Advanced therapeutic hypothermia efficacy network modeling in neonatal HIE

新生儿 HIE 的先进低温治疗功效网络模型

• 批准号: 10696194
• 负责人: ALLEN D EVERETT
• 金额: $ 66.67万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-02 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10696194
• 关键词: 2 year old; Adult; Age; Alabama; Automobile Driving; Biological; Biological Markers; Brain Injuries; Caring; Cessation of life; Child; Clinical; Clinical Data; Collaborations; Communities; Computer Simulation; Coupled; Data; Data Scientist; Data Set; Data Sources; Dimensions; Effectiveness; Enrollment; Funding; Future; Health system; Individual; Intervention; Intervention Trial; Investigation; Knowledge; Life; Machine Learning; Magnetic Resonance Imaging; Manuscripts; Measures; Methods; Modeling; Molecular; Molecular Profiling; National Institute of Child Health and Human Development; Neonatal; Neonatal Intensive Care Units; Nucleic Acids; Outcome; Pathway interactions; Patient Selection; Patient-Focused Outcomes; Patients; Peer Review; Probability; Proteins; Publishing; Research Personnel; Retinoscopy; Right to Treatments; Risk Factors; Severities; Stratification; Testing; Therapeutic; Time; Universities; Validation; Virginia; brain magnetic resonance imaging; clinical biomarkers; clinical heterogeneity; cohort; cost; data-driven model; disability; exosome; experience; feeding; follow-up; improved; improved outcome; in silico; individual patient; machine learning method; modifiable risk; molecular marker; natural hypothermia; neonatal encephalopathy; neonatal hypoxic-ischemic brain injury; neonate; network models; neural network; neurodevelopment; novel; novel marker; participant enrollment; patient safety; phenotypic biomarker; phenotypic data; predicting response; predictive modeling; prevent; prospective; response; socioeconomics

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.

50%的新生儿脑病是由缺氧缺血性脑病（HIE）引起的。