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。 治疗性低温(TH)，唯一被批准的治疗方法仍然是对HIE的最先进的治疗，尽管只有一个 死亡和严重残疾减少30%。我们准确跟踪疗效极限的能力有限 识别可能受益于辅助治疗的婴儿。在R01HD086058下，我们队报名 用TH治疗新生儿HIE，并检测成人使用的循环脑损伤生物标志物是否 与HIE严重程度、MRI和两年预后相关。我们显著地确定了新的生物标志物 与拟议的成果有关，并出版了22份经过同行评审的原创、影响很大的手稿。 我们的团队在儿童生物标记物(1R01HL150070)、脑损伤生物标记物(HIE)方面拥有丰富的经验 (U01 NS114144)和卫生系统内集成的实时机器学习(R61HD105591)。我们的 中心假设是一个整体和综合的方法，包括深入的临床和基于社区的 数据和多个生物途径的分子生物标记物，使用完全连接的节俭分析 神经网络将最好地描述与纵向结果的关系，并能够预测对 在个别患者中。我们来自约翰·霍普金斯大学、华盛顿大学的杰出研究团队 弗吉尼亚大学和阿拉巴马大学伯明翰分校提出了以下目标：目标1a。执行临床数据- 驱动建模以确定有效性。我们将使用所有母亲的深层表型数据集， 回溯(2016-2021)和前瞻性收集的新生儿、社区和随访数据 第1年，从3个中心(n=500)接受TH治疗的新生儿使用多变量建立TH疗效模型 方法对照纵向结果。目标1b。确定HIE侮辱严重程度的新分子特征 它们预测了对这一事件的反应。使用我们的发现(N=178)治疗HIE队列，我们将确定 循环脑损伤蛋白、代谢物和外体蛋白和核酸与 功效。目标1c。确定临床、社区和社区之间的整合关系 使用完全连接的简约神经网络方法的分子标记。1 C.1使用计算 模拟，以确定杠杆，可修改的风险因素和干预与概率有关 负结果，在神经网络中，和1C.2在计算机中决定是否优化神经 在个体患者层面使用这些杠杆的网络，导致预测的 消极的结果。目的2.神经网络的外部验证和潜在临床收益的估计 可通过优化神经网络在潜在患者(2-5岁)中实现。完成我们的目标 将确定导致HIE和HIE临床异质性的临床、社会经济和分子机制 对TH的回应。然后，我们将准备好快速部署动态、基于精度的模型，以优化 未来HIE辅助治疗的患者选择。