Consortium Of MRI Biomarkers In Neonatal Encephalopathy (COMBINE)

摘要 新生儿缺氧缺血性脑病(HIE)是一种新生儿脑损伤，发病率为5/1000。2005年，美国国立卫生研究院 新生儿研究网络(NRN)建立了治疗性低温(TH)，为前6名患者降温 将出生后时间延长至33-34摄氏度，持续72小时，作为高收入国家HIE的标准治疗。然而， 许多患者仍有不良结局(死亡或认知贝利婴儿发育量表) 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是否进一步减少了不利影响 结果，在21个地点进行了两项试验。不幸的是，结果并不确定，进一步的进展缓慢， 这在很大程度上是因为直到18-22个月才能可靠地评估不良后果。为了加快治疗 更及时地评估创新和评估新疗法的影响，存在着一个紧迫但尚未得到满足的问题 需要建立一个18-22个月不良结局的新生儿生物标记物。为了解决这一差距，NRN 利用神经放射学专家对临床获得性新生儿脑损伤的评分开发了这样的生物标记物 脑磁共振成像(MRI)，称为NRN MRI评分。在具有一个读取器的一个数据集中， 不良结果的敏感度/特异度为81%/78%。但是，在另一个具有两个读取器的数据集中， 读者之间的一致性只是中等的，对不良结果的特异性只有56-69%。问题 关于这个主观和耗时的评分系统是可靠的还是完全具有复杂性的 他的受伤模式。同样在许多国家，没有专家可以进行核磁共振评分。最后， 重要的临床数据元素，如出生体重、性别、APGAR评分、社会经济状况和方面 临床考试的分数没有完全纳入评分系统。我们的总体假设是，人工的 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敏感度和 在预测18-22个月的HIE不良结局方面，NRN MRI专家评分的特异度更高。我们的R61目标是 目标1，从两个已完成的NRN多点HIE试验中汇编一个大型HIE数据集(N=430)； 2、利用新生儿脑MRI建立AI预后生物标志物，并与AIM 2a的NRN评分进行比较 聚焦于MRI损伤模式和目标2b聚焦于全脑MRI信号强度模式；以及目标3， 结合临床和MRI数据开发AI预后生物标志物，并与NRN评分进行比较。去/不去- R33的GO标准是至少有一个生物标记物(2a、2b或3)在N=430的情况下超过NRN MRI得分 队列(p&lt；0.05；AUC的德龙检验)。R33目标4是进一步评估新的 队列(N=231)。交付成果：公开发布的数据和人工智能软件。影响：脑部核磁共振和临床人工智能- 动力型新生儿预后生物标记物可以在未来的全球HIE试验中加速治疗创新。

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.