Effects of Combined Renin–Angiotensin–Aldosterone System Inhibitor and Beta-Blocker Treatment on Outcomes in Heart Failure with Reduced Ejection Fraction: Insights from BIOSTAT-CHF and ASIAN-HF Registries
Bibliographic record
Abstract
BACKGROUND: Angiotensin-converting enzyme inhibitors (ACEi)/angiotensin receptor blockers (ARB) and β-blockers are guideline-recommended first-line therapies in heart failure (HF) with reduced ejection fraction (HFrEF). Previous studies showed that individual drug classes were under-dosed in many parts of Europe and Asia. In this study, we investigated the association of combined up-titration of ACEi/ARBs and β-blockers with all-cause mortality and its combination with hospitalization for HF. METHODS AND RESULTS: A total of 6787 HFrEF patients (mean age 62.6 ± 13.2 years, 77.7% men, mean left ventricular ejection fraction 27.7 ± 7.2%) were enrolled in the prospective multinational European (BIOSTAT-CHF; n = 2100) and Asian (ASIAN-HF; n = 4687) studies. Outcomes were analysed according to achieved percentage of guideline-recommended target doses (GRTD) of combination ACEi/ARB and β-blocker therapy, adjusted for indication bias. Only 14% (n = 981) patients achieved ≥50% GRTD for both ACEi/ARB and β-blocker. The best outcomes were observed in patients who achieved 100% GRTD of both ACEi/ARB and β-blocker [hazard ratio (HR) 0.32, 95% confidence interval (CI) 0.26-0.39 vs. none]. Lower dose of combined therapy was associated with better outcomes than 100% GRTD of either monotherapy. Up-titrating β-blockers was associated with a consistent and greater reduction in hazards of all-cause mortality (HR for 100% GRTD: 0.40, 95% CI 0.25-0.63) than corresponding ACEi/ARB up-titration (HR 0.75, 95% CI 0.53-1.07). CONCLUSION: This study shows that best outcomes were observed in patients attaining GRTD for both ACEi/ARB and β-blockers, unfortunately this was rarely achieved. Achieving >50% GRTD of both drug classes was associated with better outcome than target dose of monotherapy. Up-titrating β-blockers to target dose was associated with greater mortality reduction than up-titrating ACEi/ARB.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".