Measuring and Learning from Care Variation in Sepsis

项目摘要/摘要 脓毒症是一种威胁生命的器官衰竭综合征，由严重感染引起，至少可导致 美国每年有170万人入院。败血症的死亡率为15%-20%，每年导致1100万人死亡 环游世界。及时、适当地使用抗生素和静脉输液可以改善脓毒症的预后，但 许多脓毒症患者没有得到最佳的治疗。在实践中的不正当更改 医生是造成这一问题的重要因素。然而，在某些治疗领域，做法存在差异 反映出脓毒症的最佳治疗策略尚不明确。在这两种情况下，机制 脓毒症治疗中潜在的医生间差异在很大程度上是未知的。这项研究计划将适用于 先进的流行病学、统计学、机器学习和定性研究方法 用于确定医生如何做出关键管理决策的临床数据集--包括决策 与抗生素启动、静脉输液复苏和急诊科处置相关-用于 脓毒症患者。重点将是了解不同风格的决策如何相互作用 这种异质综合征的不同表型会影响患者的治疗和预后。调查 还将利用观察到的变化来提供关于最佳脓毒症管理策略的因果推断 在目前证据不足的情况下。在此过程中，此研究将产生经过验证、可扩展和 可推广的工具，支持高质量的“大数据”脓毒症研究。拟议的研究，这是很好的 与NIGMS脓毒症研究重点相匹配，将有助于卫生系统、研究人员和临床医生设计 并提供脓毒症护理，提供证据以构建针对患者表型和 临床医生的决策方式，并为未来脓毒症护理策略和实施的试验奠定基础 方法：研究方法。

PROJECT SUMMARY/ABSTRACT Sepsis is a syndrome of life-threatening organ failure resulting from serious infection that contributes to at least 1.7 million U.S. hospital admissions annually. With mortality of 15-20%, sepsis kills 11 million people each year around the world. Prompt, appropriate therapy with antibiotics and IV fluids can improve sepsis outcomes, but many patients with sepsis do not receive optimal therapy. Unwarranted variation in practice between physicians is an important contributor to this problem. In some areas of treatment, however, practice variation reflects the fact that the optimal sepsis treatment strategy is not clear. In both situations, the mechanisms underlying between-physician variation in sepsis care are largely unknown. This research program will apply advanced epidemiologic, statistical, machine learning, and qualitative research methods to large, granular clinical datasets to determine how physicians make critical management decisions — including decisions related to antibiotic initiation, intravenous fluid resuscitation, and emergency department disposition — for patients with sepsis. A key focus will be understanding how different styles of decision making interact with different phenotypes of this heterogeneous syndrome to affect patient treatment and outcomes. Investigations will also harness observed variation to inform causal inference about optimal sepsis management strategies where current evidence is inadequate. In the process, this research will produce validated, scalable, and generalizable tools to support high-quality “big data” sepsis research. The proposed research, which is well matched to the NIGMS sepsis research priorities, will help health systems, researchers, and clinicians design and deliver sepsis care, provide evidence to build decision support tools customized to patient phenotypes and clinician decision styles, and lay the groundwork for future trials of sepsis care strategies and implementation methods.