Identifying pre-sepsis opportunities for early, targeted intervention

确定败血症前早期、有针对性的干预的机会

• 批准号: 10201654
• 负责人: Vincent Liu
• 金额: $ 38.8万
• 依托单位: KAISER FOUNDATION RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10201654
• 关键词: Accident and Emergency department; Caring; Cessation of life; Characteristics; Computerized Medical Record; Data; Early treatment; Evaluation; Future; Goals; Health system; Hospital Departments; Hospitals; Infection; Informatics; Intervention; Laboratories; Methods; Organ failure; Patient Care; Patients; Pre-hospital setting; Process; Public Health; Randomized Clinical Trials; Risk; Sepsis; Symptoms; adverse outcome; authority; design; global health; high risk; improved; improved outcome; innovation; mortality; novel; predictive modeling; prevent; programs; septic patients; treatment guidelines

PROJECT SUMMARY Sepsis kills an estimated 6 million people worldwide every year. Sepsis also contributes to as many as 50% of US hospital deaths. Early treatment is the only universally recognized modifiable factor for improving sepsis mortality. Thus, current treatment guidelines focus heavily on early sepsis care immediately following hospital presentation. However, key national and global health authorities also emphasize the need to identify even earlier pre-hospital opportunities to predict, recognize, or treat sepsis. Little is known about the presentation, pace, and profile of infection in pre-sepsis patients. Understanding these characteristics could enable novel pre-hospital approaches designed to mitigate, or even prevent, sepsis. Using an innovative translational informatics approach, this project will identify and characterize pre-sepsis opportunities for early, targeted intervention. To achieve this goal, my laboratory leverages comprehensive electronic medical record data, advanced informatics methods, and the evaluation of novel care programs. Through this proposal, we will develop and validate prediction models that identify patients in common pre-hospital settings at the highest risk of impending sepsis, organ failure, and mortality. In the process, we will characterize the sepsis-related symptom profiles that portend the highest risk of adverse outcomes. Findings will have broad and immediate implications for patients, clinicians, and health systems. These results will also inform the design of sepsis public health programs and future randomized clinical trials aimed at improving outcomes for this devastating condition.

项目摘要 脓毒症每年在全世界造成约600万人死亡。脓毒症也导致多达50%的 美国医院死亡早期治疗是唯一公认的改善脓毒症的可变因素 mortality.因此，目前的治疗指南主要集中在医院后立即进行的早期脓毒症护理 演示文稿.然而，关键的国家和全球卫生当局也强调， 更早的院前机会来预测，识别或治疗败血症。对这次演讲所知甚少， 脓毒症前患者的感染速度和特征。了解这些特征可以使小说 旨在减轻甚至预防败血症的院前方法。使用创新的翻译 信息学方法，该项目将确定和表征脓毒症前的机会， 干预为了实现这一目标，我的实验室利用了全面的电子病历数据， 先进的信息学方法，以及新的护理方案的评估。通过这项提案，我们将 开发和验证预测模型，以识别处于最高风险的常见院前环境中的患者 脓毒症器官衰竭和死亡的危险在这个过程中，我们将描述脓毒症相关的 预示不良后果风险最高的症状特征。调查结果将有广泛和直接的 对患者、临床医生和卫生系统的影响。这些结果也将告知脓毒症的设计 公共卫生计划和未来的随机临床试验，旨在改善这种破坏性的结果， 条件