Development of Serum, Imaging, and Clinical Biomarker Driven Models to Direct Clinical Management after Pediatric Cardiac Arrest

描述（由申请人提供）：心脏骤停（CA）儿童的死亡率为50- 90%，主要是由于复苏后综合征的神经功能衰竭。在对儿童CA后的结局进行准确分类方面，知识和工具存在严重差距。体格检查和实验室检查不能充分评估神经系统损伤的严重程度和预后。错误分类的危害包括无效治疗和不治疗表面上良好的患者而导致的不良反应，这些患者后来被发现有神经功能缺损。早期和准确识别神经损伤的最终严重程度将允许在特定风险人群中及时进行神经保护干预和/或更有针对性的新疗法测试。我们的长期目标是改善CA存活儿童的神经功能结局。在这里，我们建议建立模型，并验证血清和影像学脑损伤的生物标志物与经验的支持，并评估其准确性与临床变量在儿童CA后的结果进行分类。我们寻求利用来自NIH资助的儿科CA单中心RCT的强大初步数据以及我们在儿科脑损伤生物标志物研究中的跟踪记录。我们的中心假设是，在一项多中心前瞻性研究（8个中心和248名受试者）中，脑损伤的血清和成像生物标志物以及临床变量将对儿童CA后1年的儿童CA（Vineland适应行为量表评分> 70）后有利结局的早期分类起到关键性作用。强有力的初步数据支持这一假设，并且将在以下3个具体目的中测试生物标志物的结果分类准确性：（神经元特异性烯醇化酶和泛素羧基末端水解酶-L1）与神经胶质损伤S100 b和胶质细胞酸性蛋白;目标2）区域（枕顶叶皮质、基底神经节和丘脑）脑MRI（T1/T2和弥散加权成像）和神经元损伤的MR波谱生物标志物Aim 3将模拟脑损伤的强血清和成像生物标志物与临床变量的组合。我们将评估脑线粒体损伤的血清生物标志物，以探索性目的作为新的治疗靶点（心磷脂和氧化心磷脂）。这项拟议的研究是创新的，因为我们将前瞻性地开发和优化一组血清和成像生物标志物与临床变量的组合，以准确分类儿科CA后的结果。这些建议的目标利用最近的试点成功，并应产生准确和可靠的生物标志物模型，显着改善复苏后的临床护理后CA的儿童。此外，这些结果预计将对这些儿童的神经重症监护产生积极影响，即将开发的血清生物标志物床旁检测和生物标志物面板将准确分类临床医生和研究人员需要按损伤严重程度分层的不利结果的风险，以监测对治疗的反应，并最终帮助他们康复和恢复。

DESCRIPTION (provided by applicant): Children with cardiac arrest (CA) have mortality rates of 50-90%, largely due to neurological failure as part of the post-resuscitation syndrome. There is a critical gap of knowledge and tools to accurately classify outcome after pediatric CA. Physical examination and laboratory testing inadequately assess the severity of neurologic injury and outcome. Hazards of misclassification include risking adverse effects from ineffective therapies and non-treatment of ostensibly well patients who later are found to have neurologic deficits. Early and accurate identification of the eventual severity of neurologic injury would allow for timely neuroprotective interventions and/or more targeted testing of new therapies in specific risk populations. Our long term objective is to improve the neurological outcome of children surviving CA. Here we propose to model and validate serum and imaging biomarkers of brain injury with empirical support, and to assess their accuracy together with clinical variables in classifying outcome after pediatric CA. We seek to capitalize on robust preliminary data from an NIH-funded single center RCT in pediatric CA and our track record in biomarker research in pediatric brain injury. Our central hypothesis is that serum and imaging biomarkers of brain injury, together with clinical variables, will critically aid in the early classification of favorable outcome after pediatric CA (Vineland Adaptive Behavior Scales score > 70) 1 year after pediatric CA in a multicenter prospective study (8 centers and 248 subjects). Strong preliminary data supports this hypothesis, and biomarkers will be tested for outcome classification accuracy in the following 3 specific aims: Aim 1) Serum biomarkers of neuronal (neuron specific enolase and ubiquitin carboxy-terminal hydrolase-L1) and glial injury (S100b and glial fibrillary acidic protein); Aim 2) Regional (occipital-parietal cortex, basal ganglia, and thalamus) brain MRI (T1/T2 and diffusion-weighted imaging) and MR spectroscopy biomarkers of neuronal injury (N-acetyl-aspartate) and energy failure (lactate); and Aim 3 will model the combination of strong serum and imaging biomarkers of brain injury with clinical variables. We will assess serum biomarkers of brain mitochondrial injury with potential for novel therapeutic targets (cardiolipin and oxidized cardiolipin) in an exploratory aim. This proposed research is innovative, because we will prospectively develop and optimize a combined panel of serum and imaging biomarkers with clinical variables to accurately classify outcome after pediatric CA. These proposed aims leverage recent pilot successes and should generate accurate and reliable models of biomarkers that markedly improve post-resuscitation clinical care in children after CA. Furthermore, these results are expected to have a positive impact in advancing neurocritical care for these children, with forthcoming development of a serum biomarker point of care test and biomarker panels that will accurately classify risk of unfavorable outcome for clinicians and researchers needing to stratify by severity of injury, to monitor response to therapy, and ultimately to assist in their rehabilitation and recovery.