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Clinical and Physiological Determinants of Pregnancy-Related Cardiovascular Complications

Award Number: R01HL182978
ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/10/2026
PERIOD OF PERFORMANCE END DATE: 06/30/2031

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Clinical and Physiological Determinants of Pregnancy-Related Cardiovascular Complications - Cardiovascular disease (CVD) is the leading cause of maternal mortality in the United States, where rates of maternal death are the highest among industrialized countries. Recent studies found that over 80% of pregnancy-related deaths are preventable due in large part to failure to identify high risk women early in pregnancy. While early assessment of patient specific risks of cardiovascular (CV) complications during pregnancy has the potential to significantly reduce maternal mortality, presently available clinical risk tools are not widely used in clinical practice because they were derived from small samples of pregnant women, largely restricted to women with existing CVD. In preliminary work, our research team has shown that data science approaches, including machine learning, applied to real world clinical data can be leveraged to identify pregnant women at high risk for pregnancy-related CV complications. We have assembled a novel multi-institutional electronic health record (EHR) pregnancy cohort of >57,000 pregnancies and have developed clinical and deep learning ECG-based risk models that accurately estimate risk of pregnancy-related CV complications. Further, we have shown that we can perform deep vascular phenotyping in women with recent preeclampsia, one of the most common pregnancy-related CV complications, highlighting a unique opportunity to pair deep physiologic phenotyping with our deep learning ECG model of preeclampsia to inform the biological and clinical relevance of our deep learning models. Taken together, our central objective is to leverage real world clinical data to elucidate clinical and physiological determinants of CV complications during pregnancy to enhance antepartum risk assessment of pregnancy-related CV complications. In Aim 1, we will test whether a clinical risk prediction model trained in a longitudinal EHR sample is more accurate than currently available risk tools for prediction of pregnancy-related CV complications. In Aim 2, we will conduct a prospective study of n=400 newly pregnant women to validate our ECG deep learning model to estimate risk of pregnancy-related CV complications. In Aim 3, we will perform a prospective observational study of n=80 pregnant women deemed high vs low preeclampsia risk using our ECG deep learning model (n=40/group) and perform detailed vascular phenotyping to examine whether ECG-predicted preeclampsia risk is associated with indices of impaired vascular health. This proposal leverages a unique and highly experienced multidisciplinary team of investigators with expertise in CVD in pregnancy, obstetrics, machine learning, vascular biology, RNA sequencing, and patient-oriented translational studies. These studies will provide important support for the translation of data science and machine learning approaches to the clinical care of pregnant women and discern novel mechanistic insights into the drivers of pregnancy related CV complications.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $893,837 )
2026	2026	THE GENERAL HOSPITAL CORPORATION	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Cardiovascular Diseases Research	000	1	9/10/2026	NEW	$893,837
														Subtotal = $893,837

Grand Total All Awards = $893,837

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Clinical and Physiological Determinants of Pregnancy-Related Cardiovascular Complications

Award Number: R01HL182978

ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/10/2026

PERIOD OF PERFORMANCE END DATE: 06/30/2031

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