Sepsis Early Prediction and Subphenotype Illumination Study (SEPSIS)

脓毒症早期预测和亚表型启发研究 (SEPSIS)

• 批准号: 10405298
• 负责人: Matthew Michael Churpek
• 金额: $ 38.88万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10405298
• 关键词: Address; Age; Algorithms; Award; Biological Markers; Biology; Caring; Cessation of life; Characteristics; Clinical; Clinical Trials; Collection; Complex; Critical Illness; Data; Department of Defense; Early identification; Electronic Health Record; Functional disorder; Funding; Future; Goals; Health; Health Care Costs; Hospitals; Immune response; Impaired cognition; Infection; Infectious Agent; International; Knowledge; Life; Lighting; Machine Learning; Mission; Modeling; Morbidity - disease rate; National Institute of General Medical Sciences; Natural Language Processing; Organ; Patient Care; Patient-Focused Outcomes; Patients; Peer Review; Population; Public Health; Publications; Publishing; Research; Secure; Sepsis; Societies; Structure; Study Section; Survivors; Syndrome; Time; United States; United States National Institutes of Health; Vision; Work; cost; deep learning; disability; high risk; improved; innovation; machine learning method; member; mortality; neglect; novel; patient stratification; personalized care; personalized medicine; physically handicapped; preventable death; programs; risk stratification; success; tool; treatment strategy; ward

PROJECT ABSTRACT Sepsis, a life-threatening organ dysfunction syndrome due to infection, is common in hospitalized patients and leads to significant morbidity, mortality, and costs. Over 1.7 million patients develop sepsis in the United States each year, a number that will increase as the population ages. Patients with sepsis contribute to over $24 billion in healthcare costs yearly, and a recent study found that sepsis contributed to up to half of hospital deaths. Furthermore, survivors of sepsis suffer long-term cognitive impairment and physical disability. Therefore, improving the care of patients with sepsis would be enormously beneficial to society. However, there are several critical gaps in the field that need to be addressed: 1) delays in identifying infected patients are common and associated with increased mortality; 2) errors in risk stratification of patients with impending critical illness and sepsis are common and deadly; 3) current treatment strategies for infected patients utilize a one-size-fits-all approach, which neglects the wide range of clinical presentations and underlying biology due to the complex interactions between patient characteristics, the infectious organism, and the host immune response. The overall vision of the PI’s research program is to address these knowledge gaps by utilizing detailed multicenter electronic health record (EHR), clinical trial, and biomarker data combined with machine learning approaches to improve the identification, risk stratification, and discover important subphenotypes of sepsis to decrease preventable death from infection. Over the past five years, the PI has successfully secured independent funding through an NIGMS R01 and Department of Defense award. The PI has published over 80 peer-reviewed publications during this time, is an active member on several national and international committees, has participated in several NIH study sections, and has 40 mentees, including six with NIH K-level awards. Importantly, the PI has also developed and implemented a machine learning risk stratification tool, called eCART, in over 20 hospitals, which has decreased mortality in high-risk ward patients. The goal of the next five years is to build upon these successes and address key gaps in the field through three future directions: 1) using natural language processing and deep learning to improve the identification and risk stratification of infected patients, 2) identifying important subphenotypes using research biomarkers, and 3) using machine learning to develop personalized treatment algorithms. These projects are innovative because they will utilize advanced machine learning methods in a large, multicenter collection of structured and unstructured EHR and biomarker data for developing novel tools in patients with sepsis. In the future, these models will be implemented for earlier identification, accurate risk stratification, and to deliver personalized care at the bedside. This has the potential to revolutionize the care of one of the most common and deadly conditions in hospitalized patients.

项目摘要 脓毒症是一种因感染而危及生命的器官功能障碍综合征，在住院患者中很常见 在美国，超过 170 万名患者罹患脓毒症，导致严重的发病率、死亡率和费用。 每年，随着人口老龄化，脓毒症患者的贡献将超过 240 亿美元。 每年的医疗费用，最近的一项研究发现败血症导致了多达一半的医院死亡。 此外，脓毒症幸存者会遭受长期的认知障碍和身体残疾。 改善脓毒症患者的护理将对社会产生巨大的好处。 该领域需要解决的关键差距：1）延迟识别感染患者的情况很常见，并且 与死亡率增加有关；2）对即将发生危重疾病的患者的风险分层错误； 败血症是常见且致命的；3) 目前针对感染患者的治疗策略采用的是一刀切的方法。 方法，由于复杂的临床表现和基础生物学而忽略了广泛的临床表现和基础生物学 患者特征、传染性微生物和宿主免疫反应之间的相互作用。 PI 研究计划的总体愿景是利用详细的知识来解决这些知识差距 多中心电子健康记录 (EHR)、临床试验和生物标志物数据与机器学习相结合 改进脓毒症识别、风险分层和发现重要亚表型的方法 减少因感染导致的可预防死亡 在过去五年中，PI 已成功确保独立。 通过 NIGMS R01 和国防部奖获得资助。PI 已发表 80 多篇经过同行评审的文章。 在此期间发表出版物，是多个国家和国际委员会的积极成员， 参加过多个 NIH 研究部门，有 40 名学员，其中 6 名获得 NIH K 级奖项。 重要的是，PI 还开发并实施了一种机器学习风险分层工具，称为 eCART， 在20多家医院，降低了高危病房患者的死亡率。未来五年的目标是。 在这些成功的基础上，通过三个未来方向解决该领域的关键差距：1）利用自然 语言处理和深度学习，以改善感染患者的识别和风险分层，2) 使用研究生物标志物识别重要的亚表型，以及 3) 使用机器学习来开发 这些项目具有创新性，因为它们将利用先进的机器。 在结构化和非结构化 EHR 和生物标记数据的大型多中心集合中学习方法 开发脓毒症患者的新工具将来，这些模型将更早实施。 识别、准确的风险分层以及在床边提供个性化护理，这具有潜力。 彻底改变住院患者最常见和最致命疾病之一的护理。