# Maternal Antecedents and Electronic Fetal Monitoring in Term Asphyxia (MAESTRA)

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2020 · $667,049

## Abstract

Neonatal hypoxic-ischemic encephalopathy (HIE) is a neurologic syndrome that results from reduced flow of
oxygenated blood to the fetal or newborn brain. HIE occurs in 1-3 per 1,000 term births and may cause death or
neurologic disabilities such as cerebral palsy. Electronic fetal monitoring (EFM) was developed in the 1970's to
assess the adequacy of fetal oxygenation as a strategy to prevent HIE, and is now standard of care. Yet clinical
trials report that EFM usage has not reduced the rate of CP, perinatal death or HIE, but is associated with a
dramatic increase in cesarean deliveries. The currently used 3 Category fetal heart rate (FHR) classification
system, based on simple rules designed to be easy to apply at the bedside, has some utility in predicting HIE.
However, Category II FHR patterns that make up the vast majority of tracings are poorly predictive of HIE and
confer “indeterminate” risk. Category III patterns are also of limited use in predicting HIE due to low sensitivity.
There is an urgent need to develop better objective methods to assess EFM that would identify more fetuses at
risk of HIE in time for corrective actions. Uterine tachysystole, or excessive frequency of uterine contractions,
has been implicated as a preventable cause of HIE; yet studies report conflicting results. EFM research has
been limited by an inability to access and manually analyze the large datasets needed to study HIE. We now
have the ability to analyze digital EFM signals using automated methods to measure standard FHR patterns as
well as to discover novel aspects of the tracing that may not be readily detectable by a clinician at the bedside.
We hypothesize that modern signal processing and machine learning techniques can create highly predictive
models of HIE by analyzing established and novel features of EFM tracings, in combination with demographic
and pertinent clinical information from the mother and fetus. We propose a population-based retrospective cohort
study of 350,000 infants born at ≥ 36 weeks gestation at Kaiser Permanente Northern California in 2010-19. Our
specific aims are: 1) To create the MAESTRA Cohort dataset that links EFM recordings to HIE and neonatal
acidosis among 350,000 infants born at ≥ 36 weeks gestation in 2010-19 at Kaiser Permanente Northern CA; 2)
Using modern signal processing and machine learning techniques, to extract established and novel FHR and
uterine contractility features from the EFM recordings, and to determine which of these features are most
predictive of HIE and acidosis when combined with maternal and fetal clinical data; and 3) To perform external
validation by applying the final predictive models to a historical dataset. We anticipate that machine learning
techniques incorporating novel FHR and uterine contractility patterns over time, as well as pre- and perinatal
clinical characteristics, will improve the predictive value of the EFM data that are already being collected as part
of routine care. Our resu...

## Key facts

- **NIH application ID:** 9972526
- **Project number:** 1R01HD099216-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Robert Edward Kearney
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $667,049
- **Award type:** 1
- **Project period:** 2020-05-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9972526

## Citation

> US National Institutes of Health, RePORTER application 9972526, Maternal Antecedents and Electronic Fetal Monitoring in Term Asphyxia (MAESTRA) (1R01HD099216-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9972526. Licensed CC0.

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