Precision Medicine Approach to Glucocortisteroids in Sepsis

糖皮质激素治疗脓毒症的精准医学方法

• 批准号: 10460580
• 负责人: Derek C Angus
• 金额: $ 64.9万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-02 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10460580
• 关键词: Address; Adrenal Glands; Affect; Algorithms; American; Bayesian Modeling; Bayesian Network; Biological; Biological Assay; Biological Markers; Blood specimen; Caring; Cessation of life; Clinical; Clinical Data; Collaborations; Computing Methodologies; Conflict (Psychology); Critical Care; Data; Data Set; Development; Disease; Electronic Health Record; Emergency Care; Etiology; Functional disorder; Future; Glucocorticoids; Goals; Heterogeneity; Hospitals; Individual; Infection; International; Knowledge; Lead; Machine Learning; Mathematics; Methods; Microbiology; Modeling; Organ; Patient Care; Patients; Pattern; Phenotype; Policies; Precision therapeutics; Prognosis; Randomized; Randomized Controlled Clinical Trials; Randomized Controlled Trials; Risk; Risk Reduction; Sampling; Sepsis; Septic Shock; Serum; Source; Specialist; Steroids; Techniques; Testing; Translating; United States; Whole Blood; base; biobank; clinical decision support; clinical practice; cohort; comorbidity; cytokine; demographics; design; efficacy testing; improved; improved outcome; individualized medicine; innovation; mortality; multidisciplinary; novel; optimal treatments; personalized medicine; pre-clinical therapy; precision medicine; predictive modeling; response; secondary analysis; septic patients; success; supervised learning; support tools; transcriptome sequencing; transcriptomics; treatment effect; treatment response; unsupervised learning

PROJECT SUMMARY Sepsis is a disorder that contributes to approximately 1 in 3 hospital deaths in the United States and 1 in 5 deaths worldwide. Advancing sepsis management has been challenging in part due to the heterogeneity of septic patients in demographics, comorbidities, infectious source, microbiologic etiology, and level of organ dysfunction. Our group has previously identified sepsis subclasses that differ in prognosis and response to treatment, suggesting that precision medicine may improve sepsis care. Glucocorticoids (GCs) are commonly used in septic patients despite inconsistent results from randomized controlled trials (RCTs), and they are an ideal candidate to develop a precision medicine approach. Recently, two of the largest RCTs ever conducted to test efficacy of GCs in sepsis (APROCCHSS and ADRENAL) also demonstrated conflicting results. Reconciling discordant results between trials has proved challenging with traditional methods but may be facilitated by state-of-the-art computational approaches which incorporate machine learning to estimate the conditional average treatment effect based on individual covariate patterns. In this proposal, we will create a ‘knowledge network’ using clinical and biologic data from 4 RCTs of GCs in sepsis (APROCCHSS, ADRENAL, ESCAPe, HYPRESS) and electronic health record data (Sepsis Endotyping in Emergency Care project). In Aim 1, we will utilize unsupervised and supervised learning approaches using clinical data from RCTs to characterize heterogeneity of treatment effect, identify subclasses that benefit, and develop a treatment policy to reduce 90-day mortality. In Aim 2, we will use causal Bayesian modeling approaches that incorporate RCT and EHR data to identify effect modifiers and confounders of GC therapy and mortality. We will use these results to develop a treatment policy to reduce 90-day mortality. Secondary analyses will compare RCT-only policies in Aim 1 to RCT-EHR policies in Aim 2. In Aim 3, we will perform cytokine assays and RNAseq using samples from the ADRENAL, ESCAPe, and HYPRESS trials to identify endotypes that benefit from GCs. We have assembled a multidisciplinary team of clinical trialists, biostatisticians, computational biologists, and critical care specialists with an established track record of collaboration for this proposal. Successful completion of our Aims will reconcile discordant results of prior RCTs testing GCs in sepsis, develop a treatment policy that can be deployed in EHRs, and improve design of future RCTs.

项目摘要 脓毒症是一种疾病，在美国，约三分之一的医院死亡是由脓毒症引起的。 全世界五分之一的死亡人数推进脓毒症管理一直具有挑战性，部分原因是 脓毒症患者在人口统计学、合并症、感染源 微生物病因学和器官功能障碍水平。我们小组之前发现败血症 亚类在预后和对治疗的反应方面不同，这表明精确性 药物可以改善脓毒症护理。糖皮质激素（GC）通常用于脓毒症患者 尽管随机对照试验（RCT）的结果不一致，但它们是理想的 候选人开发精准医疗方法。最近，有史以来最大的两个随机对照试验 用于测试GC在脓毒症中的功效（APROCCHSS和ADRENAL）的研究也证明了 矛盾的结果。消除试验之间的不一致结果已被证明具有挑战性， 但是可以通过最先进的计算方法来促进， 结合机器学习，根据以下因素估计条件平均治疗效果： 个体协变量模式。在本提案中，我们将使用临床知识创建一个“知识网络”， 和来自4项败血症GC RCT的生物学数据（APROCCHSS，ADRENAL，ESCAPe， HYPRESS）和电子健康记录数据（紧急护理项目中的败血症内定型）。 在目标1中，我们将使用临床数据使用无监督和监督学习方法 从随机对照试验中描述治疗效果的异质性，确定获益的亚类， 并制定治疗政策以降低90天死亡率。在目标2中，我们将使用因果关系 结合RCT和EHR数据的贝叶斯建模方法，以识别效应调节剂 以及GC治疗和死亡率的混杂因素。我们将利用这些结果来开发一种治疗方法 降低90天死亡率的政策。次要分析将比较目标1中的仅RCT政策 目标2中的RCT-EHR政策。在目标3中，我们将使用以下方法进行细胞因子测定和RNAseq： 来自ADRENAL、ESCAPe和HYPRESS试验的样本，以确定获益的内型 从GC。我们组建了一支由临床试验员、生物统计学家， 计算生物学家和重症监护专家， 为这项提案而合作。成功完成我们的目标将调和不和谐 先前的RCT测试GC在脓毒症中的结果，制定一项治疗政策， EHR，并改进未来RCT的设计。