The impact of clinical interventions for sepsis in routine care and among detailed patient subgroups: A novel approach for causal effect estimation in electronic health record data

脓毒症估计在全球造成五分之一的死亡，其中包括世界上每年约190，000例死亡。 美国的鉴于病情的复杂性和异质性，"一刀切"的方法来治疗脓毒症， 护理主要是临床指南采取的方法，不太可能是最有效的。然而， 可在每个患者亚组中进行随机对照试验（RCT）， 重要社会人口统计学的数百种可能组合（例如，年龄组）和临床（例如， 合并症或脓毒症的原因）的患者特征。电子健康档案的观察性研究 (EHR)由于数据量大且具有"现实生活"的代表性， EHR数据。然而，此类观察性研究有一个关键的缺点，即它们被认为 仅仅产生关联，而不是因果效应估计，因为它们使不可检验的和经常 在分析中对所有混杂因素进行了完美测量和调整的不合理假设。的 本创新奖的目的是开发和测试一种新的脓毒症临床研究设计 - 机器学习促进的回归不连续性（ML促进的RD）-这将使研究人员 在大规模EHR数据中确定常见脓毒症护理干预措施的因果效应，而无需依赖 关于混杂因素调整ML促进的RD将机器学习与一种新的因果推理相结合 技术（回归不连续性），以提高因果效应估计技术的稳健性，其 能够可靠地确定大量高粒度患者亚组中每个亚组的因果效应，以及 为了确定连续变量中的最佳阈值（例如，平均动脉压）， 应在每个患者亚组中启动感兴趣的干预。我们将进一步发展研发， 可以应用于败血症临床护理中常见的多因素决策。该项目有两个 步在第一步中，我们将借助广泛的模拟来发展这些方法创新 演习在第二步中，我们将测试ML促进RD的可行性和有效性，为12个 在来自各种临床环境的15个EHR数据集中的每一个中，败血症的常见临床干预。关键 该项目的创新之处在于，它旨在建立一个关于败血症的EHR数据的研究设计， 与目前最先进的方法相比，这是一种根本不同的因果效应估计方法。通过 为临床研究人员提供了一种新的工具，用于确定脓毒症临床干预措施的因果效应 在常规护理中以及在高度颗粒化的患者亚组中（包括在连续临床试验中的阈值）， 对于在每个亚组中启动这些干预措施是最佳的），这项研究将构成一个 在脓毒症的个性化护理方面迈出了重要一步。它还将为今后的工作奠定重要基础。 方法创新和调整，以允许ML促进EHR数据的RD和类似的因果推理 在临床医学的其他领域使用的方法。

Sepsis causes an estimated one in five deaths globally, including approximately 190,000 deaths per year in the United States. Given the complexity and heterogeneity of the condition, a “one-size-fits-all” approach to sepsis care, which is largely the approach taken by clinical guidelines, is unlikely to be most effective. Yet, it is not feasible to conduct a randomized controlled trial (RCT) among each patient subgroup that can be formed from the hundreds of possible combinations of important sociodemographic (e.g., age group) and clinical (e.g., comorbidities or cause of sepsis) characteristics of patients. Observational studies in electronic health record (EHR) data could circumvent this feasibility constraint thanks to the large size and “real-life” representativeness of EHR data. However, such observational studies have the critical disadvantage that they are thought to merely yield associations rather than causal effect estimates, because they make the untestable and frequently implausible assumption that all confounders were perfectly measured and adjusted for in the analysis. The objective of this New Innovator Award is to develop and test a new study design for clinical research on sepsis – machine-learning-facilitated regression discontinuity (ML-facilitated RD) – that would allow researchers to determine causal effects for common sepsis care interventions in large-scale EHR data without needing to rely on confounder adjustment. ML-facilitated RD combines machine learning with a novel causal inference technique (regression discontinuity) to improve the robustness of the technique for causal effect estimation, its ability to reliably determine causal effects for each of a large number of highly granular patient subgroups, and to ascertain the optimal threshold in continuous variables (e.g., in mean arterial pressure) at which the intervention of interest should be initiated in each patient subgroup. We will additionally develop RD such that it can be applied to the multi-factorial decisions that are common in clinical care for sepsis. This project has two steps. In the first step, we will develop these methodological innovations with the aid of extensive simulation exercises. In the second step, we will test the feasibility and validity of ML-facilitated RD for each of 12 common clinical interventions for sepsis in each of 15 EHR datasets from a variety of clinical settings. The key innovation of this project is that it aims to establish a study design for EHR data on sepsis that uses a fundamentally different approach for causal effect estimation than current state-of-the-art methods. By providing a new tool to clinical researchers for determining the causal effects of clinical interventions for sepsis in routine care and among highly granular patient subgroups (including which threshold in continuous clinical measurements is optimal for initiating these interventions in each subgroup), this research would constitute a major step forwards in individualizing care for sepsis. It would also establish an important foundation for further methodological innovation and adaptation to allow ML-facilitated RD in EHR data and similar causal inference approaches to be used in other areas of clinical medicine.