Pre-hospital identification of high-risk sepsis

高危脓毒症的院前识别

• 批准号: 8424368
• 负责人: Christopher Warren Seymour
• 金额: $ 18.7万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8424368
• 关键词: Accident and Emergency department; Accounting; Acute; Address; American; Antibiotics; Biological Markers; Biometry; Blood specimen; Cardiovascular Diseases; Caring; Cessation of life; Classification; Clinical; Clinical Data; Coagulation Process; Complement; Coupling; Critical Illness; Data; Databases; Diagnosis; Diagnostic; Discrimination; Disease; Emergency Care; Emergency medical service; Ethics; Evaluation; Fibrinolysis; Force of Gravity; Functional disorder; Funding; Future; Goals; Healthcare Systems; Homeostasis; Hospital Administration; Hospitalization; Hospitals; Human Resources; Incidence; Individual; Infection; Inflammation; Interleukin-6; Intervention; Investments; Knowledge; Laboratories; Lead; Link; Liquid substance; Mass Spectrum Analysis; Measures; Medical; Mentors; Metabolic; Modeling; Nested Case-Control Study; Organ; Outcome; Pancreatitis; Patient Care; Patients; Play; Population; Prospective Studies; Records; Research; Research Project Grants; Resources; Resuscitation; Retrospective Studies; Risk; Role; Science; Sepsis; Severity of illness; Shock; Staging; Stroke; System; Testing; Time; Training; Training Programs; Translating; Trauma; Triage; United States National Institutes of Health; University of Pittsburgh Cancer Institute; Validation; Work; base; career development; cohort; cost; design; high risk; high throughput technology; improved; insight; metabolomics; mortality; novel; novel strategies; outcome forecast; performance tests; prognostic; prospective; public health relevance; rapid growth; research study; skills; small molecule; tool; training project

DESCRIPTION (provided by applicant): Sepsis represents a large and increasing burden on the US health care system. The incidence of sepsis is greater than 3.0 per 1,000 population and case fatalities approach 20%, accounting for more than $17 billion annually in direct medical costs. Recent advances in sepsis therapy, such as time-sensitive antibiotic administration and fluid resuscitation, require prompt diagnosis, and in some cases, transfer to referral centers for definitive therapy. Until now, such early recognition occurs after patients arrive at hospitals, when a critical window for treatment and referral may already have passed. An alternative approach may be to diagnose and treat sepsis in the pre-hospital period (which, as shown in preliminary work, is often of considerable duration). Emergency medical services (EMS) systems already play a key role in the pre-hospital management of acute cardiovascular disease, stroke, and trauma, conditions that also benefit from care at optimal centers with advanced warning. Although EMS transports over twice as many cases of sepsis as of acute cardiovascular disease, there are no prehospital tools to identify or manage sepsis, potentially leading to missed opportunities, and avoidable deaths.16 Key to effective pre-hospital care and triage decisions is the need to estimate both the probable diagnosis and the severity of illness. My proposed research project addresses this problem by coupling clinical prediction modeling with cutting edge biomarker science to identify and risk stratify prehospital patients for treatmen of sepsis. I take a novel approach by simultaneously integrating diagnostic and prognostic information for sepsis using clinical data and biomarkers. The main portion of the research focuses on existing, widely available clinical and biologic data. However, recognizing the rapid growth of novel biomarker discovery platforms, I have also included an exploratory aim using an agnostic, global metabolomics approach. The proposed research is organized as three aims. Aim #1 will develop an integrated model for sepsis and mortality risk using a large, existing prehospital clinical database. Aim #2 will determine the incremental knowledge gained from individual and combinations of candidate prehospital biomarkers for sepsis and mortality risk in a modest prospective study. Aim #3 will explore novel mass-spectrometry-based, metabolomic biomarkers of prehospital sepsis and mortality using a global, agnostic discovery approach in a small nested subset of the Aim #2 cohort. I will conduct this research under the guidance of my mentor Dr. Derek Angus and a team of expert advisors with whom I have established relationships. I will also complement the research with a didactic training program focusing on: 1) advanced biostatistics of risk modeling, 2) the methodologic tools necessary to conduct rigorous biomarker analysis and discovery, and 3) the practical, ethical, and management skills to perform prospective, prehospital research. Together, the didactic training and research project are designed to provide me with the knowledge and skills critical for successful inter-disciplinary research that translates insights from biomarker and risk modeling into a strategy for prehospital recognition of sepsis. My goal is that these experiments will set the stage for future NIH-funded trials of patient and system-level interventions in prehospital sepsis. I submit that this investment in my career development will contribute to new knowledge regarding the mechanism underlying nascent sepsis and the predictive role of prehospital clinical data and biomarkers, and has the potential to improve the emergency care of our highest acuity, most resource-intensive patients.

描述（由申请人提供）：脓毒症对美国医疗保健系统来说是一个巨大且不断增加的负担。败血症的发病率超过每 1,000 人 3.0 人，病死率接近 20%，每年直接医疗费用超过 170 亿美元。脓毒症治疗的最新进展，例如时间敏感的抗生素给药和液体复苏，需要及时诊断，并且在某些情况下，需要转移到转诊中心进行明确治疗。到目前为止，这种早期识别是在患者到达医院之后进行的，此时治疗和转诊的关键窗口可能已经过去了。另一种方法可能是在入院前阶段诊断和治疗脓毒症（如初步工作所示，这一阶段通常持续相当长的时间）。紧急医疗服务 (EMS) 系统已经在急性心血管疾病、中风和创伤的院前管理中发挥着关键作用，这些疾病也受益于具有预先预警的最佳中心的护理。尽管 EMS 转运的脓毒症病例数量是急性心血管疾病病例的两倍多，但没有院前工具来识别或管理脓毒症，这可能会导致错失机会和可避免的死亡。 16 有效的院前护理和分诊决策的关键是需要估计可能的诊断和疾病的严重程度。我提出的研究项目通过将临床预测模型与尖端生物标志物科学相结合来解决这个问题，以识别脓毒症治疗的院前患者并对其进行风险分层。我采用了一种新颖的方法，使用临床数据和生物标志物同时整合脓毒症的诊断和预后信息。该研究的主要部分集中于现有的、广泛可用的临床和生物学数据。然而，认识到新型生物标志物发现平台的快速增长，我还使用不可知的全局代谢组学方法纳入了探索性目标。拟议的研究分为三个目标。目标#1将利用现有的大型院前临床数据库开发脓毒症和死亡风险的综合模型。目标#2将在一项适度的前瞻性研究中确定从败血症和死亡风险的候选院前生物标志物的个体和组合中获得的增量知识。目标#3 将在目标#2 队列的一小部分嵌套子集中使用全局、不可知的发现方法，探索院前败血症和死亡率的新型基于质谱的代谢组生物标志物。我将在我的导师德里克·安格斯博士和与我建立了关系的专家顾问团队的指导下进行这项研究。我还将通过教学培训计划来补充研究，重点是：1）风险建模的高级生物统计学，2）进行严格的生物标志物分析和发现所需的方法工具，以及3）进行前瞻性院前研究的实践、道德和管理技能。教学培训和研究项目旨在为我提供成功跨学科研究至关重要的知识和技能，将生物标志物和风险建模的见解转化为策略 败血症的院前识别。我的目标是，这些实验将为未来由 NIH 资助的院前脓毒症患者和系统级干预试验奠定基础。我认为，对我职业发展的这项投资将有助于获得有关初生败血症的机制以及院前临床数据和生物标志物的预测作用的新知识，并有可能改善我们最敏锐、资源最密集的患者的急诊护理。