Component network meta-regression of ARNI and guideline therapy intensity in HFrEF: quantifying additive effects on HFH
P Amornritvanich, P Numthavaj, A Tansawet, A ThakkinstianAbstract
Background
Guideline-directed medical therapy (GDMT) in HFrEF consists of layered combination regimens. Conventional meta-analyses evaluate regimens as composite packages and cannot quantify the independent contribution of individual therapeutic components. We applied component network meta-regression to decompose the relative contributions of ARNI and foundational therapy intensity to reductions in heart failure hospitalization (HFH).
Methods
We performed a component network meta-regression using study-level comparative effectiveness data in HFrEF. Treatment regimens were decomposed into additive components representing ARNI exposure and GDMT intensity, defined by the number of concurrent foundational drug classes (RAS inhibitors, beta-blockers, and MRAs), categorized into three levels. Weighted least squares regression with cluster-robust variance estimation was applied to log hazard ratios for HFH across 13 eligible studies.
Results
Thirteen comparative studies comprising 53,508 patients (67.1% male, median follow-up 0.5–3.6 years) were included. In the primary additive model, ARNI was independently associated with reduced HFH (HR 0.93, 95% CI 0.88–0.99; p=0.021), while each one-level increase in GDMT intensity conferred an additional 9% relative risk reduction (HR 0.91, 95% CI 0.85–0.97; p=0.006). Effect estimates were directionally concordant with established randomized trial evidence.
Conclusion
Component-based decomposition of layered HFrEF regimens demonstrates that ARNI and foundational therapy intensity make quantitatively additive and independent contributions to reductions in HFH, supporting the value of both optimizing background GDMT and incorporating ARNI within combination regimens. This framework extends conventional regimen-level meta-analysis by enabling component-level inference, with potential implications for treatment sequencing and guideline implementation. The limited availability of SGLT2 inhibitors across included studies precludes stable component-level estimation for this drug class, representing a significant limitation and a priority for future analyses as trial data accumulate.methodological frameworkResults for HFH in HFrEF