Real-World and Subgroup Data Synthesis to Guide PCSK9 Inhibitor Use in Cardiovascular Disease

Background Randomized controlled trials (RCTs) are the gold standard for assessing the efficacy of medical interventions; however, their findings often do not fully translate into real-world populations due to differences in patient characteristics. In this study, we aimed to estimate real-world populations treatment effects for PCSK9 inhibitors in patients with atherosclerotic cardiovascular disease (ASCVD) by integrating real-world data with subgroup-level data from two major RCTs, FOURIER and ODYSSEY LONG TERM. This approach seeks to improve precision medicine by aligning trial results with real-world data. Methods We combined individual-level data from six large population-based studies (N = 872,550) with subgroup data from the FOURIER and ODYSSEY LONG TERM trials. A meta-interpolation method was applied to estimate treatment effects for two PCSK9 inhibitors, evolocumab and alirocumab, using regression modeling to adjust for covariates such as age, sex, race, and cardiovascular history. Hazard ratios (HRs) for major adverse cardiovascular events were estimated, and sensitivity analyses were conducted to test the robustness of the findings. Results In a U.S.-representative population, evolocumab demonstrated consistent efficacy across subgroups with an HR of 0.73 (95% confidence interval [CI], 0.65–0.82). In contrast, alirocumab showed variable efficacy, with a significant difference in treatment effects between men (HR, 0.71) and women (HR, 0.89; P < 0.001). Sensitivity analyses confirmed the robustness of these findings across different covariate balance thresholds and data assumptions. Conclusions Integrating real-world data with RCT subgroup data via meta-interpolation offers a reliable method to estimate treatment effects tailored to real-world populations. Our results suggest that evolocumab provides consistent benefit across various patient subgroups, while alirocumab’s efficacy may vary based on factors such as sex and comorbidities. This approach can guide more personalized treatment decisions in ASCVD, advancing the field of precision cardiovascular pharmacotherapy. Trial Registration: Not applicable.