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: 10436592
• 负责人: Patricia Ellen Grant
• 金额: $ 94.26万
• 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/10436592
• 关键词: Address; Adverse event; Agreement; Apgar Score; Area; Artificial Intelligence; Biological Markers; Birth Weight; Boston; Brain; Brain Injuries; 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; 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 低氧缺血性脑病（HIE）是在〜5/1000新生儿中发生的脑损伤。 2005年，NIH 新生儿研究网络（NRN）建立了治疗体温过低（TH），在前6名冷却患者 产后小时至33-34°C 72小时，作为高收入国家的HIE的标准治疗。然而， 许多患者仍然经历不良结果（婴儿发育的死亡或认知贝利量表<85） 到18-22个月。从2008年到2015年，NRN测试了是否更深，更长或以后再降低不良 结果，在21个地点进行了两次试验。不幸的是，结果尚无定论，进一步的进展缓慢， 很大程度上是因为直到18-22个月才能可靠地评估不良结果。加快治疗 创新和评估更及时的新疗法的影响，有一个紧急但未定的 需要建立18-22个月不良结果的新生儿生物标志物。为了解决这个差距，NRN 使用神经放射学专家对脑损伤进行了临床获得的新生儿评分，开发了这种生物标志物 脑磁共振图像（MRI），称为NRN MRI评分。在一个数据集中，有一个读者， 广告结果的敏感性/特异性为81％/78％。但是，在另一个具有两个读者的数据集中 阅读器间协议仅是中等的，对不良结果的特异性仅为56-69％。问题 出现这种主观和耗时的评分系统是可靠的还是完全表征复杂的 Hie伤害模式。同样在许多国家，也没有专家可以执行MRI评分。最后， 重要的临床数据元素，例如出生体重，性别，Apgar分数，社会经济状况和方面 临床检查的中没有完全整合到评分系统中。我们的总体假设是人造的 关于新生儿脑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 特异性比专家NRN MRI分数在预测不良结果的情况下为18-22个月。我们的R61目标 如下：AIM 1，从两个完成的NRN多站点HIE试验中编译了一个大型HIE数据集（n = 430）；目的 2，使用新生儿脑MRI开发结果的AI生物标志物，并与NRN分数与AIM 2A进行比较 专注于MRI损伤模式，AIM 2B专注于整个大脑MRI信号强度模式；和目标3， 开发结合临床和MRI数据的结果的AI生物标志物，并与NRN分数进行比较。去/no- R33的GO标准是至少一个生物标志物（2a，2b或3）在我们的n = 430中的表现优于NRN MRI分数 队列（P <0.05； AUC的Delong检验）。 R33 AIM 4是进一步评估新的准确性和可靠性 队列（n = 231）。可交付成果：公开发布的数据和AI软件。影响：大脑MRI和临床AI- 动力的新生儿预后生物标志物可以在全球未来的HIE试验中加快治疗创新。