Pre-hospital identification of high-risk sepsis

描述（由申请人提供）：脓毒症是美国医疗保健系统的一个巨大且不断增加的负担。脓毒症的发病率大于3.0/1000人，病死率接近20%，每年的直接医疗费用超过170亿美元。脓毒症治疗的最新进展，如时间敏感的抗生素给药和液体复苏，需要及时诊断，在某些情况下，转移到转诊中心进行明确的治疗。到目前为止，这种早期识别发生在患者到达医院之后，此时治疗和转诊的关键窗口可能已经过去。另一种方法可能是在院前阶段诊断和治疗脓毒症（如初步工作所示，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了关键作用，这些疾病也受益于具有先进预警的最佳中心的护理。尽管EMS运送的败血症病例是急性心血管疾病的两倍多，但没有院前工具来识别或管理败血症，可能导致错过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，以识别和风险分层院前患者治疗脓毒症。我采取了一种新的方法，通过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。研究的主要部分集中在现有的，广泛可用的临床和生物学数据。然而，认识到新的生物标志物发现平台的快速增长，我也包括了一个探索性的目标，使用不可知的，全球代谢组学方法。拟议的研究分为三个目标。目标#1将使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将在适度的前瞻性研究中确定从脓毒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队列的小型嵌套子集中使用全局不可知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学生物标志物。我将在我的导师德里克安格斯博士和一个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学工具，以及3）进行前瞻性，院前研究的实用，道德和管理技能。总之，教学培训和研究项目旨在为我提供成功的跨学科研究的关键知识和技能，将生物标志物和风险建模的见解转化为战略， 脓毒症的院前识别。我的目标是，这些实验将为未来NIH资助的院前败血症患者和系统水平干预试验奠定基础。我认为，这种对我职业发展的投资将有助于对新生脓毒症的潜在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高紧急程度，最资源密集型患者的急诊护理。

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.