Abstract 14658: Comparative Effectiveness of Torsemide versus Furosemide in Acute Heart Failure Patients: Insights from ASCEND-HF
Bibliographic record
Abstract
Introduction: Furosemide is the most commonly used loop diuretic in heart failure (HF) patients despite potential pharmacologic and anti-fibrotic benefits with torsemide. Hypothesis: We hypothesized that the comparative benefits of post-discharge use of torsemide would be superior to furosemide in a large acute HF trial. Methods: We investigated HF patients in ASCEND-HF who were discharged on either torsemide or furosemide. Given regional variation in torsemide use, we restricted analyses to the 6 countries with at least 20 patients on one of the diuretics and patients on torsemide. Using inverse probability weighting (IPW) to account for selection of diuretic, we assessed the relationship between diuretic at discharge with 30-day mortality or HF hospitalization, and 30- and 180-day mortality. Results: Of 7,141 patients in the trial, 3,282 patients were included in this analysis, of which, 88% (n=2,893) received furosemide and 12% (n=389) received torsemide. Torsemide-treated patients had lower blood pressure, and higher creatinine and BUN at baseline compared with furosemide-treated patients. On adjusted analysis, torsemide use was associated with a trend toward lower 30-day mortality or HF hospitalization (OR 0.62, 95% CI: 0.37-1.04; P=0.067). Torsemide was associated with similar 30-day mortality (OR 0.77, 95% CI: 0.28-2.09; P=0.60), and significantly reduced 180-day mortality (HR 0.56, 95% CI: 0.36-0.87; P=0.038) compared with furosemide (Figure). Conclusion: In this acute HF trial, a minority of patients received torsemide and commonly had indicators of higher risk. After risk-adjustment, torsemide was associated with lower risk of 180-day mortality. These data should be considered as hypothesis-generating and prospective, randomized comparative effectiveness trials are needed to investigate the optimal diuretic choice between torsemide vs. furosemide.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".