发表机构
Mayo Clinic; Virginia Commonwealth University; Mayo Clinic School of Graduate Medical Education（梅奥诊所; 弗吉尼亚联邦大学; 梅奥诊所研究生医学教育学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究利用电子健康记录模拟临床试验，通过HTE引导的分层识别射血分数降低心力衰竭患者亚组。核心方法是用Cox模型评估死亡率、Meta - S学习者估计HTEs等。主要贡献是揭示了全队列模拟中被掩盖的有益和有害治疗效果模式。
AI 中文摘要
传统随机试验往往关注平均效果，掩盖了治疗反应中具有临床意义的异质性。利用真实世界数据模拟临床试验并估计异质性治疗效果（HTEs），为更精确有效的试验设计提供了一条有前景的途径。本研究利用梅奥诊所云（MCC）的电子健康记录模拟DAPA - HF试验，以调查HTE引导的分层是否能识别射血分数降低的心力衰竭患者中对达格列净与安慰剂有不同治疗反应的患者亚组。使用Cox比例风险模型评估全因死亡率，用Meta - S学习者估计HTEs，用基于决策树的阈值方法定义亚组。在模拟的总体队列中，未观察到显著的治疗差异。但与总体模拟队列相比，HTE驱动的分层识别出了具有显著且方向不同治疗效果的亚组。有益（低HTE）亚组从达格列净中显示出显著的生存益处，而有害（高HTE）亚组与死亡率风险显著增加有明显关联。这些发现表明，HTE引导的分层可以揭示在全队列模拟中被掩盖的具有临床意义的有益和有害治疗效果模式。
英文摘要
Traditional randomized trials often obscure clinically meaningful heterogeneity in treatment response by focusing on average effects. Leveraging real-world data to emulate clinical trials and estimate heterogeneous treatment effects (HTEs) offers a promising path toward more precise and efficient trial design. In this study, we emulate the DAPA-HF trial using electronic health records from the Mayo Clinic Cloud (MCC) to investigate whether HTE-guided stratification can identify patient subgroups with distinct treatment responses to dapagliflozin versus placebo in patients with heart failure with reduced ejection fraction. All-cause mortality was evaluated using Cox proportional hazards models, with HTEs estimated using a Meta-S learner and subgroups defined using a decision tree-based thresholding approach. In the overall cohort of the emulation, no significant treatment difference was observed (HR, 1.681; 95% CI, 0.828-3.413; p = 0.1507). However, compared with the overall emulated cohort, in which dapagliflozin showed no statistically significant survival benefit, HTE-driven stratification identified subgroups with significant and directionally distinct treatment effects. The beneficial (low-HTE) subgroup showed a significant survival benefit from dapagliflozin (HR = 0.203, 95% CI, 0.087-0.476, p = 0.0002), whereas the harmful (high-HTE) subgroup showed a significant harmful association with markedly increased mortality risk (HR = 6.680, 95% CI, 2.759-16.171, p < 0.0001). These findings indicate that HTE-guided stratification can uncover clinically meaningful beneficial and harmful treatment-effect patterns that are masked in the full-cohort emulation.
Comments21 pages, 7 figures
