Which patient groups benefit from antihypertensive medications, statins, and low-dose aspirin for the primary prevention of atherosclerotic cardiovascular disease: A regression discontinuity approach in large electronic health record data

哪些患者群体受益于抗高血压药物、他汀类药物和低剂量阿司匹林作为动脉粥样硬化性心血管疾病的一级预防：大型电子健康记录数据中的回归不连续性方法

• 批准号: 504369372
• 负责人: Professor Dr. Till Bärnighausen, Ph.D.
• 金额: --
• 依托单位: Heidelberger Institut für Global Health (HIGH)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/504369372?language=en
• 关键词: patient; groups; benefit; antihypertensive; medications

Randomized controlled trials have shown that antihypertensive treatment, statins, and low-dose aspirin can be effective for the primary prevention of atherosclerotic cardiovascular disease. Despite their existence for decades, there are still many unanswered questions on their optimal use, their effectiveness for large parts of the population, and their side effects.This project will test the feasibility and validity of the regression discontinuity design to estimate the causal effect of these treatments on mortality and the incidence of atherosclerotic cardiovascular disease using electronic health record (EHR) data. Regression discontinuity is a novel study design in clinical research that exploits exogenous variation in the treatment probability induced by threshold rules that exist in clinical guidelines to estimate the effect of treatment on outcomes. Intuitively, if the only reason why being just above or just below the threshold matters is that this circumstance determines if a patient is treated or not, then the impact of treatment will be the difference in outcomes between observations just above and just below the threshold.The enormous size of EHR data would allow for two innovations that have the potential for regression discontinuity to become a crucial tool in the movement towards more individually tailored cardiovascular treatment. First, regression discontinuity could not only be used to establish the average causal effect of antihypertensive treatment, statin therapy, and low-dose aspirin therapy in a “real-life” patient population but, in combination with machine learning, it also establishes a way to rigorously examine how treatment effects vary within these populations by a large array of patient characteristics. Specifically, EHR data would allow for sufficient statistical power to examine highly granular patient subgroups consisting of all permutations of important patient characteristics, such as combinations of various co- and multi-morbidities, body weight, age groups, and ethnicities. Second, employing rigorous methods for multiple hypothesis testing, regression discontinuity in EHR data can be used to causally test for a vast number of side effects – whether therapeutic or adverse – of medications and other treatments. For our analyses, we will use the Aurum and Gold dataset of the Clinical Practice Research Datalink (CPRD) data, which is large-scale EHR data from the UK. We will repeat all analyses using the Optum Clinformatics® Data Mart (CDM) version 8, containing data from the US, as well as Danish nation-wide EHR data.

随机对照试验表明，抗高血压治疗、他汀类药物和低剂量阿司匹林可有效预防动脉粥样硬化性心血管疾病。尽管这些药物已经存在了几十年，但它们的最佳使用、对大部分人群的有效性以及它们的副作用仍然存在许多未回答的问题，本项目将测试回归不连续设计的可行性和有效性，以利用电子健康记录（EHR）数据估计这些治疗对死亡率和动脉粥样硬化性心血管疾病发病率的因果效应。回归不连续性是临床研究中的一种新型研究设计，其利用存在于临床指南中的阈值规则引起的治疗概率的外源性变化来估计治疗对结果的影响。直觉上，如果仅仅高于或低于阈值的唯一原因是这种情况决定了患者是否接受治疗，那么，治疗的影响将是观察结果之间的差异刚刚高于和刚刚低于阈值。EHR数据的巨大规模将允许两个创新，这些创新具有回归不连续性的潜力，成为朝着更个性化的心血管治疗。首先，回归不连续性不仅可以用于在“现实生活”患者人群中建立抗高血压治疗，他汀类药物治疗和低剂量阿司匹林治疗的平均因果效应，而且与机器学习相结合，它还建立了一种严格检查治疗效果如何在这些人群中通过大量患者特征而变化的方法。具体而言，EHR数据将允许足够的统计功效来检查由重要患者特征的所有排列组成的高度粒度的患者亚组，例如各种合并症和多种疾病、体重、年龄组和种族的组合。其次，采用严格的方法进行多重假设检验，EHR数据中的回归不连续性可用于因果检验药物和其他治疗的大量副作用-无论是治疗性的还是不良的。对于我们的分析，我们将使用临床实践研究数据链（CPRD）数据的Aurum和Gold数据集，这是来自英国的大规模EHR数据。我们将使用Optum Clinformatics® Data Mart（CDM）第8版重复所有分析，其中包含来自美国的数据以及丹麦全国范围的EHR数据。