ArticlePaediatric and perinatal epidemiology2023
Machine learning-based placental clusters and their associations with adverse pregnancy outcomes.
Article in Paediatric and perinatal epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 6 citations in OpenAlex.
- Unsupervised Machine Learning for the Identification of Latent First-Trimester Obstetric Phenotypes Associated with Maternal and Perinatal Morbidity.Diagnostics (Basel, Switzerland) · 2026Article
- Placental Vascular Malperfusion, Perinatal Death and Neonatal Brain Injury: A Mechanism-Based Narrative Review with Medico-Legal Implications.Journal of clinical medicine · 2026Review
- Data science and artificial intelligence for maternal, newborn and child health: scoping review and thematic analysis.BMC public health · 2025Article
- Identification of distinct clinical profiles of sepsis risk in paediatric emergency department patients using Bayesian profile regression.BMJ paediatrics open · 2025Article
- Rigour and reproducibility in perinatal and paediatric epidemiologic research using big data.Paediatric and perinatal epidemiology · 2023Article
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6 authors at 3 institutions in 2 countries.
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Abstract
backgroundPlacental abnormalities have been described in clinical convenience samples, with predominately adverse outcomes. Few studies have described placental patterns in unselected samples.
objectiveWe aimed to investigate associations between co-occurring placental features and adverse pregnancy outcomes in a prospective cohort of singletons.
methodsData were from the Safe Passage study (U.S. and South Africa, 2007-2015). Before 24 weeks' gestation, participants were randomly invited to donate placental tissue at delivery for blinded, standardised pathological examination. We used hierarchical clustering to construct statistically derived groups using 60 placental features. We estimated associations between the placental clusters and select adverse pregnancy outcomes, expressed as unadjusted and adjusted risk ratios (RRs) and robust 95% confidence intervals (CI).
resultsWe selected a 7-cluster model. After collapsing 2 clusters to form the reference group, we labelled the resulting 6 analytic clusters according to the overarching category of their most predominant feature(s): severe maternal vascular malperfusion (n = 117), fetal vascular malperfusion (n = 222), other vascular malperfusion (n = 516), inflammation 1 (n = 269), inflammation 2 (n = 175), and normal (n = 706). Risks for all outcomes were elevated in the severe maternal vascular malperfusion cluster. For instance, in unadjusted analyses, this cluster had 12 times the risk of stillbirth (RR 12.07, 95% CI 4.20, 34.68) and an almost doubling in the risk of preterm delivery (RR 1.93, 95% CI 1.27, 2.93) compared with the normal cluster. Small infant size was more common among the abnormal clusters, with the highest unadjusted RRs observed in the fetal vascular malperfusion cluster (small for gestational age birth RR 2.99, 95% CI 2.24, 3.98, head circumference <10th percentile RR 2.86, 95% CI 1.60, 5.12). Upon adjustment for known risk factors, most RRs attenuated but remained >1.
conclusionOur study adds to the growing body of epidemiologic research, finding adverse pregnancy outcomes may occur through etiologic mechanisms involving co-occurring placental abnormalities.
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