Optimizing Clinical Trial Protocols Using EHR-Derived Heterogeneous Treatment Effects
Artificial IntelligenceMachine Learning
Summary
The authors used electronic health records to mimic a heart failure drug trial and looked for differences in how patients responded to treatment. While the overall group didn't show a clear benefit from the drug dapagliflozin, the authors found distinct patient subgroups with very different results. One subgroup benefited a lot from the drug, while another subgroup actually had worse outcomes. This shows that looking at subgroups can reveal important treatment effects that average results might hide.
Authors
Xiaodi Li, Munhuwan Lee, Pengyang Li, Xiaoke Liu, Jose K. James, Patricia A. Pellikka, Cui Tao, Nansu Zong
Abstract
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.