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

脓毒症临床干预措施对常规护理和详细患者亚组的影响：电子健康记录数据因果效应估计的新方法

• 批准号: 10505906
• 负责人: Pascal Geldsetzer
• 金额: $ 47.22万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-18 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10505906
• 关键词: Affect; Area; Award; Caring; Cessation of life; Characteristics; Clinical; Clinical Medicine; Clinical Research; Clinical Trials; Clinical effectiveness; Complement; Complex; Confounding Factors (Epidemiology); Data; Data Set; Diagnosis; Disadvantaged; Effectiveness; Electronic Health Record; Erythrocyte Transfusion; Exercise; Foundations; Guidelines; Hemoglobin; Heterogeneity; Household; Intensive Care Units; Intervention; Life; Machine Learning; Measurement; Measures; Medicine; Methodology; Methods; Monitor; Nature; Observational Study; Outcome; Patients; Probability; Public Health; Randomized; Randomized Controlled Trials; Research; Research Design; Research Personnel; Sepsis; Series; Subgroup; Surveys; Techniques; Testing; Uncertainty; United States; Work; age group; base; clinical care; clinical effect; comorbidity; effectiveness evaluation; feasibility testing; health care settings; improved; innovation; interest; intervention effect; method development; novel; novel strategies; patient subsets; personalized care; population health; pressure; randomized trial; routine care; screening; septic patients; simulation; sociodemographics; statistical and machine learning; tool; treatment effect; virtual

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.

据估计，脓毒症导致全球五分之一的死亡，其中每年约有 19 万人死亡 鉴于病情的复杂性和异质性，针对脓毒症采取“一刀切”的方法。 护理，这主要是临床指南所采取的方法，不太可能是最有效的，但它并不是最有效的。 在每个患者亚组中进行随机对照试验（RCT）是可行的，该试验可以由 重要的社会人口统计（例如年龄组）和临床（例如， 电子健康记录中的观察性研究。 由于数据规模大且具有“现实生活”代表性，（EHR）数据可以规避这种可行性限制 然而，此类观察性研究有一个严重的缺点，即它们被认为是 仅仅产生关联而不是因果效应估计，因为它们使得无法测试并且经常 令人难以置信的假设是，在分析中所有混杂因素都得到了完美的测量和调整。 该新创新者奖的目的是开发和测试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-facilitated RD） – 这将使研究人员能够 确定大规模 EHR 数据中常见脓毒症护理干预措施的因果影响，无需依赖 机器学习促进的 RD 将机器学习与新颖的因果推理相结合。 技术（回归不连续性）来提高因果效应估计技术的鲁棒性，其 能够可靠地确定大量高度精细的患者亚组中每个亚组的因果效应，以及 确定连续变量（例如平均动脉压）的最佳阈值，在该阈值 应在每个患者亚组中启动感兴趣的干预措施，我们将另外开发 RD，以便它能够实施。 可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。 第一步，我们将借助广泛的模拟来开发这些方法创新。 在第二步中，我们将测试 12 个 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 数据集中脓毒症的常见临床干预措施。 该项目的创新之处在于，它旨在建立脓毒症 EHR 数据的研究设计，该设计使用 与当前最先进的方法完全不同的因果效应估计方法。 为临床研究人员提供一种新工具，用于确定脓毒症临床干预措施的因果影响 在常规护理中，在细粒度患者亚组中高度（包括连续临床中的阈值） 测量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 在脓毒症个体化护理方面迈出了重要一步，这也将为进一步的发展奠定重要基础。 方法创新和适应，以允许在 EHR 数据中进行 ML 促进的 RD 和类似的因果推理 方法可用于临床医学的其他领域。