The Efficacy of Finerenone on Hierarchical Composite Endpoint Analysed Using Win Statistics in Patients with Heart Failure and Mildly Reduced or Preserved Ejection Fraction: A Prespecified Analysis of FINEARTS-HF
Bibliographic record
Abstract
AIMS: FINEARTS-HF demonstrated the efficacy of finerenone in reducing total worsening heart failure (HF) events (first and recurrent) and cardiovascular death, compared to placebo, in patients with HF and mildly reduced or preserved ejection fraction. We examined the effect of finerenone on these events according to their clinical importance using win statistics. METHODS AND RESULTS: We developed a prespecified hierarchical composite endpoint including the components of the original primary outcome: cardiovascular death (tier 1), total HF hospitalizations (tier 2), and total urgent HF visits (tier 3). For tiers 2 and 3, the number of events was analysed first, followed by the time-to-first event. Because win statistics are affected by the censoring distribution, we assessed the hierarchical composite outcome over a fixed period of 24 months. The 6001 participants analysed were randomized equally to finerenone (n = 3003) or placebo (n = 2998). At 24 months, a total of 825 cardiovascular deaths and worsening HF events were observed in the finerenone group, compared with 1012 events in the placebo group. The win ratio was 1.17 (95% confidence interval [CI] 1.04-1.32) (p = 0.010), demonstrating more wins than losses in the finerenone group. The win odds, corresponding to the treatment effect, was 1.05 (95% CI 1.01-1.09), and the net benefit, corresponding to the absolute risk difference, was 2.6% (95% CI 0.6-4.5%). The win ratio remained above 1.0 from 60 days after randomization and reached a plateau after approximately 12 months. HF hospitalizations contributed more to the overall results than cardiovascular death. The win odds at 12 months was 1.04 (95% CI 1.01-1.08), and when adding the Kansas City Cardiomyopathy Questionnaire total symptom score to the hierarchical endpoint as a continuous variable, that increased to 1.07, which is almost identical to the win ratio due to the decrease in ties. CONCLUSION: Finerenone treatment led to a significant improvement in a composite hierarchical outcome that incorporated cardiovascular death, total HF hospitalizations, and total urgent HF visits, with early onset of benefit. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov ID NCT04435626.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".