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

据估计，全球五分之一的死亡是由脓毒症引起的，其中每年约有19万人死于败血症。 美国。鉴于这种情况的复杂性和异质性，“一刀切”的方法治疗脓毒症 护理在很大程度上是临床指南采取的方法，但不太可能是最有效的。然而，它并不是 在每个患者亚组中进行随机对照试验(RCT)是可行的，可以从 重要的社会人口统计(例如，年龄组)和临床(例如， 合并症或败血症的原因)患者的特征。电子健康档案中的观察性研究 (EHR)数据可以避开这种可行性限制，这要归功于其庞大的规模和现实生活中的代表性 电子病历数据。然而，这种观察性研究有一个严重的缺点，那就是它们被认为是 只是产生关联，而不是因果关系估计，因为它们使无法检验的和频繁的 令人难以置信的假设，即所有混杂因素在分析中都得到了完美的衡量和调整。这个 这一新创新者奖的目标是开发和测试一种用于脓毒症临床研究的新研究设计 -机器学习促进的回归不连续性(ML-促进的RD)-这将允许研究人员 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，而不需要依赖 关于混杂调整。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)，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颗粒患者亚组中每一组的因果关系，以及 确定连续变量(例如，平均动脉压)中的最佳阈值 应在每个患者亚组中启动有意义的干预。我们将额外发展研发，使其 可应用于脓毒症临床护理中常见的多因素决策。这个项目有两个 台阶。在第一步中，我们将借助广泛的模拟来开发这些方法创新 练习。在第二步中，我们将测试ML促进的RD的可行性和有效性 来自各种临床环境的15个EHR数据集中的每个败血症的常见临床干预措施。钥匙 该项目的创新之处在于，它旨在建立一项关于脓毒症的EHR数据的研究设计，该研究设计使用了 与目前最先进的方法相比，因果效应估计的方法有根本的不同。通过 为临床研究人员确定脓毒症临床干预措施的因果关系提供了新的工具 在常规护理和高粒度患者亚组中(包括连续临床中的哪个阈值 测量是在每个亚组中启动这些干预的最佳方法)，这项研究将构成 在脓毒症个体化护理方面向前迈进了一大步。它还将为进一步 方法创新和调整，以允许在EHR数据和类似因果推理中使用ML促进的RD 可用于临床医学其他领域的方法。

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.