Document text
Principal Investigator: Robert Edward Kearney
Organization: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
Fiscal Year: 2024
Award: $509,907
Funding agency: Eunice Kennedy Shriver National Institute of Child Health and Human Development
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 results will inform future clinical trials. Such an unprecedented large-scale multidisciplinary
study will lead to improvements in our ability to use EFM data to prevent neonatal brain injury while minimizing
unnecessary cesarean sections.
Terms: <0-4 weeks old><Abdominal Delivery><Acidosis><Address><Apgar Score><Asphyxia><Blood><Blood Reticuloendothelial System><Blood flow><Brain><Brain Metabolic Disorders><Brain Nervous System><C section><California><Cardiac Chronotropism><Categories><Cause of Death><Cell Communication and Signaling><Cell Signaling><Cerebral Palsy><Cesarean><Cesarean section><Cessation of life><Characteristics><Classification><Clinical><Clinical Data><Clinical Trials><Cohort Studies><Computerized Medical Record><Concurrent Studies><Data><Data Set><Death><Discipline of obstetrics><Educational workshop><Electronic Medical Record><Electronics><Encephalon><Fetal Heart Rate><Fetal Monitoring><Fetus><Frequencies><Fullterm Birth><Future><Gestation><Heart Rate><Infant><Intracellular Communication and Signaling><Lead><Link><Long term disability><Machine Learning><Manuals><Metabolic Brain Diseases><Metabolic Brain Syndromes><Metabolic Encephalopathies><Metabolic acidosis><Methods><Modeling><Modernization><Mothers><Myometrial Contraction><NICHD><National Institute of Child Health and Human Development><National Institute of Children's Health and Human Development><Neonatal><Neonatal Brain Injury><Neurologic><Neurological><Neurological disability><Newborn Infant><Newborns><O element><O2 element><Observation research><Observation study><Observational Study><Observational research><Obstetrics><Outcome><Oxygen><Pattern><Pb element><Perinatal><Perinatal Mortalities><Perinatal lethality><Perinatal mortality demographics><Peripartum><Population><Position><Positioning Attribute><Predictive Value><Pregnancy><Preventative intervention><Records><Reporting><Research><Research Priority><Retrospective cohort study><Risk><Sensitivity and Specificity><Signal Transduction><Signal Transduction Systems><Signaling><Suffocation><Syndrome><System><Systematics><Techniques><Term Birth><Testing><Time><Uterine Contraction><Uterus><Validation><Workshop><asphyxiation><assess effectiveness><at-risk fetus><biological signal transduction><cohort><computer based prediction><computerized><design><designing><determine effectiveness><digital><digital data><effectiveness assessment><effectiveness evaluation><electronic><electronic device><evaluate effectiveness><examine effectiveness><falls><fetal><fetus at risk><fetus monitoring><full-term birth><fullterm newborn><heavy metal Pb><heavy metal lead><high risk><hypoxic ischemic encephalopathy><improved><intervention for prevention><large data sets><large datasets><machine based learning><multidisciplinary><neonatal HIE><neonatal hypoxia-ischemia><neonatal hypoxic-ischemic brain injury><neonatal hypoxic-ischemic encephalopathy><neonatal seizure><newborn child><newborn children><newborn seizure><novel><perinatal deaths><population based><predictive modeling><prenatal><prevent><preventing><prevention intervention><preventional intervention strategy><preventive intervention><routine care><signal processing><standard measure><standard of care><term newborn><unborn><uterine contractility><validations><womb>