The Sepsis ClinicAl Resource And Biorepository (SCARAB) Project

败血症临床资源和生物储存库 (SCARAB) 项目

基本信息

项目摘要

Sepsis is a major cause of organ failure and death in both children and adults around the world for which there are no specific therapies other than antibiotics and supportive care. A major reason for the lack of targeted therapies is our incomplete understanding of the genetic, environmental, microbial, socioeconomic and biologic factors that underly risk of and outcomes from human sepsis and that drive heterogeneity within this clinical syndrome. We propose to create a unique resource, the Sepsis ClinicAl Resource And Biorepository (SCARAB), a two center prospective study in adults and children with sepsis and critically ill controls that is designed to be both comprehensive and scalable to large multicenter implementation. To create this resource, we will leverage our experience with large scale prospective observational studies in the ICU, our novel method of non-invasively sampling the distal airspace in mechanically ventilated patients, our world class bioinformatics expertise, state-of-the-art phenotyping methodologies, and unique institutional resources. SCARAB will prospectively enroll subjects from pediatric and adult ICUs at VUMC and Meharry Medical College, an institution that cares for a primarily minority population. Subjects will be enrolled early in their illness and serial biologic specimens of blood, DNA, respiratory micro-droplets (from Heat Moisture Exchange (HME) filters), tracheal aspirates and urine will be collected. To study the end organ most commonly injured in sepsis, we will establish best practices for collection, processing and storage of lung fluid collected from HME filters, a novel method for sampling the distal airspace in ventilated patients. Working with our collaborators in Biomedical Informatics, we will develop automated phenotyping algorithms to identify patients with sepsis and organ failures including ARDS, AKI and delirium. We will use cutting edge natural language processing methods such as concept mapping to conduct detailed sub-phenotyping for clinically important variables that are challenging to phenotype on a large scale, such as hyper- and hypoactive forms of delirium. SCARAB will also be linked to unique VUMC resources such as MicroVU which retains clinical isolates of all microbial specimens at VUMC allowing large studies of host-pathogen interactions using contemporary multi-omics approaches. SCARAB clinical data and biospecimens will be accessible to the broader research community through an application on our web based study portal. Proposals will be reviewed by a Resource Utilization Committee comprising study investigators and national experts in sepsis and organ dysfunction. Data will be de-identified using the most rigorous security standards. SCARAB is also designed to be readily accessible to early career investigators by providing statistical support, customized phenotyping and access to Studios for expert engagement for study design. Bringing together all of these innovative elements will allow for mechanistic studies, development of predictive biomarkers, understanding of pre-hospital factors and clinical outcome studies in patients with sepsis and appropriate controls at a scale not previously possible.
脓毒症是全世界儿童和成人器官衰竭和死亡的主要原因, 除了抗生素和支持性治疗外,没有其他具体的治疗方法。缺乏针对性的一个主要原因 治疗是我们对遗传、环境、微生物、社会经济和生物学的不完全理解, 这些因素是人类脓毒症的风险和结局的基础,并推动了本临床研究中的异质性。 综合征我们建议创建一个独特的资源,脓毒症临床资源和生物储存库 (SCARAB),一项在成人和儿童脓毒症和危重病对照中进行的两中心前瞻性研究, 设计为既全面又可扩展到大型多中心实施。要创建此资源, 我们将利用我们在ICU进行的大规模前瞻性观察研究的经验, 在机械通气患者的远端空气中进行无创采样的方法,我们世界一流的 生物信息学专业知识、最先进的表型分析方法和独特的机构资源。 SCARAB将从VUMC和Meharry Medical的儿科和成人ICU前瞻性入组受试者 大学,一个主要照顾少数民族的机构。受试者将在他们的早期入组 疾病和血液、DNA、呼吸道微滴的系列生物标本(来自热湿交换 (HME)过滤器)、气管抽吸物和尿液。研究最常损伤的终末器官, 脓毒症,我们将建立从HME收集的肺液的收集、处理和储存的最佳实践 过滤器,一种用于对通气患者远端空气进行采样的新方法。与我们的合作伙伴一起, 生物医学信息学,我们将开发自动表型分析算法,以识别败血症患者, 器官衰竭包括ARDS、阿基和谵妄。我们将使用最先进的自然语言处理技术 方法,如概念映射,对临床重要变量进行详细的亚表型分析, 在大规模的表型上具有挑战性,例如谵妄的过度活跃和机能减退形式。SCARAB将 还与独特的VUMC资源相关联,例如MicroVU,其保留了所有微生物的临床分离株 VUMC的标本允许使用当代多组学进行宿主-病原体相互作用的大型研究 接近。航天器再入大气层和气动热分析方案的临床数据和生物标本将向更广泛的研究界开放 通过我们的网络学习门户网站上的应用程序。提案将由资源利用部审查 由败血症和器官功能障碍研究者和国家专家组成的委员会。数据将 使用最严格的安全标准去识别。航天器再入大气层和气动热碎裂方案的设计还使下列人员能够随时进入 通过提供统计支持,定制表型和访问工作室, 专家参与研究设计。将所有这些创新要素结合在一起, 机制研究,预测性生物标志物的开发,院前因素的理解和临床 在脓毒症患者和适当的对照组中进行的结局研究,其规模在以前是不可能的。

