ArticlePloS one2019
Clinical risk assessment in early pregnancy for preeclampsia in nulliparous women: A population based cohort study.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 3 syntheses or guidelines pooled it, 52 citations in OpenAlex.
- Assisted Reproductive Technology as Predictor for Preeclampsia: A Systematic Review of Current Clinical Prediction Models.Reproductive sciences (Thousand Oaks, Calif.) · 2026Pooled it
- Machine Learning Prediction Models for Preeclampsia: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Machine Learning Algorithms Versus Classical Regression Models in Pre-Eclampsia Prediction: A Systematic Review.Current hypertension reports · 2024Pooled it
- Construction and comparison of preeclampsia prediction models using logistic regression and machine learning.BMC medical informatics and decision making · 2026Article
- A novel approach to preeclampsia early prediction addressing predictive uncertainty due to missing data in clinical dataset.Scientific reports · 2026Article
- Validation of a machine-learning-based algorithm to predict preeclampsia-related adverse outcomes on a real-world dataset.Archives of gynecology and obstetrics · 2026Article
- Machine learning predictive system to predict the risk of developing pre-eclampsia.BMJ health & care informatics · 2025Article
- Preeclampsia screening and prevention-A Nordic perspective.Acta obstetricia et gynecologica Scandinavica · 2025Article
- Preeclampsia prediction and diagnosis: a comprehensive historical review from clinical insights to omics perspectives.Frontiers in medicine · 2025Review
- Preeclampsia prediction via machine learning: a systematic literature review.Health systems (Basingstoke, England) · 2025Review
- Risk Assessment for Preeclampsia in the Preconception Period Based on Maternal Clinical History via Machine Learning Methods.Journal of clinical medicine · 2024Article
- Using machine learning to predict the risk of developing hypertensive disorders of pregnancy using a contemporary nulliparous cohort.AJOG global reports · 2024Article
- Identifying Predictor Variables for a Composite Risk Prediction Tool for Gestational Diabetes and Hypertensive Disorders of Pregnancy: A Modified Delphi Study.Healthcare (Basel, Switzerland) · 2024Article
- Novel Associations Between Mid-Pregnancy Cardiovascular Biomarkers and Preeclampsia: An Explorative Nested Case-Control Study.Reproductive sciences (Thousand Oaks, Calif.) · 2024Article
- Predicting the onset of preeclampsia by longitudinal monitoring of metabolic changes throughout pregnancy with Raman spectroscopy.Bioengineering & translational medicine · 2024Article
- New advances in prediction and surveillance of preeclampsia: role of machine learning approaches and remote monitoring.Archives of gynecology and obstetrics · 2023Review
- Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications.Frontiers in endocrinology · 2023Review
- Pre-pregnancy body mass index combined with peripheral blood PLGF, DCN, LDH, and UA in a risk prediction model for pre-eclampsia.Frontiers in endocrinology · 2023Article
- Development of early prediction model for pregnancy-associated hypertension with graph-based semi-supervised learning.Scientific reports · 2022Article
- Waist Circumference Measurement for Prediction of Preeclampsia: A Population-Based Cohort Study.American journal of hypertension · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate the capacity of multivariable prediction of preeclampsia during pregnancy, based on detailed routinely collected early pregnancy data in nulliparous women. DESIGN AND
settingA population-based cohort study of 62 562 pregnancies of nulliparous women with deliveries 2008-13 in the Stockholm-Gotland Counties in Sweden.
methodsMaternal social, reproductive and medical history and medical examinations (including mean arterial pressure, proteinuria, hemoglobin and capillary glucose levels) routinely collected at the first visit in antenatal care, constitute the predictive variables. Predictive models for preeclampsia were created by three methods; logistic regression models using 1) pre-specified variables (similar to the Fetal Medicine Foundation model including maternal factors and mean arterial pressure), 2) backward selection starting from the full suite of variables, and 3) a Random forest model using the same candidate variables. The performance of the British National Institute for Health and Care Excellence (NICE) binary risk classification guidelines for preeclampsia was also evaluated. The outcome measures were diagnosis of preeclampsia with delivery <34, <37, and ≥37 weeks' gestation.
resultsA total of 2 773 (4.4%) nulliparous women subsequently developed preeclampsia. The pre-specified variables model was superior the other two models, regarding prediction of preeclampsia with delivery <34 and <37 weeks, both with areas under the curve of 0.68, and sensitivity of 30.6% (95% CI 24.5-37.2) and 29.2% (95% CI 25.2-33.4) at a 10% false positive rate, respectively. The performance of these customizable multivariable models at the chosen false positive rate, was significantly better than the binary NICE-guidelines for preeclampsia with delivery <37 and ≥37 weeks' gestation.
conclusionMultivariable models in early pregnancy had a modest performance, although providing advantages over the NICE-guidelines, in predicting preeclampsia in nulliparous women. Use of a machine learning algorithm (Random forest) did not result in superior prediction.
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