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

产时相关新生儿脑病的流行病学：探索全球范围内的患病率和结果预测因素

• 批准号: 2734765
• 金额: --
• 依托单位: London School of Hygiene & Tropical Medicine
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2734765
• 关键词: Epidemiology; Intrapartum; related; Neonatal; Encephalopathy

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specific PhD objectives and methodologies are:Objective 1: Systematic review and meta-analysis of published and unpublished global data to determine prevalence, and predictors, of adverse outcomes (death and disability =/+18 months) after NE.i) Conduct a literature search using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country classification by income (as per World Bank), and cooling intervention status.ii) Identify sources of unpublished data (including national networks, cooling cohorts/ registers) through the literature review and professional networking; contact investigators for relevant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 iii) Combine comparable published and unpublished data in random-effects meta-analysis.Objective 2: Develop a novel risk score to identify those at highest risk of adverse outcomes after NE, applicable for use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

出生时的并发症导致新生儿脑损伤，被称为“产中相关性新生儿脑病”(NE)，是全球儿童死亡和残疾的主要原因。全球议程的重点是儿童不仅要“生存”，而且要“茁壮成长”。关于NE后不良后果(死亡或残疾)流行率的数据有限，特别是在低收入和中等收入国家(LMIC)。在确定哪些最有可能受益于有针对性的干预措施的死亡或残疾风险最大的人方面存在挑战。现有的临床风险评分(包括各种新生儿临床特征)在LMIC环境中的适用性有限。该博士项目旨在探索全球NE后不良结局(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LMIC的不良结局风险。具体的博士目标和方法是：目标1：对已发表和未发表的全球数据进行系统审查和荟萃分析，以确定NE.i后不良结局(死亡和残疾=/+18个月)的患病率和预测因素)使用与新生儿脑病相关的术语进行文献搜索；将发表日期限制为2012年11月1日起(自Lee等人2013年上次全球系统评价以来)。摘录NE后不良后果(死亡和残疾=/+18个月)的患病率和预测因素的数据。2)通过文献综述和专业网络确定未公布数据的来源(包括国家网络、冷却队列/登记册)；与调查人员联系，获取有关NE后患病率和不良后果预测因素的相关数据。3)在随机效应荟萃分析中结合可比的已发表和未发表的数据。目标2：开发一种新的风险评分来确定NE后不良结局风险最高的人，适用于LMIC。i)从240名患有NE的乌干达婴儿的样本(乌干达婴儿脑和ABAANA研究)和通过目标1确定的其他队列中得出模型。通过文献综述(目标1)确定潜在的候选变量，这将包括母亲和新生儿的社会人口学/临床特征。将变量包括在一个完整的多变量模型中，使用反向逐步选择逐步简化。目标3：评估新风险评分在LMIC环境下在医护人员中的可行性和可接受性。i)使用R计算机软件创建新的基于网络的风险计算器，以便能够输入患者特定的数据并计算在NE.ii)培训医护人员样本(例如，来自正在进行的撒哈拉以南非洲的NEST360研究)以在高危新生儿中使用基于网络的计算器管理评分。使用定性和定量相结合的方法来评估医务工作者的可行性和可接受性。目标4：利用来自婴儿大脑研究的数据，描述头颅超声的发现，包括阻力指数(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与NE(N=40)不良结局的相关性。使用回归模型评估颅脑超声损伤类型(N=184)和NE术后不良结局之间的关系。iii)利用存储的婴儿大脑血清样本和ABAANA研究(N=250)，评估早期代谢和蛋白质组学特征(出生第2天)与不良结局之间的关系