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.

主要研究者/项目负责人（最后，第一，中间）：Reddy，Jr.，鲍比 项目概要： 脓毒症是一种不完全了解的临床综合征，其特征是宿主对 感染Prenosis与8家美国医院合作，积累了世界上最大的数据集和生物库之一 它结合了疑似感染患者的生物标志物和临床数据，储存了超过70，000份血浆或 超过14,000名患者的血清样本我们还策划了一个密集的时间序列数据集， 患者的电子病历（EMR），包括人口统计学、生命体征、实验室结果、干预措施、结局， 以及许多其他参数。为了将这些数据的见解商业化，Prenosis构建了ImproxTM，一个FHR/HL 7 用于诊断和临床决策支持工具的临床部署的兼容软件平台。 根据先前授予的NIGMS赠款（1 R44 GM 139529），Prenosis建立了一个测试管道，以测量40 关键的蛋白质生物标记物。到目前为止，我们只在最初的 6,000例患者的每例患者样本，并将这些数据与EMR临床参数结合，构建8 败血症的内源性内型的识别和分类-相似的患者分组 生物学和临床特征-越来越被认为是一种有价值的方法学方法， 评估脓毒症患者为了完成现有赠款的工作，Prenosis将测量基线 样本用于额外的患者，总计约10，000例患者，以完善和验证内型。 在这个项目中，Prenosis建议通过测定和分析来为数据添加一个关键的新维度 纵向、时间序列生物标志物数据。我们将利用我们的管道来测量40个核心生物标志物， 我们已经收集了来自约3,400名患者的9,000份随访样本，并储存在生物库中。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。初始 来自1,000多个测量样本的可行性数据表明，纵向数据提供了额外的 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 用于败血症和COVID的诊断和药物开发产品。这个项目的总体假设是， 纵向生物标志物将为败血症的内源性增加有价值的生物学和预后维度;并且这些 纵向内型将更好地对可能对脓毒症治疗具有异质性反应的患者进行分类。