Related Experiment Video
Updated: Jul 12, 2025
Author Spotlight: Modeling an Aspect of Preeclampsia in Female Mice Using Hypoxic Human Placenta-Derived Small Extracellular Vesicles
Published on: January 26, 2024
Preeclampsia Prediction Using Machine Learning and Polygenic Risk Scores From Clinical and Genetic Risk Factors in
Vesela P Kovacheva1, Braden W Eberhard1, Raphael Y Cohen1,2
1Department of Anesthesiology, Perioperative and Pain Medicine (V.P.K., B.W.E., R.Y.C.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Insights
Predicting preeclampsia risk is crucial. Machine learning models using clinical data show high accuracy in late pregnancy, though genetic risk scores did not significantly improve predictions.
Area of Science:
- Obstetrics and Gynecology
- Genetics
- Medical Informatics
Background:
- Preeclampsia is a serious pregnancy complication causing maternal and infant mortality.
- New predictive tools are needed to identify high-risk pregnancies.
Purpose of the Study:
- To develop and evaluate machine learning models for predicting preeclampsia risk.
- To assess the utility of polygenic risk scores in preeclampsia prediction.
Main Methods:
- A cohort of 1125 pregnant individuals was analyzed using electronic health record and genetic data.
- Machine learning (XGBoost) and logistic regression models were developed to predict preeclampsia.
- Systolic blood pressure polygenic risk scores were incorporated into the models.
Main Results:
- XGBoost models demonstrated strong predictive performance, achieving an AUC of 0.91 in late pregnancy using clinical variables.
- Individuals in the top quartile of systolic blood pressure polygenic risk score had higher blood pressure throughout pregnancy.
- Adding polygenic risk scores did not significantly enhance model prediction accuracy.
Conclusions:
- Integrating clinical factors into predictive models improves preeclampsia risk assessment.
- Personalized prediction tools can guide preventative therapies and interventions for better maternal and neonatal outcomes.
Background:
Preeclampsia, a pregnancy-specific condition associated with new-onset hypertension after 20-weeks gestation, is a leading cause of maternal and neonatal morbidity and mortality. Predictive tools to understand which individuals are most at risk are needed.
Methods:
We identified a cohort of N=1125 pregnant individuals who delivered between May 2015 and May 2022 at Mass General Brigham Hospitals with available electronic health record data and linked genetic data. Using clinical electronic health record data and systolic blood pressure polygenic risk scores derived from a large genome-wide association study, we developed machine learning (XGBoost) and logistic regression models to predict preeclampsia risk.
Results:
Pregnant individuals with a systolic blood pressure polygenic risk score in the top quartile had higher blood pressures throughout pregnancy compared with patients within the lowest quartile systolic blood pressure polygenic risk score. In the first trimester, the most predictive model was XGBoost, with an area under the curve of 0.74. In late pregnancy, with data obtained up to the delivery admission, the best-performing model was XGBoost using clinical variables, which achieved an area under the curve of 0.91. Adding the systolic blood pressure polygenic risk score to the models did not improve the performance significantly based on De Long test comparing the area under the curve of models with and without the polygenic score.
Conclusions:
Integrating clinical factors into predictive models can inform personalized preeclampsia risk and achieve higher predictive power than the current practice. In the future, personalized tools can be implemented to identify high-risk patients for preventative therapies and timely intervention to improve adverse maternal and neonatal outcomes.
Related Concept Videos
01:19Mitral Valve Prolapse III: Nursing Management
01:18Polygenic Traits
01:22Diabetes Mellitus: Type 2 and Gestational