Fever Etiology and Prognostic Point-of-Care Biomarkers in African Children with Severe Febrile Illness

摘要 严重的儿科发热病具有较高的全球死亡率，并不成比例地影响儿童资源-- 撒哈拉以南非洲(SSA)的有限环境。虽然快速，适当的治疗可以改善预后 严重的发热性疾病，我们缺乏对如何快速识别需要紧急和 具体管理。我的中心假设是，下一代测序可以提高对 与培养相比，严重的细菌感染和生物标记物的护理点测试可以预测严重的 有严重发热性疾病的儿童的细菌感染和临床恶化。与儿科 严重感染协作(UCSF、坦桑尼亚Muhimbili医院和Chan Zuckerberg BioHub in 旧金山)，这项提案利用了使用临床数据、尖端病原体检测 方法，一个创新的生物信息学平台(Global IDSeq)，以及开发治疗的生物标记物 非洲严重发热性疾病的算法。在这项坦桑尼亚重症儿童的前瞻性队列研究中 发热性疾病，我们将确定与不良临床结果相关的风险因素(目标1)，并同时采用这两种方法 本地可用的诊断技术(目标1)和最先进的下一代测序 严重发热性疾病的病因(目标2)。使用来自AIMS 1和2的数据，我们将确定哪些POCT用于 宿主血清生物标志物(降钙素原、C反应蛋白、铁蛋白、乳酸、血糖和血红蛋白)最佳 预测严重细菌感染和临床恶化(目标3)并开发治疗算法 纳入指导管理和资源分配的临床体征和生物标记物。 我的长期职业目标是改善患有严重发热病的儿童的临床结果 制定和实施以证据为基础的干预措施，这些干预措施对 资源受限设置。这个以患者为导向的导师研究职业发展奖为我提供了 机会：(1)培养成为独立内科医生-科学家所需的技能和专业知识 儿科重温病领域；(2)整合特定人群的临床数据，本地可用诊断 数据、尖端的下一代测序科学和宿主生物标记物，以更好地识别严重细菌 感染和高危儿童；(3)在坦桑尼亚建立进一步的临床研究基础设施和流程 未来的临床研究；以及(4)学习和应用使用生物信息学的下一代测序数据分析 对复杂观测数据的方法和高级生物统计分析。我已经召集了一位导师， 具有不同专业知识的协作团队，他们致力于帮助我实现这些目标。之后 完成这项建议，使用当地的临床数据，高度敏感的病原体检测方法，以及 创新的生物信息学平台，以及通过护理点生物标志物进行风险分层的独特方法， 我们将独一无二地创建基于证据的、特定于上下文的治疗算法，以改进 SSA中患有严重发热性疾病的儿童的结果，这将在R01资助的研究中进行测试。

ABSTRACT Severe pediatric febrile illness has high global mortality and disproportionately affects children in resource- limited settings in sub-Saharan Africa (SSA). Though rapid, appropriate treatment can improve outcomes in severe febrile illness, we lack a robust understanding of how to rapidly identify children in need of urgent and specific management. My central hypotheses are that next generation sequencing can improve detection of a serious bacterial infection compared to culture and that biomarker point-of-care tests can predict a serious bacterial infection and clinical deterioration in children presenting with severe febrile illness. With the Pediatric Severe Infection Collaboration (UCSF, Muhimbili Hospital in Tanzania, and the Chan Zuckerberg Biohub in San Francisco), this proposal leverages expertise that uses clinical data, cutting-edge pathogen detection methods, an innovative bioinformatics platform (Global IDSeq), and biomarkers to develop a treatment algorithm for severe febrile illness in Africa. In this prospective cohort study of Tanzanian children with severe febrile illness, we will determine risk factors associated with poor clinical outcomes (Aim 1) and employ both locally available diagnostic techniques (Aim 1) and state-of-the-art next generation sequencing to determine the etiology of severe febrile illness (Aim 2). Using data from Aims 1 & 2, we will determine which POCTs for host serum biomarkers (procalcitonin, C-reactive protein, ferritin, lactate, blood sugar, and hemoglobin) best predict serious bacterial infection and clinical deterioration (Aim 3) and develop a treatment algorithm incorporating clinical signs and biomarkers that will guide management and resource allocation. My long-term career objective is to improve clinical outcomes for children with severe febrile illness through the development and implementation of evidence-based interventions that are appropriate and context-relevant for resource-limited settings. This Mentored Patient-Oriented Research Career Development Award offers me the opportunity to: (1) develop skills and expertise necessary to become an independent physician-scientist in the field of pediatric severe febrile illness; (2) integrate population-specific clinical data, locally available diagnostic data, cutting-edge next generation sequencing science, and host biomarkers to better identify serious bacterial infections and high-risk children; (3) build further clinical research infrastructure and processes in Tanzania for future clinical studies; and (4) learn and apply next-generation sequencing data analysis using a bioinformatics approach and advanced biostatistical analyses of complex observational data. I have assembled a mentoring, collaborative team with diverse expertise who are committed to helping me achieve these goals. After completion of this proposal that employs local clinical data, a highly sensitive method of pathogen detection, an innovative bioinformatics platform, and a unique approach to risk stratification with point-of-care biomarkers, we will be uniquely positioned to create an evidence-based, context–specific treatment algorithm to improve outcomes in children with severe febrile illness in SSA, which will be tested in an R01-funded study.