Reduced Loop Diuretic Use in Patients Taking Sacubitril/Valsartan Compared with Enalapril: The PARADIGM-HF Trial
Bibliographic record
Abstract
AIMS: To assess differences in diuretic dose requirements in patients treated with sacubitril/valsartan compared with enalapril in the Prospective comparison of ARNI with ACEI to Determine Impact on Global Mortality and morbidity in Heart Failure (PARADIGM-HF) trial. METHODS AND RESULTS: Overall, 8399 patients with New York Heart Association class II-IV heart failure and reduced LVEF were randomized to sacubitril/valsartan 200 mg bid or enalapril 10 mg twice daily. Loop diuretic doses were assessed at baseline, 6, 12, and 24 months, and furosemide dose equivalents were calculated via multiplication factors (2x for torsemide and 40x for bumetanide). Percentages of participants with reductions or increases in loop diuretic dose were determined. At baseline, 80.8% of participants were taking any diuretics (n = 6290 for loop diuretics, n = 496 for other diuretics); of those, recorded dosage data for loop diuretics were available on 5487 participants. Mean baseline furosemide equivalent doses were 48.2 mg for sacubitril/valsartan and 49.6 mg for enalapril (P = 0.25). Patients treated with sacubitril/valsartan were more likely to reduce diuretic dose and less likely to increase diuretic dose relative to those randomized to enalapril at 6, 12, 24 months post-randomization, with an overall decreased diuretic use of 2.0% (P = 0.02), 4.1% (P < 0.001), and 6.1% (P < 0.001) at 6, 12, and 24 months, respectively, with similar findings in an on-treatment analysis. CONCLUSION: Treatment with sacubitril/valsartan was associated with more loop diuretic dose reductions and fewer dose increases compared with enalapril, suggesting that treatment with sacubitril/valsartan may reduce the requirement for loop diuretics relative to enalapril in patients with heart failure with reduced ejection fraction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".