Finerenone in Patients with Severe Heart Failure: The FINEARTS-HF Trial
Bibliographic record
Abstract
Abstract Aims While patients with severe heart failure (HF) were historically considered to have reduced left ventricular ejection fraction (LVEF), it is increasingly recognized that severe HF occurs across the full spectrum of LVEF. The aim of this study was to assess prevalence, cardiovascular (CV) outcome risk, and treatment response to the non-steroidal mineralocorticoid receptor antagonist finerenone among patients with severe HF in FINEARTS-HF. Methods and results Treatment effects of finerenone on the primary endpoint of total (first and recurrent) HF events and CV death were assessed by severe HF status, as defined by the adapted multiparametric ESC-HFA criteria including New York heart Association functional class III/IV, hospitalization for HF within the previous 12 months, and impairment of health status measured by Kansas City Cardiomyopathy Questionnaire total symptom score <75. Overall, 888 (14.8%) patients fulfilled the definition for severe HF. Patients with severe HF were older, with a higher comorbidity burden, and higher N-terminal pro-B-type natriuretic peptide levels. Over a median follow-up of 2.7 years, total HF events and CV death occurred at a higher rate among those with severe HF (31.6 per 100 patient-years [py]) as compared to those without severe HF (13.9 per 100py). Finerenone was beneficial in reducing the rate of the primary endpoint regardless of severe HF status (pinteraction = 0.98), with a greater absolute rate reduction in those with severe HF (5.9 per 100py) compared with those without severe HF (2.2 per 100py) in light of higher baseline risk. The proportion of patients who discontinued study treatment for any reason or experienced adverse events according to treatment assignment was similar regardless of severe HF status. Conclusions Among patients with mildly reduced or preserved LVEF, severe HF was associated with a heightened risk of CV events. Treatment with finerenone appeared safe and effective, regardless of HF severity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".