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Am J Perinatol
DOI: 10.1055/a-2781-6377
Original Article

First-Trimester Machine Learning to Predict Preeclampsia in Normotensive Pregnancies by American Heart Association Guidelines

Authors

  • Rebecca Horgan 

    1   Eastern Virginia Medical School, Old Dominion University, Norfolk, Virginia, United States

  • Erkan Kalafat

    2   Department of Obstetrics & Gynecology, Koc University School of Medicine, Istanbul, Türkiye

  • Elena Sinkovskaya

    1   Eastern Virginia Medical School, Old Dominion University, Norfolk, Virginia, United States

  • Alfred Z. Abuhamad

    1   Eastern Virginia Medical School, Old Dominion University, Norfolk, Virginia, United States

  • George Saade

    1   Eastern Virginia Medical School, Old Dominion University, Norfolk, Virginia, United States

Funding Information The study was funded by the U.S. Department of Health and Human Services, National Institutes of Health, Eunice Kennedy Shriver National Institute of Child Health and Human Development (grant no.: HD08631301).
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Abstract

Objective

This study aimed 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 ≤136/7 weeks' gestation at two academic centers. Participants with prepregnancy chronic hypertension or major fetal/placental abnormalities were excluded. First-trimester blood pressure was categorized using the 2017 AHA guidelines. Among individuals with AHA-defined normal blood pressure (<120/80 mm Hg), 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 analyses.

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 < 0.001) and HDP (28.6 vs. 5.7%; p < 0.001) compared to the low-risk cluster. After adjustment, the high-risk normotensive cluster had an eight-fold increased hazard of preeclampsia (adjusted hazard ratio [aHR] = 8.01; 95% CI: 3.09–20.74) and increased risk of SGA (adjusted odds ratio [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.

Key Points

  • Machine learning identified a distinct high-risk cluster (7.4%) within normotensive pregnancies.

  • This cluster had an eight-fold higher risk of preeclampsia and a three-fold increased risk of SGA neonate.

  • Machine learning may enhance early pregnancy risk stratification.

Keywords

preeclampsia - hypertensive disorders of pregnancy - blood pressure phenotype - machine learning - small for gestational age - risk stratification - American Heart Association - Pregnancy - hypertension

Data Availability Statement

Data are available upon request from the authors.


Contributors' Statement

R.H.: Data curation, writing–original draft. E.K.: Formal analysis. E.S.: Data curation, investigation. A.Z.A.: Conceptualization, funding acquisition, investigation, supervision, writing–review and editing. G.S.: Conceptualization, funding acquisition, investigation, supervision, writing–review and editing.




Publication History

Received: 08 December 2025

Accepted: 30 December 2025

Accepted Manuscript online:
12 January 2026

Article published online:
29 January 2026

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