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

利用统一生物标志物临床数据集开发的脓毒症内型来开发脓毒症产品的协作平台

• 批准号: 10252921
• 负责人: Bobby Reddy
• 金额: $ 98.95万
• 依托单位: PRENOSIS, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-05 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10252921
• 关键词: Accident and Emergency department; Algorithms; Antibiotics; Award; Back; Biological Assay; Biological Markers; Blood specimen; Businesses; Cause of Death; Charge; Clinical; Clinical Data; Collaborations; Complex; Computer software; Computerized Medical Record; Contracts; Data; Data Security; Data Set; Data Storage and Retrieval; Derivation procedure; Diagnosis; Drops; Elements; Environment; Excision; Fees; Functional disorder; Future; Generations; Goals; Government Agencies; Health; Healthcare Systems; Hospitals; Hour; Immune response; Infection; Measurement; Measures; Organ; Outcome; Patients; Pharmaceutical Preparations; Physicians; Principal Investigator; Proteins; Reporting; Research Personnel; Resources; Risk; Sample Size; Sampling; Secure; Security; Security Measures; Sepsis; Series; Shock; Site; Survival Rate; Syndrome; Technology; Time; Training; Triage; United States; University Hospitals; Validation; Work; algorithm training; base; biobank; clinical decision support; cohort; commercialization; cost; data de-identification; data hosting; encryption; health information technology; machine learning algorithm; payment; prevent; programs; sample collection; screening; specific biomarkers; success; support tools; tool; unsupervised learning

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).

首席研究员/计划主任（最后，第一，中间）：小雷迪，鲍比 项目摘要： 败血症是一种知识熟悉的临床综合征，其特征是免疫系统的失调 对感染的反应。这是死亡的主要原因，是美国医院治疗的最昂贵病情， 2011年，烧毁了203亿美元的燃烧，全国医疗保健系统的总成本的5.2％。专业之一 临床医生面临的挑战是识别和识别败血症患者和即将发生的器官功能障碍。 败血症的临床表现高度可变，感染和器官功能障碍的迹象可能是 微妙，可能模仿其他条件。败血症也很重要。每1小时的抗生素延迟 紧急部门（ED）分类或器官功能障碍或冲击的发作与增加3–7％有关 结果很差。这些条件创造了一个必须诊断的环境 在短时间内的复杂，异构条件，信息有限。目前有艰巨的需求 对于可以快速评估患者是否有败血症风险的工具。 Prenosis是一家致力于阐明宿主对感染反应失调的复杂性的公司。在 与4家医院的合作伙伴关系，我们建立了世界上最大，增长最快的数据集和数据集 将时间序列生物标志物数据与临床数据相结合的生物库，用于涉嫌在医院感染的患者 环境。该数据集和生物库目前有> 2,000名患者，> 70,000个专有生物标志物 测量>> 1,200,000电子病历（EMR）参数，> 25,000个样品（全部） 随着参与的全日制EMR数据）。我们目前已经签订了6家医院的合同 合作伙伴关系，如果我们的管道满足，则有可能每年扩大> 65,000名患者 容量。 在这个拟议的项目中，Prenosis将通过增长我们的当前 专有数据集和生物库目前的大约2,000名患者到超过10,000名患者（AIM 1）。使用当前的2,000名患者数据集，我们证明了败血症的初始承诺内型 对于各种关键的临床问题可能很有用。当我们将数据集增加到10,000名患者时，我们将使用 对大约一半的患者（5,000）培训的无监督的机器学习算法，以明确证明 这些内型的鲁棒性和实用性。另一半患者（其他5,000例）将用作 由ML算法确定的内型的多站点验证队列（AIM 2）。我们还将最终确定 NoSistm产品的实际软件平台（AIM 3），包括数据安全性，限制访问权限 合作者培训和共同开发产品，以及具有潜在的业务合作伙伴关系的模板 合作者（最初关注热门公司和制药公司）。