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: 10056599
• 负责人: Matthew Michael Churpek
• 金额: $ 36.54万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10056599
• 关键词: Sepsis; Early; Prediction; Subphenotype; Illumination

PROJECT SUMMARY Sepsis, defined as life-threatening organ dysfunction in response to infection, is a devastating condition that contributes to up to half of hospital deaths and over $24 billion in healthcare costs in the U.S. annually. Over 750,000 patients develop sepsis in the U.S. each year, and survivors suffer long-term cognitive impairment and physical disability. Historically, sepsis research has focused on patients who are already critically ill. However, up to 50% of patients with sepsis receive their care on the inpatient wards, and only 10% of patients with sepsis are initially diagnosed in the intensive care unit (ICU). Because early intervention improves outcomes in sepsis, it is important to optimize the detection and treatment of sepsis outside the ICU. The current sepsis paradigm has several problems. The first problem is that early identification of infection relies on clinician intuition, and caregivers often disagree regarding which patients are infected. This leads to delays in therapy and increased mortality in some patients and unnecessary therapies and adverse medication side effects in others. A second problem is that there is a lack of accurate tools to risk stratify infected patients outside the ICU after they are identified. Some patients with infection are treated outside the ICU and are later discharged home, while others develop life-threatening complications and die in the hospital. Accurate risk stratification of infected patients would bring additional critical care resources to the bedside of the high-risk patients that need them most. A third problem with the current sepsis paradigm is that it is often treated as a one-size-fits-all syndrome. However, patients with sepsis have a wide range of clinical presentations and outcomes due to the complex interactions between patient risk factors, the infectious organism, and the host immune response. These data suggest that the impact of timely and more aggressive interventions on outcomes may differ based on a patient's clinical phenotype. Identifying important subphenotypes of infected patients is critical to delivering more personalized care at the bedside. The purpose of this project is to use data from the electronic health record and statistical modeling techniques to identify high-risk infected patients and important new subphenotypes of this syndrome. In Aim 1, we will develop a novel tool for identifying infected patients outside the ICU using modern machine learning techniques. In Aim 2, we will develop a tool for risk stratifying infected patients outside the ICU using machine learning methods. Finally, in Aim 3 we will use cluster analysis techniques to determine whether the benefit of early and more aggressive interventions varies based on clinical phenotype. Our project will provide clinicians with powerful new tools to identify high-risk infected patients and important new subphenotypes of this common and deadly syndrome. This work will help to deliver early, life-saving care to the bedside of septic patients and lead to future interventional trials aimed at decreasing preventable death.

项目摘要 脓毒症，定义为对感染作出反应的危及生命的器官功能障碍，是一种毁灭性的疾病， 在美国，每年有多达一半的医院死亡和超过240亿美元的医疗费用。超过 在美国，每年有750，000名患者发生败血症，幸存者遭受长期的认知障碍， 身体残疾。从历史上看，脓毒症的研究主要集中在已经病情危重的患者身上。然而，在这方面， 高达50%的脓毒症患者在住院病房接受护理，只有10%的脓毒症患者 败血症最初在重症监护室（ICU）中诊断。因为早期干预可以改善 因此，重要的是优化ICU外脓毒症的检测和治疗。 目前的脓毒症范例有几个问题。第一个问题是， 感染依赖于临床医生的直觉，并且护理人员经常对哪些患者被感染存在分歧。这 导致治疗延迟和一些患者死亡率增加，以及不必要的治疗和不良反应。 其他药物的副作用。第二个问题是缺乏准确的风险分层工具 被感染的病人在被确认后在ICU外。一些感染的病人在医院外接受治疗。 重症监护室，后来出院回家，而其他发展危及生命的并发症，并在医院死亡。 对感染患者进行准确的风险分层将为患者的床旁提供额外的重症监护资源， 最需要他们的高危病人当前脓毒症范例的第三个问题是， 被当作一种通用的综合症。然而，脓毒症患者具有广泛的临床表现， 由于患者风险因素、传染性疾病和其他疾病之间的复杂相互作用， 生物体和宿主免疫反应。这些数据表明，及时和更积极的影响， 根据患者的临床表型，对结果的干预可能有所不同。确定主要 感染患者的亚表型对于在床边提供更个性化的护理至关重要。 这个项目的目的是使用来自电子健康记录和统计建模的数据 技术，以确定高风险的感染患者和重要的新的亚表型的这种综合征。在目标1中， 我们将开发一种新的工具，利用现代机器学习来识别ICU外的感染患者。 技术.在目标2中，我们将开发一种工具，用于使用机器对ICU外的感染患者进行风险分层 学习方法最后，在目标3中，我们将使用聚类分析技术来确定 早期和更积极的干预根据临床表型而变化。我们的项目将为临床医生提供 利用强大的新工具来识别高风险感染患者及其重要的新亚表型， 常见而致命的综合症这项工作将有助于提供早期，挽救生命的护理，以床边的脓毒症 患者，并导致未来旨在减少可预防死亡的干预性试验。