Sepsis endotyping using clinical and biological data

使用临床和生物学数据进行脓毒症内分型

• 批准号: 9765334
• 负责人: Christopher Warren Seymour
• 金额: $ 39.13万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-02 至 2021-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9765334
• 关键词: American; Big Data; Bioinformatics; Biological; Biological Markers; Blood; Cessation of life; Characteristics; Clinical; Clinical Trials; Complex; Computational Biology; Data; Derivation procedure; Early treatment; Electronic Health Record; Enrollment; Ethers; Future; Hospitals; Immune response; Inflammation; K-Series Research Career Programs; Laboratories; Link; Measures; Mentors; Methods; Outcome; Pathogenesis; Patients; Play; Research; Residual state; Rheumatology; Role; Scientist; Sepsis; Sepsis Syndrome; Specimen; Subgroup; Supervision; Syndrome; Systems Biology; Tissues; Translating; Validation; Work; biobank; improved outcome; innovation; insight; mortality; novel; oncology; pathogen; programs; septic patients; targeted treatment

PROJECT SUMMARY / ABSTRACT More than 2 million Americans are hospitalized with sepsis each year, and account for 1 out of every 2 to 3 hospital deaths. Most efforts to reduce mortality among septic patients begin with the premise that patients are largely similar, and that ether moving treatment earlier or targeting therapeutics to a single mechanism will improve outcomes. Drawing insights from rheumatology and oncology, as well as my sepsis biomarker work during my K23 mentored career development award, we argue that endotypes (biologic subtypes) play an important role in the pathogenesis and outcome of sepsis. When measured early in the sepsis syndrome, these endotypes may identify distinct subgroups comprised of immune response, host tolerance, cellular and tissue damage, and pathogen characteristics. I propose to leverage our clinical translational laboratory to derive and validate novel sepsis endotypes using bioinformatics methods in electronic health record (EHR) data linked to a biorepository of residual blood. This innovative program of research translate findings from “big data” in the EHR and efficiently enrolled biologic specimens into generalizable bio-types for enrichment strategies in future clinical trials and EHR alerts. My program will be supervised by an external advisory board of experts in endotyping, inflammation, and computational and systems biology, while promoting the independence of my clinical-translational laboratory and mentoring of junior scientists.

项目概要/摘要 每年有超过 200 万美国人因败血症住院，其中 1 每 2 至 3 例医院死亡病例中就有 1 例。为降低脓毒症患者死亡率所做的最大努力 患者开始的前提是患者在很大程度上相似，并且乙醚移动 早期治疗或针对单一机制的治疗将改善结果。 从风湿病学和肿瘤学以及我的脓毒症生物标志物工作中汲取见解 在我的 K23 指导职业发展奖期间，我们认为内型（生物 亚型）在脓毒症的发病机制和结果中发挥重要作用。什么时候 在脓毒症综合征的早期进行测量，这些内型可以识别不同的亚组 包括免疫反应、宿主耐受性、细胞和组织损伤，以及 病原体特征。我建议利用我们的临床转化实验室 使用电子生物信息学方法推导并验证新型脓毒症内型 与残留血液生物储存库相关的健康记录 (EHR) 数据。这种创新的 研究计划将电子病历中“大数据”的发现转化为有效的登记 将生物样本转化为可概括的生物类型，以用于未来的富集策略 临床试验和 EHR 警报。我的计划将由外部顾问监督 内分型、炎症、计算和系统生物学专家委员会， 同时促进我的临床转化实验室和指导的独立性 的青年科学家。