Sepsis Early Prediction and Subphenotype Illumination Study (SEPSIS)

脓毒症早期预测和亚表型启发研究 (SEPSIS)

基本信息

批准号：
9904745
负责人：
Matthew Michael Churpek
金额：
$ 33.19万
依托单位：
UNIVERSITY OF WISCONSIN-MADISON
依托单位国家：
美国
项目类别：
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/9904745
关键词：
Address Advisory Committees Algorithm Design Algorithms Anti-Inflammatory Agents Area Caregivers Caring Centers for Disease Control and Prevention (U.S.)Cessation of life Characteristics Clinical Cluster Analysis Complex Critical Care Critical Illness Data Data Set Detection Deterioration Diagnosis Early Diagnosis Early Intervention Early identification Early treatment Electronic Health Record Functional disorder Future Goals Grant Health Care Costs Heart Arrest Home environment Hospitals Immune response Impaired cognition Infection Infectious Agent Inpatients Intensive Care Units Intervention Intervention Trial Intuition Knowledge Label Lead Life Lighting Logistic Regressions Machine Learning Manuals Medicine Methods Modeling Modernization Organ Outcome Patient risk Patients Pharmaceutical Preparations Probability Publishing Research Resources Risk Risk Factors Risk stratification Savings Sepsis Severity of illness Statistical Models Subgroup Survivors Syndrome Techniques Time Validation Variant Work base clinical phenotype cohort cost electronic data high risk improved improved outcome machine learning algorithm machine learning method mortality novel patient subsets personalized care physically handicapped preventable death random forest response septic patients side effect tool ward

项目摘要

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.

项目概要脓毒症被定义为响应感染而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 亿美元的医疗费用由该疾病造成。超过美国每年有 750,000 名患者患上败血症，幸存者遭受长期认知障碍和身体残疾。从历史上看，脓毒症研究主要集中在已经病情危重的患者。然而，高达 50% 的脓毒症患者在住院病房接受护理，而只有 10% 的脓毒症患者脓毒症最初是在重症监护病房 (ICU) 中诊断出来的。因为早期干预可以改善结果脓毒症，优化 ICU 外脓毒症的检测和治疗非常重要。目前的脓毒症范式存在几个问题。第一个问题是早期识别感染依赖于临床医生的直觉，而护理人员对于哪些患者被感染往往存在分歧。这导致一些患者的治疗延迟和死亡率增加以及不必要的治疗和不良反应其他人的药物副作用。第二个问题是缺乏准确的风险分层工具确诊后，重症监护病房外的感染患者。一些感染患者在院外接受治疗重症监护室，后来出院回家，而其他人则出现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床边带来额外的重症监护资源最需要它们的高危患者。当前脓毒症范式的第三个问题是，它经常被视为一种一刀切的综合症。然而，脓毒症患者具有广泛的临床表现。由于患者危险因素、传染性因素之间复杂的相互作用，导致临床表现和结果有机体和宿主的免疫反应。这些数据表明，及时、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。识别重要的感染患者的亚表型对于在床边提供更加个性化的护理至关重要。该项目的目的是使用电子健康记录和统计模型中的数据识别高危感染患者和该综合征的重要新亚表型的技术。在目标 1 中，我们将开发一种新工具，利用现代机器学习识别 ICU 外的感染患者技术。在目标 2 中，我们将开发一种工具，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措施根据临床表型而有所不同。我们的项目将为临床医生提供拥有强大的新工具来识别高危感染患者及其重要的新亚表型常见且致命的综合症。这项工作将有助于为化粪池患者提供早期救生护理患者并导致未来旨在减少可预防死亡的干预试验。