Consortium Of MRI Biomarkers In Neonatal Encephalopathy (COMBINE)

新生儿脑病 MRI 生物标志物联盟 (COMBINE)

• 批准号: 10614588
• 负责人: Patricia Ellen Grant
• 金额: $ 88.39万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10614588
• 关键词: Address; Adverse event; Agreement; Apgar Score; Area; Artificial Intelligence; Biological Markers; Birth Weight; Boston; Brain; Brain Injuries; Brain Neoplasms; Brain region; Cessation of life; Clinical; Clinical Data; Clinical Trials; Cognitive; Complex; Computer software; Consumption; Country; Data; Data Element; Data Set; Entropy; Future; General Hospitals; Geometry; Heterogeneity; Hour; Income; Infant Development; Injury; International; Letters; Location; Magnetic Resonance Imaging; Massachusetts; Multicenter Neonatal Research Network; National Institute of Child Health and Human Development; Neonatal; Newborn Infant; Outcome; Patients; Pattern; Pediatric Hospitals; Prognostic Marker; Reader; Receiver Operating Characteristics; Sample Size; Sensitivity and Specificity; Shapes; Signal Transduction; Site; Socioeconomic Status; Specificity; Stroke; System; Testing; Texture; Therapeutic; Therapeutic Trials; Time; United States National Institutes of Health; Vision; adverse outcome; artificial intelligence algorithm; brain magnetic resonance imaging; clinical biomarkers; clinical examination; cohort; experience; injured; innovation; magnetic resonance imaging biomarker; natural hypothermia; neonatal brain; neonatal encephalopathy; neonatal hypoxic-ischemic brain injury; neonatal magnetic resonance imaging; neonate; novel therapeutics; outcome prediction; postnatal; predictive marker; sex; standard care

Abstract Hypoxic Ischemic Encephalopathy (HIE) is a brain injury occurring in ~5/1000 newborns. In 2005, the NIH Neonatal Research Network (NRN) established therapeutic hypothermia (TH), cooling patients in the first 6 postnatal hours to 33-34°C for 72 hours, as the standard treatment for HIE in high-income countries. However, many patients still experience adverse outcomes (death or cognitive Bayley Scales of Infant Development <85) by 18-22 months. Thus, from 2008 to 2015, the NRN tested if deeper, longer, or later TH further reduced adverse outcomes, with two trials in 21 sites. Unfortunately, results were inconclusive and further progress has been slow, largely because adverse outcomes cannot be reliably assessed until 18-22 months. To expedite therapeutic innovations and assess the impact of novel therapies in a more timely manner, there is an urgent but unmet need to establish a neonatal biomarker of 18-22 month adverse outcomes. To address this gap, the NRN developed such a biomarker using neuroradiological expert scoring of brain injury on clinically acquired neonatal brain magnetic resonance images (MRIs), known as the NRN MRI score. In one dataset with one reader, sensitivity/specificity for adverse outcomes was 81%/78%. However, in another dataset with two readers, the inter-reader agreement was only moderate and specificity for adverse outcomes was only 56-69%. Questions arise for whether this subjective and time-consuming scoring system is reliable or fully characterizes complex HIE injury patterns. Also in many countries, there are no experts available to perform MRI scoring. Finally, important clinical data elements such as birth weight, sex, APGAR scores, socioeconomic status, and aspects of the clinical exam are not fully integrated into the scoring system. Our overall hypothesis is that Artificial Intelligence (AI) algorithms on neonatal brain MRI and clinical data elements can provide higher sensitivity and specificity than the expert NRN MRI scores in predicting adverse HIE outcomes by 18-22 months. Our R61 Aims are as follows: Aim 1, Compile a large HIE dataset (N=430) from two completed NRN multi-site HIE trials; Aim 2, Develop an AI biomarker of outcome using neonatal brain MRI, and compare with NRN scores with Aim 2a focusing on MRI injury patterns and Aim 2b focusing on whole brain MRI signal intensity patterns; and Aim 3, Develop an AI biomarker of outcome combining clinical and MRI data, and compare with NRN scores. Go/No- Go criteria for the R33 is if at least one biomarker (2a, 2b, or 3) outperforms NRN MRI scores in our N=430 cohort (p<0.05; DeLong Test of AUC). The R33 Aim 4 is to further evaluate accuracy and reliability in a new cohort (N=231). Deliverables: Publicly released data and the AI software. Impact: A brain MRI and clinical AI- powered neonatal prognostic biomarker could expedite therapeutic innovations in future HIE trials worldwide.

抽象的 缺氧缺血性脑病 (HIE) 是一种约 5/1000 新生儿发生的脑损伤。 2005 年，美国国立卫生研究院 新生儿研究网络 (NRN) 建立了低温治疗 (TH)，在前 6 名患者中为患者降温 产后 33-34°C 持续 72 小时，作为高收入国家 HIE 的标准治疗。然而， 许多患者仍然经历不良后果（死亡或婴儿认知贝利发育量表<85） 18-22 个月。因此，从 2008 年到 2015 年，NRN 测试了更深、更长或更晚的 TH 是否会进一步减少不利的影响。 结果，在 21 个地点进行了两项试验。不幸的是，结果尚无定论，进一步进展缓慢， 主要是因为不良后果要到 18-22 个月才能可靠评估。为了加快治疗 创新并更及时地评估新疗法的影响，这是一个紧迫但尚未得到满足的问题 需要建立 18-22 个月不良结局的新生儿生物标志物。为了解决这一差距，NRN 利用神经放射学专家对临床获得的新生儿脑损伤进行评分，开发了这样一种生物标志物 脑部磁共振图像 (MRI)，称为 NRN MRI 评分。在一个读者的一个数据集中， 不良结果的敏感性/特异性为 81%/78%。然而，在另一个有两个读者的数据集中， 读者间的一致性程度中等，不良结果的特异性仅为 56-69%。问题 这个主观且耗时的评分系统是否可靠或完全表征复杂性的问题 HIE 损伤模式。同样在许多国家，没有专家可以进行 MRI 评分。最后， 重要的临床数据元素，例如出生体重、性别、APGAR 评分、社会经济状况等 临床考试的内容尚未完全纳入评分系统。我们的总体假设是人工 新生儿脑 MRI 和临床数据元素的智能 (AI) 算法可以提供更高的灵敏度和 在预测 18-22 个月的不良 HIE 结果方面，其特异性高于专家 NRN MRI 评分。我们的 R61 目标 目标如下： 目标 1，从两个已完成的 NRN 多站点 HIE 试验中编译大型 HIE 数据集（N=430）；目的 2、使用新生儿脑部 MRI 开发结果的 AI 生物标志物，并与 Aim 2a 的 NRN 评分进行比较 重点关注 MRI 损伤模式，Aim 2b 重点关注全脑 MRI 信号强度模式；和目标 3， 结合临床和 MRI 数据开发结果的 AI 生物标志物，并与 NRN 评分进行比较。去/不- R33 的 Go 标准是在我们的 N=430 中是否至少有一种生物标志物（2a、2b 或 3）优于 NRN MRI 评分 队列（p<0.05；AUC 的 DeLong 检验）。 R33 Aim 4 旨在进一步评估新的精度和可靠性 队列（N = 231）。可交付成果：公开发布的数据和人工智能软件。影响：脑 MRI 和临床 AI 强有力的新生儿预后生物标志物可以加快全球未来 HIE 试验的治疗创新。