Key result
Machine learning-identified high-risk hemodynamic cluster predicts ~25% preeclampsia incidence in first-trimester normotensives.
Why the study?
To determine whether unsupervised machine learning can identify phenotypically distinct subgroups at increased risk for preeclampsia among pregnant individuals with AHA-defined normal first-trimester blood pressure.
Does unsupervised machine learning applied to first-trimester blood pressure identify subgroups at increased risk for preeclampsia among normotensive pregnant individuals?
Does unsupervised machine learning applied to first-trimester blood pressure identify subgroups at increased risk for preeclampsia among normotensive pregnant individuals?
Unsupervised machine learning applied to first-trimester blood pressure can identify pregnant individuals at high risk for preeclampsia despite having AHA-defined normal blood pressure.
Answers are grounded in this paper and cite their sources.
May flag normotensive women for closer preeclampsia surveillance; hypothesis-generating and should not yet change practice.
Objective: To determine whether unsupervised machine learning can identify phenotypically distinct subgroups at increased risk for preeclampsia among pregnant individuals with American Heart Association (AHA)-defined normal blood pressure in the first trimester. Methods: This was a secondary analysis of a prospective cohort study of singleton pregnancies enrolled at ≤13+6 weeks’ gestation at two academic centers. Participants with pre-pregnancy chronic hypertension or major fetal/placental abnormalities were excluded. First-trimester blood pressure was categorized using 2017 AHA guidelines. Among individuals with AHA-defined normal blood pressure (<120/80 mmHg), unsupervised machine learning (k-means clustering) was applied to systolic, diastolic, and mean arterial pressure to identify distinct hemodynamic phenotypes. The primary outcome was preeclampsia; secondary outcomes included hypertensive disorders of pregnancy (HDP) and small-for-gestational-age (SGA) neonates. Associations were assessed using multivariable Cox regression and Kaplan-Meier analysis. Results: Of 570 participants, 378 (66.3%) had AHA-normal blood pressure. Among these, machine learning identified a high-risk cluster (7.4%) and a low-risk cluster (92.6%). Despite normotensive values, individuals in the high-risk cluster had a significantly higher incidence of preeclampsia (25.0% vs. 3.1%; P < .001) and HDP (28.6% vs. 5.7%; P < .001) compared to the low-risk cluster. After adjustment, the high-risk normotensive cluster had an 8-fold increased hazard of preeclampsia (aHR 8.01; 95% CI 3.09–20.74) and increased risk of SGA (aOR 3.36; 95% CI 1.36–8.31). Risk within this group exceeded that of individuals with AHA-abnormal blood pressure. Conclusion: Among pregnant individuals with first-trimester AHA-normal blood pressure, unsupervised clustering identified a distinct subgroup at elevated risk for preeclampsia and SGA. These findings suggest that conventional thresholds may overlook early vascular risk and support further investigation into machine learning–based risk stratification in pregnancy.
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Horgan et al. (2026) studied this question. Among first-trimester normotensive individuals, a high-risk cluster identified by machine learning had a preeclampsia incidence of 25% compared to 3.1% in the low-risk group (P < .001).
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