Measuring and Learning from Care Variation in Sepsis

脓毒症护理变化的测量和学习

• 批准号: 10712986
• 负责人: Ithan Daniel Peltan
• 金额: $ 38.38万
• 依托单位: IHC HEALTH SERVICES, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10712986
• 关键词: Accident and Emergency department; Affect; Antibiotics; Area; Big Data; Caring; Clinical Data; Data Set; Decision Making; Epidemiology; Future; Health system; Hospitalization; IV Fluid; Infection; Investigation; Learning; Life; Liquid substance; Measures; Methods; Morbidity - disease rate; National Institute of General Medical Sciences; Organ failure; Outcome; Patients; Persons; Phenotype; Physicians; Process; Qualitative Research; Research; Research Methodology; Research Personnel; Research Priority; Resuscitation; Sepsis; Syndrome; Triage; Variant; design; improved; individual patient; mortality; optimal treatments; programs; statistical and machine learning; support tools; tool; treatment and outcome; treatment strategy

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.

项目总结/摘要 脓毒症是一种由严重感染引起的危及生命的器官衰竭综合征， 1.7每年有100万美国人入院。败血症的死亡率为15- 20%，每年造成1100万人死亡 世界各地及时、适当的抗生素和静脉输液治疗可以改善脓毒症的预后， 许多脓毒症患者没有接受最佳治疗。在实践中存在不必要的差异， 医生是造成这一问题的重要因素。然而，在某些治疗领域， 反映了最佳脓毒症治疗策略尚不明确的事实。在这两种情况下， 脓毒症治疗中潜在的医师间差异在很大程度上是未知的。该研究计划将适用于 先进的流行病学、统计学、机器学习和定性研究方法， 临床数据集，以确定医生如何做出关键管理决策-包括决策 与抗生素启动、静脉液体复苏和急诊室处置相关-对于 脓毒症患者一个关键的重点将是了解不同风格的决策如何相互作用， 这种异质性综合征的不同表型影响患者的治疗和结果。调查 还将利用观察到的变化，为最佳脓毒症管理策略的因果推理提供信息 目前的证据不足。在这个过程中，这项研究将产生有效的，可扩展的， 可推广的工具，以支持高质量的“大数据”脓毒症研究。拟议的研究，这是很好的 与NIGMS脓毒症研究优先事项相匹配，将有助于卫生系统，研究人员和临床医生设计 并提供败血症护理，提供证据以建立针对患者表型定制的决策支持工具， 临床医生的决策风格，并奠定了基础，为未来的试验败血症护理策略和实施 方法.