Precision Medicine Approach to Glucocortisteroids in Sepsis

项目总结 脓毒症是一种疾病，在美国大约三分之一的医院死亡是由这种疾病引起的 全球每5人中就有1人死亡。推进脓毒症治疗一直具有挑战性，部分原因是 脓毒症患者在人口学、合并症、感染源、 微生物学病因学和器官功能障碍程度。我们小组此前已确诊为脓毒症 预后和治疗反应不同的亚类，表明精确度 药物可能会改善脓毒症的护理。糖皮质激素(GC)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 开发一种精准医学方法的候选人。最近，有史以来最大的两个RCT 用来测试GCs对脓毒症(APROCCHSS和肾上腺)的疗效也证明了 相互矛盾的结果。事实证明，在试验之间调和不一致的结果具有挑战性 传统的方法，但可以由最先进的计算方法来促进，这些方法 结合机器学习估计条件平均处理效果 个体协变量模式。在这份提案中，我们将创建一个使用临床知识网络 和4个脓毒症GCs RCT的生物学数据(APROCCHSS，肾上腺，逃逸， HYPRESS)和电子健康记录数据(急诊护理项目中的败血症内切分型)。 在目标1中，我们将利用临床数据利用无监督和有监督的学习方法 从随机对照试验中描述治疗效果的异质性，确定受益的亚类， 并制定治疗政策，以减少90天的死亡率。在目标2中，我们将使用因果关系 结合RCT和EHR数据以确定效果修饰剂的贝叶斯建模方法 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 减少90天死亡率的政策。二次分析将比较目标1中仅限RCT的政策 在目标2中，我们将使用RCT-EHR政策进行细胞因子分析和RNAseq 来自肾上腺、EASH和HYPRESS试验的样本以确定有益的内型 来自大中华区的。我们已经组建了一支由临床试验专家、生物统计学家、 计算生物学家和重症监护专家 这项提案的协作。成功完成我们的目标将调和不和谐 先前RCT在脓毒症中测试GC的结果，制定可部署在 EHR，并改进未来RCT的设计。

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.