# Maternal Morbidity and Mortality: Risk Factors, Early Detection and Personalized Intervention

> **NIH NIH UL1** · GEORGETOWN UNIVERSITY · 2020 · $146,537

## Abstract

PROJECT ABSTRACT
 In the U.S., from 2011 to 2014, 7,208 maternal fatalities, with the trend worsening year-on-year (CDC,
2016). In addition to the 700+ fatalities, at least 50,000 woman experienced life-threatening complications,
annually. According to CDC (2019) for every fatality, 70 more women suffer avoidable, traumatic
complications as a result of pregnancy.
 Medstar Health Research Institute and Invaryant, Inc, propose the evaluation of a cardiac risk
assessment tool for pregnant and postpartum women; this tool updates automatically directly from patient
medical records; wearable devices; and patient surveys. Success implies disruptive improvement in women's
health. Proposed research involves three key elements: technology, data, and social determinants of health
(SDOH) using geospatial mapping of patient locations.
Technology: Study technology shall be based on the Invaryant Health Platform (IHP), a technology that
automatically ingests data, from medical records, wearable devices, and other sources using proprietary AI
based interoperability technology called Mesh-Complex Method Exchange (Mesh-CMX). We propose using the
IHP in conjunction with novel prototype-level technology, namely Healthy Outcomes for all Pregnancy
Experiences-Cardiovascular-risk Assessment Technology (HOPE-CAT) and the Invaryant machine learning
technologies to monitor the patient, based on signals, out-of-range “trip-wires”, and trends in the mother's
health data that merit medical intervention. By extending the proof of concept into an early commercial version
of the software, and integrating it to the IHP which will automatically update changes in the patient's medical
record, we will provide an “early warning” system for mothers and their providers.
Data: The study will be tested on patients' medical records using the MedStar's Analytics Platform (MAP), a
registry of over 5 Million unique patients. The tool will subsequently have the potential to be leveraged to over
90 million medical records for the MedStar and Cerner hospital systems, distilled down to meet specific
eligibility criteria including, gender, age, race, pregnancy and medical outcomes. A second phase of this
project would take the findings from the retrospective study (this grant request) and use the technology within
the Medstar hospital system, to validate the efficacy of the findings in a “real-world setting”. Using our
proprietary AI technology, we will compare each mother's progress against a cohort of retrospective data to
enhance diagnostics and provide real-time feedback to caregivers and patients.
Geospatial mapping: Mapping the patient medical record and the health information to their social setting is
vital for understanding the underlying social constructs that affect the health of mothers in different regions.

## Key facts

- **NIH application ID:** 10200448
- **Project number:** 3UL1TR001409-06S2
- **Recipient organization:** GEORGETOWN UNIVERSITY
- **Principal Investigator:** THOMAS A MELLMAN
- **Activity code:** UL1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $146,537
- **Award type:** 3
- **Project period:** 2015-08-28 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10200448

## Citation

> US National Institutes of Health, RePORTER application 10200448, Maternal Morbidity and Mortality: Risk Factors, Early Detection and Personalized Intervention (3UL1TR001409-06S2). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10200448. Licensed CC0.

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