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%的新生儿脑病病例是由缺氧缺血性脑病（HIE）引起的。 治疗性低温（TH），唯一批准的治疗仍然是最先进的治疗新生儿缺氧缺血性脑病，尽管只有一个 死亡和严重残疾减少30%。我们准确跟踪TH疗效限值的能力有限 识别婴儿，谁可能受益于替代疗法。根据R 01 HD 086058，我们的团队注册了 用TH治疗新生儿HIE，并测试成人中使用的循环脑损伤生物标志物是否 与HIE严重程度、MRI和2年结局相关。我们发现新的生物标志物显着 与拟议成果相关的出版物，并出版了22份经同行评审的高影响力原创手稿。 我们的团队在儿童生物标志物（1 R 01 HL 150070）、HIE脑损伤生物标志物 (U01 NS 114144）和实时机器学习集成在卫生系统中（R61 HD 105591）。我们 中心假设是，一个整体和综合的方法，包括深入的临床和社区为基础的 数据和多个生物途径的分子生物标志物，使用完全连接的简约 神经网络将最好地描述与纵向结果的关系，并能够预测对 个体患者的TH。我们杰出的研究小组来自约翰霍普金斯大学， 弗吉尼亚大学和亚拉巴马伯明翰大学提出了以下目标：目标1a。执行临床数据- 驱动建模以确定TH有效性。我们将使用所有产妇的深层表型数据集， 回顾性（2016-2021年）和前瞻性收集的新生儿、社区和随访数据， 第1年，来自3个中心接受TH治疗的新生儿（n = 500），使用多变量 方法对纵向结果。目标1b。确定新生儿缺氧缺血性脑病损伤严重程度的新分子特征 预测对TH的反应。使用我们的发现（N=178）TH治疗的HIE队列，我们将确定 循环脑损伤蛋白、代谢物和外来体蛋白和核酸与TH相关 功效目标1c。确定临床、基于社区的和 使用完全连接的简约神经网络方法的分子标记。1C.1使用计算 模拟，以确定杠杆，可修改的风险因素和干预措施与概率 负面结果，在神经网络中，和1C.2通过计算机确定是否优化神经网络 网络在个体患者水平上使用这些杠杆，导致预测的概率降低， 消极的结果。目标二。神经网络的外部验证和潜在临床增益的估计 在前瞻性患者中（2-5年），通过优化神经网络可实现。完成我们的目标 将确定临床，社会经济和分子机制驱动临床异质性在HIE和 回答TH。然后，我们将准备快速部署一个动态的、基于精度的模型， 未来HIE治疗的患者选择。