# Leveraging remote blood pressure monitoring and interpretable machine learning to improve clinical workflows for hypertensive disorders of pregnancy

> **NIH NIH R43** · DELFINA CARE INC. · 2023 · $275,739

## Abstract

Project Summary/Abstract:
Hypertensive disorders of pregnancy (HDP) are a leading cause of pregnancy-related deaths in the United
States. Specific interventions, such as nutrition counseling and prophylactic aspirin use, are known to prevent
the onset and exacerbation of HDP. However, current approaches to identify patients early in pregnancy are
limited due to challenges collating patient data from the electronic health record (EHR) and low precision and
recall of traditional rules-based medical calculators. Machine learning (ML) methods that can flexibly capture
complex relationships between HDP risk factors offer a potential solution, but often only render a static
prediction at one time point and do not update as additional information is collected during pregnancy. The
objective of this project is to develop a clinically actionable machine learning model that updates dynamically
as patients track blood pressure throughout their pregnancies.
 Specifically, in Aim 1, we will assess the increased predictive power of utilizing blood pressure
measurements arising from remote blood pressure monitoring (RBPM) as compared to in-office
measurements. We will phenotype patient blood pressure trajectories and investigate associations between
phenotypes and HDP diagnosis. In Aim 2, we will use a Bayesian machine learning approach to incorporate
the RBPM phenotypes developed in Aim 1 to enhance an existing static HDP model built on EHR data. The
developed model will be able to assess patients at multiple time points throughout their pregnancy based on
their at-home BP measures. Finally, in Aim 3, we will conduct a mixed-methods study with obstetricians and
certified nurse midwives to build a user-centered display that effectively communicates the results from the
dynamic model.
 The project outlined in this proposal will give obstetricians a clinically interpretable tool – BotoML – to
help them identify patients that would benefit from intervention early in their pregnancy. The completion of
these aims will enable a future Phase II to deploy and prospectively validate BotoML in geographically diverse
provider and patient populations.

## Key facts

- **NIH application ID:** 10822625
- **Project number:** 1R43HD114360-01
- **Recipient organization:** DELFINA CARE INC.
- **Principal Investigator:** Isabel Fulcher
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $275,739
- **Award type:** 1
- **Project period:** 2023-09-18 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10822625

## Citation

> US National Institutes of Health, RePORTER application 10822625, Leveraging remote blood pressure monitoring and interpretable machine learning to improve clinical workflows for hypertensive disorders of pregnancy (1R43HD114360-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10822625. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*