Improving preeclampsia risk prediction by modeling pregnancy trajectories from routinely collected electronic medical record data

In this study, we explored modeling the risk of preeclampsia by reconstructing pregnancy journeys across tens of thousands of pregnancies through electronic medical record (EMR) data in addition to pre-and post-pregnancy data. Preeclampsia, a pregnancy complication, has been a leading cause of maternal mortality in the U.S. over the past two decades,3 and can lead to serious complications for both the mother and the fetus.  The mechanisms underlying preeclampsia have not been fully recognized and the only treatment for this condition is delivery. Thus, a personalized, precision medicine approach is needed to characterize preeclampsia risk and identify patients at risk of this condition earlier to better monitor, manage, and optimize therapeutic strategies, improve clinical outcomes, and lower adverse events.

The models we developed leverage dynamic characteristics along the pregnancy journey, capturing predictive features based on longitudinal data across each time protocol visit at the antepartum, intrapartum, and postpartum stages. One of the key findings across the three pregnancy stages was that systolic blood pressure (SBP) predicts the risk of preeclampsia at 130 mmHg, which contrasts with the diagnostic guidelines of 140 mmHg defined by American College of Obstetricians and Gynecologists (ACOG). As the first data-driven effort to predict preeclampsia events across the entire journey, we were able to capture important features contributing to preeclampsia prediction, and some of the findings give further credence to the underlying mechanisms associated with preeclampsia. Click the link below to read the full study and learn more about our additional findings.