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Principal Investigator: ALLEN D EVERETT
Organization: JOHNS HOPKINS UNIVERSITY
Fiscal Year: 2024
Award: $654,440
Funding agency: Eunice Kennedy Shriver National Institute of Child Health and Human Development
Fifty percent of neonatal encephalopathy cases result from hypoxic-ischemic encephalopathy (HIE).
Therapeutic hypothermia (TH), the only approved therapy remains state of the art care for HIE, despite only a
30% reduction in death and significant disability. Our limited ability to accurately track TH efficacy limits
identification of babies, who may benefit from adjunctive therapies. Under R01HD086058, our team enrolled
neonates with HIE treated with TH and tested whether circulating brain injury biomarkers used in adults were
associated with HIE severity, MRI and 2-year outcomes. We identified the novel biomarkers significantly
associated with the proposed outcomes and published 22 peer-reviewed original, high-impact manuscripts.
Our team has extensive experience in biomarkers in children (1R01HL150070), brain injury biomarkers in HIE
(U01 NS114144) and real-time machine learning integrating within health systems (R61HD105591). Our
central hypothesis is that a holistic and integrative approach, including deep clinical and community-based
data, and molecular biomarkers of multiple biologic pathways, analyzed using a fully connected parsimonious
neural network will best describe relationships with longitudinal outcomes, and be able to predict response to
TH in individual patients. Our outstanding group of investigators from Johns Hopkins University, University of
Virginia and University of Alabama Birmingham, propose the following Aims: Aim 1a. Perform clinical data-
driven modeling to ascertain TH effectiveness. We will use deep phenotyping data sets of all maternal,
neonatal, community-based, and follow-up data collected retrospectively (2016-2021) and prospectively thru
year 1, from neonates treated with TH at the 3 centers (n = 500) to model TH efficacy using multivariable
methods against longitudinal outcomes. Aim 1b. Identify novel molecular signatures for HIE insult severity
which predict response to TH. Using our discovery (N=178) TH treated HIE cohort, we will determine if
circulating brain injury proteins, metabolites and exosome proteins and nucleic acids are associated with TH
efficacy. Aim 1c. Determine relationships emerging from integration between clinical, community-based, and
molecular markers using a fully connected parsimonious neural network approach. 1C.1 Use computational
simulations to identify the levers, modifiable risk factors and interventions associated with the probability of
negative outcomes, in the neural network, and 1C.2 Determine in silico whether optimization of the neural
network using those levers at the individual patient level, results in a reduction in the predicted probability of
negative outcomes. Aim 2. External validation of neural network and estimation of potential clinical gain
achievable by optimization of the neural network, in prospective patients (Years 2-5). Completion of our aims
will identify the clinical, socioeconomic, and molecular mechanisms driving clinical heterogeneity in HIE and
response to TH. We will then be poised to rapidly deploy a dynamic, precision-based model to optimized
patient selection for future HIE adjunctive therapies.
Terms: <0-11 years old><2 year old><2 years of age><21+ years old><Acquired brain injury><Active Follow-up><Adult><Adult Human><Age><Alabama><Automobile Driving><Biological><Biological Markers><Brain Injuries><Caring><Cessation of life><Child><Child Youth><Children (0-21)><Clinical><Clinical Data><Collaborations><Communities><Computer Simulation><Computer based Simulation><Coupled><Data><Data Scientist><Data Set><Data Sources><Death><Dimensions><Effectiveness><Enrollment><Funding><Future><Health system><Hypothermia><Individual><Intervention><Intervention Strategies><Intervention Trial><Interventional trial><Investigation><Investigators><Knowledge><Life><MR Imaging><MR Tomography><MRI><MRIs><Machine Learning><Magnetic Resonance Imaging><Manuscripts><Measures><Medical Imaging, Magnetic Resonance / Nuclear Magnetic Resonance><Methods><Modeling><Molecular><Molecular Fingerprinting><Molecular Profiling><NICHD><NMR Imaging><NMR Tomography><National Institute of Child Health and Human Development><National Institute of Children's Health and Human Development><Neonatal><Neonatal Intensive Care Units><Neural Development><Newborn Intensive Care Units><Nuclear Magnetic Resonance Imaging><Nucleic Acids><Outcome><Pathway interactions><Patient Selection><Patient outcome><Patient-Centered Outcomes><Patient-Focused Outcomes><Patients><Peer Review><Probability><Proteins><Publishing><Research Personnel><Researchers><Retinoscopy><Right to Treatments><Risk Factors><Severities><Shadow Test><Skiametries><Skiametry><Skiascopy><Stratification><Testing><Therapeutic><Time><Universities><Validation><Virginia><Zeugmatography><active followup><adulthood><age 2 years><aged 2 years><aged two years><ages><bio-markers><biologic><biologic marker><biomarker><brain MR imaging><brain MRI><brain damage><brain magnetic resonance imaging><brain-injured><cerebral MR imaging><cerebral MRI><cerebral magnetic resonance imaging><clinical biomarkers><clinical heterogeneity><clinically useful biomarkers><cohort><computational simulation><computer based prediction><computerized simulation><cost><data-driven model><disability><driving><enroll><exosome><experience><feeding><follow up><follow-up><followed up><followup><hypoxic ischemic encephalopathy><improved><improved outcome><in silico><individual patient><interventional strategy><kids><machine based learning><machine learning based method><machine learning method><machine learning methodologies><malleable risk><modifiable risk><molecular biomarker><molecular marker><molecular profile><molecular signature><natural hypothermia><neonatal HIE><neonatal ICU><neonatal encephalopathy><neonatal hypoxia-ischemia><neonatal hypoxic-ischemic brain injury><neonatal hypoxic-ischemic encephalopathy><neonate><network models><neural network><neurodevelopment><new marker><novel><novel biomarker><novel marker><participant enrollment><pathway><patient enrollment><patient oriented outcomes><patient safety><phenotypic biomarker><phenotypic data><phenotypic marker><predict responsiveness><predicting response><predictive modeling><prevent><preventing><prospective><response><socio-economic><socio-economically><socioeconomically><socioeconomics><two year old><two years of age><validations><youngster>