Sepsis endotyping using clinical and biological data

项目摘要/摘要 每年有200多万美国人因脓毒症住院，占1 每2到3例医院死亡中就有1例死亡。为降低脓毒症死亡率所做的最大努力 病人开始的前提是病人大体相似，并且以太移动 更早的治疗或针对单一机制的治疗将改善结果。 从风湿病和肿瘤学以及我的败血症生物标记物工作中获得洞察力 在我的K23指导职业发展奖期间，我们认为内型(生物) 各亚型)在脓毒症的发病机制和转归中起重要作用。什么时候 在脓毒症综合征早期测量，这些内型可以识别不同的亚群 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来 用生物信息学方法推导和验证新的脓毒症内型 健康记录(EHR)数据链接到残留血液的生物储存库。这是一项创新 研究计划将研究成果转化为电子病历中的大数据并有效地录用 将生物标本归纳为可推广的生物类型，为未来的浓缩策略做准备 临床试验和电子健康记录警报。我的项目将由外部顾问监督 内生型、炎症、计算和系统生物学专家委员会， 同时促进我的临床翻译实验室和指导的独立性 初级科学家。

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.