Epidemiology of Intrapartum-related Neonatal Encephalopathy: Exploring Prevalence and Predictors of Outcomes Globally

出生时的并发症导致新生儿脑损伤，被称为“产时相关新生儿脑病”，是全世界儿童死亡和残疾的主要原因。全球议程的重点是儿童不仅需要“生存”，而且需要“茁壮成长”。关于NE后不良后果（死亡或残疾）发生率的数据有限，特别是在低收入和中等收入国家。在确定可能从有针对性的干预措施中受益的最有可能死亡或残疾的人方面存在挑战。现有的临床风险评分（包括各种新生儿临床特征）在低mic环境中的适用性有限。该博士项目旨在探索全球范围内NE后不良后果（死亡和残疾）的患病率和预测因素，并开发一种新的评分方法来预测适用于中低收入国家的不良后果风险。具体的博士目标和方法如下：目标1：对已发表和未发表的全球数据进行系统回顾和荟萃分析，以确定NE后不良结果（死亡和残疾=/+18个月）的患病率和预测因素。i)使用新生儿脑病相关术语进行文献检索；将发表日期限制在2012年11月1日以后（自Lee等人2013年进行的上一次全球系统综述以来）。提取有关NE后不良结局（死亡和残疾=/+18个月）的患病率和预测因素的数据。按新能源定义、按收入（按世界银行）划分的国家分类和降温干预状况对结果进行分层。ii)通过文献审查和专业网络确定未发表数据的来源（包括国家网络、冷却队列/登记册）；联系调查人员获取有关NE后不良后果的流行率和预测因素的相关数据。iii)在随机效应荟萃分析中结合可比较的已发表和未发表的数据。目标2：开发一种新的风险评分，以识别NE术后不良后果风险最高的人群，适用于中低收入国家。i)从240名患有NE的乌干达婴儿样本（婴儿大脑和ABAaNA研究，乌干达）以及通过目标1确定的其他队列中导出模型。通过文献回顾（目标1）确定潜在的候选变量，包括产妇和新生儿的社会人口统计学/临床特征。在一个完整的多变量模型中包含变量，使用反向逐步选择逐步简化。ii)使用开发样本的自举重新抽样在内部验证风险评分。目的3：在低收入和中等收入国家背景下，评估新型风险评分在卫生保健工作者（HCWs）中的可行性和可接受性。i)使用R计算机软件创建一个新颖的基于网络的风险计算器，以便输入患者特定数据并计算NE后不良后果的个性化风险。㈡培训一批卫生保健工作者（例如来自撒哈拉以南非洲正在进行的NEST360研究），以便在高危新生儿中使用基于网络的计算器进行评分。采用混合的定性和定量方法评估卫生保健中心的可行性和可接受性。目的4：探讨脑成像和新型生物标志物在预测NE术后不良结局中的附加价值。i)利用婴儿大脑研究的数据，描述颅超声的发现，包括电阻指数（N=51）、磁共振成像（N=27）和磁共振波谱（N=24），并使用回归模型评估NE后不良后果的相关性（N=40）。ii)利用ABAaNA研究的数据，利用回归模型评估颅脑损伤超声模式（N=184）与NE术后不良结局之间的关系。iii)利用婴儿大脑和ABAaNA研究中储存的血清样本（N= 250），评估早期代谢组学和蛋白质组学特征（出生第2天）与不良结局之间的关联

Complications around the time of birth leading to newborn brain injury, known as 'Intrapartum-related Neonatal Encephalopathy' (NE), is a leading cause of child death and disability worldwide. The global agenda focuses on the need for children not only to 'survive' but 'thrive'. Data is limited on the prevalence of adverse outcomes (death or disability) after NE particularly in low- and middle- income countries (LMICs). Challenges exist in identifying those most at risk of death or disability who may benefit from targeted interventions. Existing clinical risk scores (comprising a variety of neonatal clinical characteristics) have limited applicability in LMIC settings. This PhD project aims to explore the prevalence, and predictors, of adverse outcomes (death and disability) after NE globally, and develop a novel score to predict risk of adverse outcomes applicable for use in LMICs.The specific PhD objectives and methodologies are:Objective 1: Systematic review and meta-analysis of published and unpublished global data to determine prevalence, and predictors, of adverse outcomes (death and disability =/+18 months) after NE.i) Conduct a literature search using terms related to Neonatal Encephalopathy; limit publication date to 1 November 2012 onwards (since the last global systematic review by Lee et al 2013). Extract data on prevalence, and predictors, of adverse outcomes (death and disability =/+18 months) after NE. Stratify results by NE definition, country classification by income (as per World Bank), and cooling intervention status.ii) Identify sources of unpublished data (including national networks, cooling cohorts/ registers) through the literature review and professional networking; contact investigators for relevant data on prevalence and predictors of adverse outcomes after NE. iii) Combine comparable published and unpublished data in random-effects meta-analysis.Objective 2: Develop a novel risk score to identify those at highest risk of adverse outcomes after NE, applicable for use in LMICs.i) Derive the model from a sample of 240 Ugandan infants with NE (Baby BRAiN and ABAaNA studies, Uganda), and additional cohorts identified through objective 1. Identify potential candidate variables through literature review (objective 1) which will comprise maternal and neonatal sociodemographic/ clinical characteristics. Include variables in a complete multivariable model, progressively simplified using reverse stepwise selection. ii) Internally validate the risk score using bootstrap resampling of the development sample.Objective 3: Assess the feasibility and acceptability of the novel risk score amongst healthcare workers (HCWs) in LMIC contexts.i) Create a novel web-based risk calculator using the R computer software, to enable entering of patient-specific data and calculation of individualised risk of adverse outcomes after NE.ii) Train a sample of healthcare workers (e.g. from the ongoing NEST360 study in sub-Saharan Africa) to administer the score using the web-based calculator, in high-risk neonates. Utilise mixed qualitative and quantitative methods to evaluate feasibility and acceptability amongst HCWs.Objective 4: Explore the additional value of brain imaging and novel biomarkers in predicting adverse outcomes after NE.i) Utilising data from the baby BRAiN study, describe findings from cranial ultrasound including resistive indices (N=51), magnetic resonance imaging (N=27), and magnetic resonance spectroscopy (N=24), and evaluate associations with adverse outcomes after NE (N=40), using regression modelling.ii) Utilising data from the ABAaNA study, evaluate associations between cranial ultrasound patterns of injury (N=184) and adverse outcomes after NE using regression modelling.iii) Utilising stored serum samples from the baby BRAiNS and ABAaNA studies (N= 250), evaluate associations between early metabolomic and proteomic profiles (birth-day 2), and adverse outcomes