# Personalized Postpartum Hemorrhage Prediction Using Machine Learning And Polygenic Risk Scores

> **NIH NIH K08** · BRIGHAM AND WOMEN'S HOSPITAL · 2022 · $168,480

## Abstract

ABSTRACT
Postpartum hemorrhage, defined as estimated blood loss of at least 1000 mL within 24 hours of delivery, is the
leading cause for severe maternal morbidity and mortality. Annually, postpartum hemorrhage complicates 2-3%
of all pregnancies and accounts for 140,000 maternal deaths globally. In the United States, there are also
significant racial disparities: Black women have a five-fold higher risk of hemorrhage-related death compared to
non-Black women. While clinical postpartum hemorrhage risk prediction tools have been developed, they fail to
identify up to 40% of cases; as a result, no evidence-based prediction tool is currently widely adopted in clinical
practice. Thus, an efficient, precise, and personalized postpartum hemorrhage risk prediction tool is urgently
needed. Recently, machine learning approaches have been increasingly used to develop accurate predictive
models with superior performance compared to the traditional statistical approaches and to discover new
predictors, with little prior pre-specification. Moreover, the explainable machine learning methods allow for
transparent decision making and reduction of bias. In this way, machine learning models may lead to more
accurate postpartum hemorrhage prediction than currently existing tools. In addition, since up to 18% of
postpartum hemorrhage risk is familial and many of the clinical risk factors associated with postpartum
hemorrhage have a well-established polygenic architecture, using polygenic risk tools may further enhance
postpartum hemorrhage risk prediction. In line with the NIH IMPROVE initiative goals to improve maternal safety
and outcomes, we propose here to develop a high-fidelity algorithm, combining both clinical and genetic factors,
to more accurately predict the risk of postpartum hemorrhage in pregnant individuals. We will leverage our rich
patient database and state-of-the-art computational tools to: (1) develop an improved algorithm to stratify patient
postpartum hemorrhage risk with a focus on transparency and bias reduction, and (2) delineate the contribution
of the genetics to postpartum hemorrhage risk. Overall, this project will advance our ability to precisely predict
patients at risk for postpartum hemorrhage, with the investigation of novel predictors, interaction between clinical
and genetic contributors, and novel application of both machine learning and polygenic risk scores to these
outcomes. Ultimately, we aim to validate and implement these tools in clinical practice, leading to greatly
enhanced ability to prevent maternal morbidity and mortality. By completion of these aims, I will develop a
specific skill set essential for establishing my research trajectory and transition to independence as a physician-
scientist utilizing translational computational approaches to predict and improve adverse obstetric outcomes.

## Key facts

- **NIH application ID:** 10524826
- **Project number:** 1K08HL161326-01A1
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Vesela Kovacheva
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $168,480
- **Award type:** 1
- **Project period:** 2022-08-01 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10524826

## Citation

> US National Institutes of Health, RePORTER application 10524826, Personalized Postpartum Hemorrhage Prediction Using Machine Learning And Polygenic Risk Scores (1K08HL161326-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10524826. Licensed CC0.

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