Sepsis endotyping using clinical and biological data

项目总结/摘要 每年有200多万美国人因败血症住院，占美国总人数的1 每2到3例医院死亡。为降低败血症患者死亡率所作的努力 病人开始的前提是，病人在很大程度上是相似的， 早期治疗或针对单一机制的治疗将改善结果。 从血液流变学和肿瘤学以及我的败血症生物标志物工作中汲取见解 在我的K23指导职业发展奖，我们认为，内型（生物 亚型）在脓毒症的发病机制和结果中起重要作用。当 在败血症综合征的早期测量，这些内型可以识别不同的亚群 包括免疫应答、宿主耐受性、细胞和组织损伤， 病原特征我建议利用我们的临床转化实验室， 在电子计算机中使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）数据与残留血液的生物储存库相关联。这一创新 研究计划将EHR中的“大数据”的发现转化为有效的注册 将生物标本分成可推广的生物型，以供今后采用富集战略 临床试验和EHR警报。我的项目将由外部顾问监督 内定型、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的独立性， 年轻的科学家。

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.