# Advanced therapeutic hypothermia efficacy network modeling in neonatal HIE

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2022 · $715,813

## Abstract

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
respo...

## Key facts

- **NIH application ID:** 10538972
- **Project number:** 1R01HD110091-01
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** ALLEN D EVERETT
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $715,813
- **Award type:** 1
- **Project period:** 2022-09-02 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10538972

## Citation

> US National Institutes of Health, RePORTER application 10538972, Advanced therapeutic hypothermia efficacy network modeling in neonatal HIE (1R01HD110091-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10538972. Licensed CC0.

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