Prenatal Blood Pressure Patterns to Predict Pregnancy-Related Hypertension and Later Life Cardiovascular Risk

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ABSTRACT Pregnancy-related hypertensive (PRH) disorders, preeclampsia (PE) and gestational hypertension (GH), complicate up to 10% of all pregnancies and are a leading cause of maternal and perinatal morbidity and mortality in the U.S. These disorders also have been linked with higher risk of later life hypertension (HTN) and cardiovascular disease (CVD) in women. In 2013, the American College of Obstetrics and Gynecology identified the need for early identification of women at risk for PRH disorders because available predictive models could not demonstrate clinical utility among low risk women. In 2017, the U.S. Preventive Services Task Force report cited research gaps including the need “to further develop and validate tools for risk prediction using rigorous methodology, including appropriate calibration statistics and validated models that use parameters available in routine care (e.g., clinical history and clinical testing).” The proposed study addresses these gaps utilizing statistical methods designed to identify latent classes of individuals with similar patterns of blood pressure (BP) change over time. This advanced statistical technique classifies women into BP trajectory groups that may identify those at higher risk for PE and GH among “low risk” women. We chose this method because it has already proven to be highly effective for prediction of future CVD in non-pregnant adults. This study will utilize BP trajectory groups and clinical risk factors to evaluate and validate models for prediction of PE and GH during the index and subsequent pregnancies, as well as later risk of HTN and CVD. We propose a retrospective cohort study of pregnancies delivered in 2009-2018 (~330,000) along with prospective follow up for later life HTN and CVD outcomes in women. This large, community-based, highly diverse sample from the Kaiser Permanente Northern California (KPNC) integrated healthcare delivery system leverages the established electronic health record (EHR) since 2008 linking all clinical data sources. The study will develop prediction models for PE and GH that show high clinical utility across most settings for the early risk stratification of low risk women, and prediction of new onset HTN and CVD in later life. The specific aims are: Aim 1: To identify the first 20 wks' gestation BP trajectory groups associated with risk of PE and GH, and evaluate and validate the BP trajectory model's predictive ability to identify women at risk for PE and GH; Aim 2: To evaluate and validate the predictive ability of first 20 wks' gestation BP trajectory groups, and the entire pregnancy BP trajectory groups to each identify women with or without PE and GH who are at risk for new onset HTN and CVD up to 12 years post-delivery; Aim 3: Among women without PE or GH (Aims 1-2), to evaluate and validate the entire pregnancy BP trajectory groups ability to predict PE and GH in a subsequent pregnancy. The clinical translation is to develop an automated algorithm for low risk women with EHR clinical data to improve early risk stratification for PE and GH, and later life HTN and CVD. The method may direct clinical monitoring, use of available therapies, and testing of new therapies for early prevention.