用真实医疗数据验证慢性肾病干预效果，发现肾功能是核心影响因素。
Testing Causal Explanations: A Case Study for Understanding the Effect of Interventions on Chronic Kidney Disease
- 基于电子病历数据构建类随机实验，结合动态贝叶斯网络分析因果关系。
- 在超两百万患者中验证，肾小球滤过率（eGFR）是最重要的影响变量。
- 适用于临床研究者和医疗决策者，为真实世界干预提供科学依据。
随机对照试验（RCT）是评估临床干预效果的标准方法。为克服RCT在真实人群中的局限性，我们开发了一种利用大规模电子健康记录（EHR）数据的方法。基于回归断点设计（RD），生成类随机数据子集，通过动态贝叶斯网络（DBNs）的do-操作测试专家提出的干预措施。该方法应用于超过两百万患者的慢性肾病（CKD）队列，分析了与估算肾小球滤过率（eGFR）下降≥40%这一替代终点相关的关联与因果关系。来自两个独立医疗系统的DBN分析结果一致：关联分析显示最影响变量为eGFR、尿白蛋白/肌酐比值和脉压；因果分析则表明eGFR为最重要变量，其次为可调节因素如长期影响肾功能的药物。该方法展示了如何利用真实世界EHR数据为改进医疗提供群体层面洞察。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。