Video Experimental Relacionado
Updated: May 7, 2026
Implantation of Combined Telemetric ECG and Blood Pressure Transmitters to Determine Spontaneous Baroreflex Sensitivity in Conscious Mice
Published on: February 14, 2021
Primer trimestre de aprendizaje automático para predecir la preeclampsia en embarazos normotensos según las
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.
