first trimester, the maximum AUC was 60% (95% CI: 58-62%) with artificial neural networks in the validation sample. During the second trimester, 17 variables were significantly associated with preterm birth, among which complications during pregnancy had the highest AOR (13.03; 95% CI: 12.21-13.90). During the second trimester, the AUC increased to 65% (95% CI: 63-66%) with artificial neural networks in the validation sample. Including complications during the pregnancy yielded an AUC of 80% (95% CI: 79-81%) with artificial neural networks. All models yielded 94-97% negative predictive values for spontaneous PTB during the first and second trimesters.
conclusionAlthough artificial neural networks provided slightly higher AUC than logistic regression, prediction of preterm birth in the first trimester remained elusive. However, including data from the second trimester improved prediction to a moderate level by both logistic regression and machine learning approaches.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.