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: 9140876
• 负责人: Christopher Warren Seymour
• 金额: $ 37.74万
• 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/9140876
• 关键词: Accounting; American; Big Data; Bioinformatics; Biological; Biological Markers; Blood; Cessation of life; Characteristics; Clinical; Clinical Trials; Complex; 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; Syndrome; Systems Biology; Tissues; Translating; Validation; Work; biobank; improved outcome; innovation; insight; mortality; novel; oncology; pathogen; programs; septic; 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.

项目摘要/摘要