ArticleJournal of clinical medicine2023
Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers.
Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- Machine Learning Prediction Models for Preeclampsia: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- NT-proBNP and BNP as Biomarkers for Preeclampsia: A Systematic Review and Meta-Analysis.International journal of molecular sciences · 2025Pooled it
- World Pre-eclampsia Day: are we moving in the right direction?Singapore medical journal · 2026Article
- Angiogenic Imbalance Defines Multisystem Phenotypes of Preeclampsia: A Phenotype-Oriented Cohort Study.Clinics and practice · 2026Article
- NT-proBNP as a Predictive and Prognostic Biomarker for Complications in Hypertensive Pregnancy Disorders.Journal of clinical medicine · 2026Article
- Cardiovascular biomarkers and preeclampsia: A narrative review.European journal of clinical investigation · 2026Review
- Prognostic value of maternal N-terminal pro-B-type natriuretic peptide for prediction of severe maternal adverse outcome in women with pre-eclampsia.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2025Article
- Emerging roles of natural killer cell ligands-HLA-E, HLA-F, HLA-G, MICA, and MICB-inFrontiers in genetics · 2025Review
- Preeclampsia prediction via machine learning: a systematic literature review.Health systems (Basingstoke, England) · 2025Review
- Multidimensional roles of cfDNA fragmentomics in preeclampsia: from placental hypoxia and TLR9 inflammation to clinical risk stratification.Frontiers in medicine · 2025Review
- Research Progress on the Relationship Between Serum Uric Acid Levels and Coagulation Dysfunction in Preeclampsia.International journal of women's health · 2025Review
- A Prospective Study on Risk Prediction of Preeclampsia Using Bi-Platform Calibration and Machine Learning.International journal of molecular sciences · 2024Article
- Review
- Genetic Associations of Circulating Cardiovascular Proteins With Gestational Hypertension and Preeclampsia.JAMA cardiology · 2024Article
- Cardiac function in pregnant women with preeclampsia.Frontiers in cardiovascular medicine · 2024Article
- Recent Advances in the Prevention and Screening of Preeclampsia.Journal of clinical medicine · 2023Review
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
Abstract
N-terminal pro-brain natriuretic peptide (NT-proBNP) and uric acid are elevated in pregnancies with preeclampsia (PE). Short-term prediction of PE using angiogenic factors has many false-positive results. Our objective was to validate a machine-learning model (MLM) to predict PE in patients with clinical suspicion, and evaluate if the model performed better than the sFlt-1/PlGF ratio alone. A multicentric cohort study of pregnancies with suspected PE between 24+0 and 36+6 weeks was used. The MLM included six predictors: gestational age, chronic hypertension, sFlt-1, PlGF, NT-proBNP, and uric acid. A total of 936 serum samples from 597 women were included. The PPV of the MLM for PE following 6 weeks was 83.1% (95% CI 78.5−88.2) compared to 72.8% (95% CI 67.4−78.4) for the sFlt-1/PlGF ratio. The specificity of the model was better; 94.9% vs. 91%, respectively. The AUC was significantly improved compared to the ratio alone [0.941 (95% CI 0.926−0.956) vs. 0.901 (95% CI 0.880−0.921), p < 0.05]. For prediction of preterm PE within 1 week, the AUC of the MLM was 0.954 (95% CI 0.937−0.968); significantly greater than the ratio alone [0.914 (95% CI 0.890−0.934), p < 0.01]. To conclude, an MLM combining the sFlt-1/PlGF ratio, NT-proBNP, and uric acid performs better to predict preterm PE compared to the sFlt-1/PlGF ratio alone, potentially increasing clinical precision.
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Registered trials
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