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.

项目概要/摘要 败血症是一种由严重感染引起的危及生命的器官衰竭综合征，至少导致 美国每年有 170 万人因败血症入院，死亡率为 15-20%，每年导致 1100 万人死亡。 在世界各地，及时、适当的抗生素和静脉输液治疗可以改善败血症的结果，但是 许多脓毒症患者在实践中没有接受最佳治疗。 然而，在某些治疗领域，医生是造成这一问题的重要因素。 反映了这样一个事实：在这两种情况下，最佳脓毒症治疗策略的机制尚不清楚。 脓毒症护理中医生之间潜在的差异很大程度上未知。 先进的流行病学、统计、机器学习和定性研究方法，以进行大规模、精细的研究 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 - 用于 脓毒症患者的重点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结果。 还将利用观察到的变化来为最佳脓毒症管理策略的因果推断提供信息 在当前证据不足的情况下，这项研究将产生经过验证的、可扩展的和 支持高质量“大数据”脓毒症研究的通用工具。拟议的研究很好。 与 NIGMS 脓毒症研究重点相匹配，将有助于卫生系统、研究人员和顾客设计 并提供脓毒症护理，提供证据来构建根据患者表型定制的决策支持工具， 临床医生的决策风格，并为脓毒症护理策略和实施的未来试验奠定基础 方法。