Cost-Effectiveness of Mineralocorticoid Receptor Antagonists in Ischemic and Nonischemic Heart Failure With Reduced Ejection Fraction: Perspective From a Universal Healthcare System
Bibliographic record
Abstract
OBJECTIVES: Mineralocorticoid receptor antagonists (MRAs) are cornerstones in the management of heart failure (HF) with reduced ejection fraction (HFrEF). New MRAs with improved safety profile, such as finerenone and eplerenone, were recently introduced. However, because of typical budget restrictions in middle-income countries, evaluating their cost-effectiveness is essential for optimizing treatment strategies. METHODS: We used a Bayesian network and Markov influence diagrams to estimate the incremental cost-effectiveness ratios (ICERs) in international dollars (Int$) per quality-adjusted life-year (QALY). Our model was fed by a systematic review and a network meta-analysis to compare MRAs effectiveness and used data from a cohort of 1066 Brazilian individuals with HFrEF (36% with ischemic and 64% with nonischemic disease). RESULTS: Over a 10-year time horizon, the treatment with spironolactone, eplerenone, and finerenone compared with no MRA utilization yielded discounted QALY per person of 0.072, 0.111, and 0.034, respectively. The ICERs were Int$7955, Int$6460, and Int$109 840 per QALY gained, respectively. Compared with spironolactone, eplerenone showed an ICER of Int$6178 per QALY gained. Assuming a willingness-to-pay threshold of 1 Brazilian per capita gross domestic product (Int$17 589) per QALY gained, the probabilistic sensitivity analyses suggest that spironolactone and eplerenone were cost-effective, respectively, in 87% and 92% of iterations. The 95% CIs were Int$2282 to Int$13 149 for spironolactone and Int$1795 to Int$12 351 for eplerenone per QALY gained. These findings were consistent across several scenarios including ischemic/nonischemic HF. CONCLUSIONS: Eplerenone is likely the most cost-effective MRA in Brazil considering individuals with both ischemic and nonischemic HFrEF.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".