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; 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.

项目摘要/摘要 败血症是威胁生命的器官衰竭综合征，导致严重感染，至少导致 每年170万美国医院入院。败血症的死亡率为15-20％，每年杀死1100万人 世界各地。及时，适当的抗生素和静脉液体疗法可以改善败血症的结果，但 许多败血症患者无法接受最佳治疗。实践中无理的变化 医师是解决这个问题的重要贡献者。但是，在某些治疗领域，练习变化 反映出最佳败血症治疗策略尚不清楚的事实。在这两种情况下，机制 脓毒症护理中的基础介质之间的基本变异在很大程度上是未知的。该研究计划将适用 高级流行病学，统计，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 与抗生素起始，静脉输液复苏和急诊科处置有关 - 用于 败血症患者。一个重点将是了解不同的决策方式如何与 这种异质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，以告知因果推断有关最佳败血症管理策略的推论 当前证据不足。在此过程中，这项研究将产生经过验证的，可扩展的，并且 支持高质量“大数据”败血症研究的可概括工具。拟议的研究很好 与NIGMS败血症研究重点相匹配，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，提供证据以建立针对患者表型定制的决策支持工具，并 临床决策方式，并为败血症护理策略和实施的未来试验奠定基础 方法。