Sepsis Early Prediction and Subphenotype Illumination Study (SEPSIS)

项目总结 败血症被定义为对感染作出反应而危及生命的器官功能障碍，是一种毁灭性的疾病， 在美国，每年高达一半的医院死亡和超过240亿美元的医疗费用都是由这种疾病造成的。完毕 在美国，每年有75万名患者患上脓毒症，幸存者患有长期的认知障碍和 身体残疾。从历史上看，脓毒症的研究一直集中在已经危重的患者身上。然而， 高达50%的脓毒症患者在住院病房接受护理，而只有10%的患者在 败血症最初是在重症监护病房(ICU)诊断的。因为早期干预改善了治疗结果 对于败血症来说，优化ICU外脓毒症的检测和治疗是很重要的。 目前的脓毒症模式存在几个问题。第一个问题是及早识别出 感染依赖于临床医生的直觉，护理人员经常在哪些患者被感染的问题上存在分歧。这 导致一些患者延迟治疗和增加死亡率，以及不必要的治疗和不良反应 其他人的用药副作用。第二个问题是缺乏准确的风险分层工具。 在ICU外的感染患者被确认后。一些感染患者在医院外接受治疗。 其他人出现危及生命的并发症，在医院死亡。 对感染患者进行准确的风险分层将为床边的 最需要它们的高危患者。目前脓毒症模式的第三个问题是，它经常 作为一种一刀切的综合症来治疗。然而，败血症患者的临床范围很广。 由于患者风险因素之间的复杂相互作用，感染性疾病 生物体，以及宿主的免疫反应。这些数据表明，影响及时和更具侵略性 根据患者的临床表型，对结果的干预可能会有所不同。确定重要信息 感染患者的亚型对于在床边提供更个性化的护理至关重要。 本项目的目的是使用来自电子健康记录的数据和统计建模 识别高危感染患者和这种综合征的重要新亚型的技术。在目标1中， 我们将开发一种新的工具，使用现代机器学习来识别ICU外的感染患者 技巧。在目标2中，我们将开发一种使用机器对ICU外的感染患者进行风险分层的工具 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 使用强大的新工具来识别高危感染患者和重要的新亚型 常见而致命的综合症。这项工作将有助于为脓毒症患者的床边提供早期、救命的护理。 并导致未来旨在减少可预防死亡的干预试验。

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.