项目成果

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Julie Anne Bastarache其他文献

Julie Anne Bastarache的其他文献

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{{ truncateString('Julie Anne Bastarache', 18)}}的其他基金

The Sepsis ClinicAl Resource And Biorepository (SCARAB) Project
败血症临床资源和生物储存库 (SCARAB) 项目
  • 批准号:
    10353314
  • 财政年份:
    2022
  • 资助金额:
    $ 22.81万
  • 项目类别:
Neuroinflammatory mechanisms underlying sepsis-induced cognitive dysfunction
脓毒症引起认知功能障碍的神经炎症机制
  • 批准号:
    10525755
  • 财政年份:
    2022
  • 资助金额:
    $ 22.81万
  • 项目类别:
Neuroinflammatory mechanisms underlying sepsis-induced cognitive dysfunction
脓毒症引起认知功能障碍的神经炎症机制
  • 批准号:
    10835675
  • 财政年份:
    2022
  • 资助金额:
    $ 22.81万
  • 项目类别:
Applying Innovative Lung Mapping Strategies to Understand Alveolar Capillary Barrier Permeability in ARDS
应用创新的肺标测策略来了解 ARDS 中的肺泡毛细血管屏障渗透性
  • 批准号:
    10650403
  • 财政年份:
    2020
  • 资助金额:
    $ 22.81万
  • 项目类别:
Applying Innovative Lung Mapping Strategies to Understand Alveolar Capillary Barrier Permeability in ARDS
应用创新的肺标测策略来了解 ARDS 中的肺泡毛细血管屏障渗透性
  • 批准号:
    10424547
  • 财政年份:
    2020
  • 资助金额:
    $ 22.81万
  • 项目类别:
Applying Innovative Lung Mapping Strategies to Understand Alveolar Capillary Barrier Permeability in ARDS
应用创新的肺标测策略来了解 ARDS 中的肺泡毛细血管屏障渗透性
  • 批准号:
    9894231
  • 财政年份:
    2020
  • 资助金额:
    $ 22.81万
  • 项目类别:
Potential Protective Mechanisms of Tissue Factor in Acute Lung Injury
组织因子在急性肺损伤中的潜在保护机制
  • 批准号:
    10045936
  • 财政年份:
    2017
  • 资助金额:
    $ 22.81万
  • 项目类别:
Institutional Career Development Core
机构职业发展核心
  • 批准号:
    10591586
  • 财政年份:
    2017
  • 资助金额:
    $ 22.81万
  • 项目类别:
Targeting cell-free hemoglobin in sepsis to reduce lung microvascular permeability: mechanistic and translational studies
靶向脓毒症中的无细胞血红蛋白以降低肺微血管通透性:机制和转化研究
  • 批准号:
    9922349
  • 财政年份:
    2017
  • 资助金额:
    $ 22.81万
  • 项目类别:
NRSA Training Core
NRSA 培训核心
  • 批准号:
    10591563
  • 财政年份:
    2017
  • 资助金额:
    $ 22.81万
  • 项目类别:

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机械建模与机器学习相结合诊断急性呼吸窘迫综合征
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