Combined Biomarker and EMR Data for Heterogeneous Treatment Effects and Surrogate Endpoints in Sepsis

主要研究者/项目负责人（最后，第一，中间）：Reddy，Jr.，鲍比 项目摘要： 脓毒症是一种知之甚少的临床综合征，其特征是宿主对感染的反应失调。的 宿主反应的复杂性和异质性使开发有效治疗的尝试受挫。 Prenosis与6家美国医院合作，积累了NOSIS，这是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60，000份血浆或 超过12,000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。 在这个项目中，Prenosis将建立在以前的工作，进行有针对性的分析，个人治疗 皮质类固醇对脓毒症患者预后的影响。对患者基线数据使用倾向评分匹配 （他们被怀疑严重感染时的生物标志物/EMR谱），我们已经确定了1，350对 治疗组和对照组患者的样本已经收集并储存在生物库中。我们将 利用我们现有的管道来测量这2，700名患者的8，100个样本的40个核心生物标志物： 最接近治疗患者接受类固醇的时间的治疗前样本，最接近 治疗后24小时，患者接受类固醇，并通过患者的最终样本了解如何 治疗影响的生物标志物谱以及这些变化如何与关注的临床结果相关。 这些数据和分析将作为至少三种可立即商业化的产品的基础。第一、 阐明哪些患者可能受益于皮质类固醇治疗或患有皮质类固醇治疗将提高诊断率。 以及可在Prenosis现有的ImproxTM平台中部署的临床决策支持产品。第二、 这将证明NOSIS数据集是一个强大的平台，用于临床试验的预测富集。 在NOSIS中估计个体治疗效果可确定招募的最佳患者亚群 临床试验，否则就会失败。第三，了解治疗对生物标志物谱的影响， 与临床结果的相应关联将确立NOSIS宿主应答谱为有效 临床试验的替代终点。

Principal Investigator/Program Director (Last, first, middle): Reddy, Jr., Bobby Project Summary: Sepsis is a poorly understood clinical syndrome characterized by dysregulated host response to infection. The complexity and heterogeneity of the host response has frustrated attempts at developing effective treatments. In partnership with 6 U.S. hospitals, Prenosis amassed NOSIS, one of the world’s largest datasets and biobanks that combines biomarker and clinical data for patients suspected of infection, housing over 60,000 plasma or serum samples from over 12,000 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. In this project, Prenosis will build upon previous work to conduct targeted analyses of the individual treatment effects of corticosteroids on septic patient outcomes. Using propensity score matching on patient baseline data (their biomarker/EMR profile at the time they were suspected of serious infection), we have identified 1,350 pairs of treatment and control patients whose samples have already been collected and stored in the biobank. We will leverage our existing pipeline to measure the 40 core biomarkers on 8,100 samples for these 2,700 patients: the pre-treatment sample closest to the time at which the treatment patient received steroids, the sample closest to 24 hours after the treatment patient received steroids, and the patients’ final samples to understand how treatment impacted biomarker profile and how these changes are associated with clinical outcomes of interest. These data and analyses will serve as the basis for at least three immediately commercializable products. First, elucidating which patients are likely to benefit or suffer from treatment with corticosteroids will improve diagnostic and clinical decision support products that are deployable within Prenosis’ existing ImmunixTM platform. Second, this will demonstrate the NOSIS dataset is a powerful platform for predictive enrichment of clinical trials. Estimating individual treatment effects in NOSIS identifies the optimal patient subpopulations to be recruited for clinical trials, which would otherwise fail. Third, understanding treatment effects on biomarker profile and corresponding association with clinical outcomes will establish the NOSIS host response profile as effective surrogate endpoints for clinical trials.