Collaborative Platform for Developing Sepsis Products by Leveraging Sepsis Endotypes Developed Using a Unified Biomarker-Clinical Dataset

首席调查员/项目总监(最后、第一、中间)：小雷迪，鲍比 项目总结： 脓毒症是一种鲜为人知的临床综合征，其特征是免疫系统调节失调。 对感染的反应。它是导致死亡的主要原因，也是美国医院治疗费用最高的疾病， 2011年造成203亿美元的负担，占全国医疗体系总成本的5.2%。其中一个主要的 临床医生面临的挑战是识别和识别脓毒症和即将发生的器官功能障碍的患者。 脓毒症的临床表现多种多样，感染和器官功能障碍的体征可以 很微妙，可能会模仿其他情况。脓毒症也是高度时间紧迫的。抗生素在服用后每延迟1小时 急诊科(ED)分诊或器官功能障碍或休克的发作与3%-7%的增长有关 在一个糟糕的结果的可能性。这些情况创造了一个医生必须诊断的环境 在信息有限的短时间内，一种复杂的、不同的情况。目前有一种迫切的需求 寻找一种可以快速评估患者是否有脓毒症风险的工具。 Prensis是一家专注于阐明宿主对感染反应失调的复杂性的公司。在……里面 我们与4家医院合作，建立了世界上最大、增长最快的数据集&数据丰富 将时间序列生物标记物数据与医院内疑似感染患者的临床数据相结合的生物库 环境。该数据集和生物库目前有2,000名患者，70,000名专有生物标记物 测量、1,200,000个电子病历(EMR)参数和25,000个样本存储库(所有 附有完整的时间序列电子病历数据)。我们目前已经签署了6家医院的合同 合作伙伴关系，如果我们的渠道满了，每年有可能扩大6.5万名患者的数据集 容量。 在这个拟议的项目中，Prensis将最终确定NOSISTM平台的第一个版本，通过发展我们目前的 专有数据集&Biobank从目前约2,000名患者的规模增加到10,000多名患者(目标 1)。使用目前2,000名患者的数据集，我们已经展示了最初有希望的脓毒症内型 可能对各种严重的临床问题有用。随着我们将数据集扩展到10,000名患者，我们将使用 对大约一半(5000名)的患者进行了无监督机器学习算法的培训，以最终证明 这些内型的健壮性和实用性。另一半的患者(另外5000人)将被用作 ML算法确定的内型的多点验证队列(目标2)。我们还将最终敲定 NOSISTM产品(AIM 3)的实际软件平台，包括数据安全，访问受限于 协作者培训和联合开发产品，并为潜在的业务合作伙伴关系提供模板 合作者(最初重点关注热门公司和制药公司)。

Principal Investigator/Program Director (Last, first, middle): Reddy, Jr., Bobby Project Summary: Sepsis is a poorly understood clinical syndrome characterized by a dysregulation of the immune system’s response to infection. It is the leading cause of death and is the most expensive condition treated in U.S. hospitals, exerting a $20.3 billion burden in 2011, 5.2% of total costs to the healthcare system nationwide. One of the major challenges facing clinicians is to identify and recognize patients with sepsis and impending organ dysfunction. The clinical manifestations of sepsis are highly variable and the signs of infection and organ dysfunction can be subtle and may mimic other conditions. Sepsis is also highly time critical. Every 1-hour delay in antibiotics after emergency department (ED) triage or the onset of organ dysfunction or shock is associated with a 3–7% increase in the odds of a poor outcome. These conditions have created an environment where physicians have to diagnose a complex, heterogeneous condition in a short timeframe with limited information. There is currently a dire need for a tool that can quickly assess if a patient is at risk for sepsis. Prenosis is a company focused on elucidating the complexity of dysregulated host response to infection. In partnership with 4 hospitals, we have built the world’s largest and most rapidly growing dataset & data-rich biobank that combine time series biomarker data with clinical data for patients suspected of infection in hospital environments. This dataset & biobank currently have >2,000 patients, >70,000 proprietary biomarker measurements, >1,200,000 Electronic Medical Record (EMR) parameters, and >25,000 samples banked (all with accompanying full time series EMR data). We currently have executed contracts for 6 total hospital partnerships, with the potential to expand the dataset by >65,000 patients per year if our pipeline were at full capacity. In this proposed project, Prenosis will finalize the first version of the NOSISTM platform by growing our current proprietary dataset & biobank from its current size of about 2,000 patients to over 10,000 total patients (Aim 1). Using the current 2,000 patient dataset, we have demonstrated initial promising endotypes of sepsis that could be useful for a variety of critical clinical problems. As we grow the dataset to 10,000 patients, we will use unsupervised machine learning algorithms trained on roughly half of the patients (5,000) to definitively prove the robustness and usefulness of these endotypes. The other half of the patients (other 5,000) will be used as a multi-site validation cohort for the endotypes determined by the ML algorithms (Aim 2). We will also finalize the actual software platform for the NOSISTM product (Aim 3), including data security, restricted access by collaborators to train and jointly develop products, and templates for business partnerships with potential collaborators (with an initial focus on HIT companies and pharma companies).