Use of Time Series Biomarker and Clinical Data to Construct a Time Trajectory Host Response Map

使用时间序列生物标志物和临床数据构建时间轨迹宿主响应图

• 批准号: 10699456
• 负责人: Bobby Reddy
• 金额: $ 127.22万
• 依托单位: PRENOSIS, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10699456
• 关键词: Admission activity; Adrenal Cortex Hormones; Anti-Infective Agents; Antibiotics; Award; Biological; Biological Assay; Biological Markers; Classification; Clinical; Clinical Data; Clinical Trials; Computer software; Computerized Medical Record; Data; Data Set; Diagnostic; Dimensions; Evaluation; Grant; Grouping; Heterogeneity; Hospitals; Hour; Housing; Immune response; Infection; Intervention; Libraries; Link; Liquid substance; Machine Learning; Maps; Measurement; Measures; Methodology; Modeling; National Institute of General Medical Sciences; Outcome; Patients; Plasma; Principal Investigator; Probability; Sampling; Sepsis; Series; Serum; Surrogate Endpoint; Syndrome; Testing; Time; Vasoconstrictor Agents; Ventilator; Work; biobank; clinical decision support; commercialization; coronavirus disease; deep learning; demographics; design; diagnostic biomarker; drug candidate; drug development; follow-up; immune modulating agents; improved; insight; interest; longitudinal analysis; mortality; novel diagnostics; outcome prediction; patient population; product development; prognostic; prognostic value; programs; protein biomarkers; response; success; support tools; time use; tool; treatment effect

Principal Investigator/Program Director (Last, first, middle): Reddy, Jr., Bobby Project Summary: Sepsis is an incompletely understood clinical syndrome characterized by a dysregulated host response to infection. In partnership with 8 U.S. hospitals, Prenosis amassed one of the world’s largest datasets and biobanks that combines biomarker and clinical data for patients suspected of infection, housing over 70,000 plasma or serum samples from over 14,000 patients. We also curated a dataset of dense time-series data from each patient’s Electronic Medical Record (EMR), including demographics, vitals, lab results, interventions, outcomes, and many other parameters. To commercialize insights from these data, Prenosis built ImmunixTM, an FHR/HL7 compatible software platform for clinical deployment of diagnostics and clinical decision support tools. Under a previously awarded NIGMS grant (1R44GM139529), Prenosis built a testing pipeline to measure 40 critical protein biomarkers from biobanked samples. To date, we measured these biomarkers on only the initial sample per patient for 6,000 patients and combined these data with EMR clinical parameters to construct 8 endotypes of sepsis. The identification and classification of endotypes—groupings of patients with similar biologic and clinical features—is increasingly becoming recognized as a valuable methodologic approach to assessing patients with sepsis. To complete work for the existing grant, Prenosis will measure the baseline sample for additional patients to total about 10,000 patients to refine and validate the endotypes. In this project, Prenosis proposes to add a critical new dimension to the data by assaying and analyzing longitudinal, time-series biomarker data. We will leverage our pipeline to measure the 40 core biomarkers from 9,000 follow-up samples from ~3,400 patients that we have already collected and stored in the biobank. We will assess the additional value of longitudinal time-series biomarker measurements and clinical data. Initial feasibility data from over 1,000 measured samples demonstrates that longitudinal data provide additional powerful new biologic and prognostic insights. Analytics built upon these data have the potential to improve diagnostic and drug development products for sepsis and COVID. The overall hypothesis of this project is that longitudinal biomarkers will add a valuable biologic and prognostic dimension to endotypes for sepsis; and these longitudinal endotypes will better classify patients who may have a heterogeneous response to sepsis therapies.

首席研究员/计划主任（最后，第一，中间）：小雷迪，鲍比 项目摘要： 败血症是一种未完全了解的临床综合征，其特征是宿主对 感染。与8家美国医院合作，Prenosis积累了世界上最大的数据集和生物库之一 结合了涉嫌感染的患者的生物标志物和临床数据，住房超过70,000个等离子体或 来自14,000多名患者的血清样品。我们还从每个 患者的电子病历（EMR），包括人口统计，生命力，实验室结果，干预措施，结果， 以及许多其他参数。为了使这些数据的见解商业化，PRENOSIS构建了ImmunixTM，FHR/HL7 临床部署诊断和临床决策支持工具的兼容软件平台。 根据先前授予的NIGMS赠款（1R44GM139529），Prenosis建立了一条测试管道，以测量40 来自生物类样品的关键蛋白质生物标志物。迄今为止，我们仅根据初始测量了这些生物标志物 每位患者的样品对6,000名患者进行样本，并将这些数据与EMR临床参​​数合并为构建8 败血症的内型。内型的识别和分类 - 相似的患者组群 生物学和临床特征 - 越来越多地被认为是一种有价值的方法论方法 评估败血症患者。为了完成现有赠款的工作，PRENOSIS将测量基线 为更多患者采样，总计约10,000名患者，以完善和验证内型。 在这个项目中，通过分析和分析，培养的提议为数据增加了关键的新维度 纵向，时间序列生物标志物数据。我们将利用管道来测量40个核心生物标志物 我们已经收集并存储在生物库中的约3,400名患者的9,000例随访样本。我们 将评估纵向时间序列生物标志物测量和临床数据的额外价值。最初的 来自1,000多个测得的样本的可行性数据表明，纵向数据提供了其他 强大的新生物学和预后见解。基于这些数据的分析有可能改善 败血症和covid的诊断和药物开发产品。该项目的总体假设是 纵向生物标志物将为败血症的内型增添有价值的生物学和预后维度。和这些 纵向内型将更好地对可能对败血症疗法有异质反应的患者进行分类。