Diuretic Use and Outcomes in Patients with Heart Failure with Reduced Ejection Fraction: Insights from the VICTORIA Trial
Bibliographic record
Abstract
Abstract Aims In VICTORIA, vericiguat compared with placebo reduced the risk of cardiovascular death (CVD) and heart failure hospitalization (HFH) in patients enrolled after a worsening heart failure (WHF) event. We examined clinical outcomes and efficacy of vericiguat as it relates to background use of loop diuretics in patients with WHF. Methods and results We calculated the total daily loop diuretic dose equivalent to furosemide dosing at randomization and categorized these as: no loop diuretic, 1–39, 41–80, 40, and >80 mg total daily dose (TDD). The primary composite outcome of CVD/HFH and its components were evaluated based on TDD loop diuretic and expressed as adjusted hazard ratios with 95% confidence intervals. Post-randomization rates of change in TDD were also examined. Of 4974 patients (98% of the trial) with diuretic dose information available at randomization, 540 (10.8%) were on no loop diuretic, 647 (13.0%) were on 1–39, 1633 (32.8%) were on 40, 1185 (23.8%) were on 41–80, and 969 (19.4%) were on >80 mg TDD. Patients with higher TDD had a higher rate of primary and secondary clinical outcomes. There were no significant interactions with TDD at randomization and efficacy of vericiguat versus placebo for any outcome (all pinteraction > 0.5). Post-randomization diuretic dose changes for vericiguat and placebo showed similar rates of up-titration (19.6 and 20.2/100 person-years), down-titration (16.8 and 18.1/100 person-years), and stopping diuretics (22.9 and 24.2/100 person-years). Conclusions Loop diuretic TDD at randomization was independently associated with worse outcomes in this high-risk population. The efficacy of vericiguat was consistent across the range of diuretic doses.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".