Initial Decline in Glomerular Filtration Rate With Finerenone in HFmrEF/HFpEF
Bibliographic record
Abstract
BACKGROUND: An initial decline in estimated glomerular filtration rate (eGFR) often leads to reluctance to continue life-saving therapies in patients with heart failure (HF). OBJECTIVES: The goal of this study was to describe the association between initial decline in eGFR and subsequent clinical outcomes in patients randomized to placebo or finerenone. METHODS: In this prespecified analysis of FINEARTS-HF (Finerenone Trial to Investigate Efficacy and Safety Superior to Placebo in Patients with Heart Failure), we examined the association between initial decline in eGFR (≥15%) from randomization to 1 month and subsequent outcomes in patients assigned to finerenone or placebo. The primary outcome was the composite of total HF events and cardiovascular death. RESULTS: = 0.50 for percent change in eGFR), and safety, including hyperkalemia, was similar regardless of an early eGFR decline. CONCLUSIONS: Although an initial decline in eGFR was associated with worse outcomes in patients assigned to placebo, this relationship was not as strong in those treated with finerenone. An early decline in eGFR can be anticipated with finerenone and should not automatically lead to the discontinuation of this disease-modifying therapy (FINEARTS-HF Finerenone Trial to Investigate Efficacy and Safety Superior to Placebo in Patients with Heart Failure [NCT04435626]; A Multicenter, Randomized, Double-Bline, Parallel-Group, Placebo-Controlled Study to Evaluate the efficacy and safety of finerenone on morbidity and mortality in participants With Heart Failure [NYHA II-IV] and left ventricular ejection fraction ≥40% [EudraCT 2020-000306-29]).
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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 | 0.002 | 0.002 |
| 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".