正常血圧妊娠における子癇前症予測のための第一三半期機械学習：米国心臓協会ガイドラインによる
Rebecca Horgan1, Erkan Kalafat2, Elena Sinkovskaya3
1Maternal Fetal Medicine, Eastern Virginia Medical School, Norfolk, United States.
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.
