Randomized controlled trials (RCTs) are the standard for evaluating the effectiveness of clinical interventions. To address the limitations of RCTs on real-world populations, we developed a methodology that uses a large observational electronic health record (EHR) dataset. Principles of regression discontinuity (rd) were used to derive randomized data subsets to test expert-driven interventions using dynamic Bayesian Networks (DBNs) do-operations. This combined method was applied to a chronic kidney disease (CKD) cohort of more than two million individuals and used to understand the associational and causal relationships of CKD variables with respect to a surrogate outcome of >=40% decline in estimated glomerular filtration rate (eGFR). The associational and causal analyses depicted similar findings across DBNs from two independent healthcare systems. The associational analysis showed that the most influential variables were eGFR, urine albumin-to-creatinine ratio, and pulse pressure, whereas the causal analysis showed eGFR as the most influential variable, followed by modifiable factors such as medications that may impact kidney function over time. This methodology demonstrates how real-world EHR data can be used to provide population-level insights to inform improved healthcare delivery.

翻译：随机对照试验是评估临床干预措施有效性的金标准。为克服随机对照试验在真实世界人群中的局限性，我们开发了一种利用大规模观察性电子健康记录数据集的方法论。该方法运用断点回归原理推导随机化数据子集，通过动态贝叶斯网络的do-操作检验专家驱动的干预措施。我们将这种组合方法应用于超过两百万人的慢性肾脏病队列，用以分析慢性肾脏病变量与估算肾小球滤过率下降≥40%这一替代结局之间的关联关系与因果关系。基于两个独立医疗系统的动态贝叶斯网络模型，关联分析与因果分析呈现了相似的结果。关联分析显示最具影响力的变量是估算肾小球滤过率、尿白蛋白肌酐比和脉压，而因果分析表明估算肾小球滤过率是最具影响力的变量，其次是可能随时间影响肾功能的药物等可调控因素。本方法论证明了如何利用真实世界电子健康记录数据提供群体层面的洞见，从而为改善医疗服务的实施提供依据。