Loop Diuretic Dose Adjustments after a Hospitalization for Heart Failure: Insights From ASCEND-HF
Bibliographic record
Abstract
AIMS: Loop diuretics are a cornerstone of heart failure (HF) treatment, but data regarding diuretic dose adjustments after a HF hospitalization and the association with subsequent outcomes are limited. This study was therefore conducted to determine these factors. METHODS AND RESULTS: We analysed data from 6119 patients enrolled in ASCEND-HF, examining the association between loop diuretic use at the time of discharge, compared with admission, and the composite outcome of 30-day HF re-hospitalization or all-cause mortality. The majority of patients, 3921 (64%), were taking a loop diuretic on admission. At discharge, 3411 (56%) patients were prescribed higher doses compared with admission, including 1867 (31%) initiating daily outpatient diuretics; 1912 (31%) had no dose change and 795 (13%) were prescribed lower doses compared with admission. Initiation of an oral loop diuretic at discharge was independently associated with better 30-day outcomes compared with no dose change [adjusted odds ratio (OR) 0.51, 95% confidence interval (CI) 0.37-0.68]. However, for patients that were already established on a loop diuretic prior to admission, change in the dose at discharge was not associated with improved outcomes compared with no dose change (adjusted OR 0.92, 95% CI 0.79-1.07). CONCLUSIONS: In a large multinational clinical trial, 56% of patients hospitalized with HF were either initiated on a daily loop diuretic at discharge or discharged on higher doses compared with admission. In patients established on diuretics prior to hospitalization, we found no association between changes to chronic doses at discharge and improved outcomes, whereas initiation of loop diuretic therapy was associated with better outcomes compared with no dose change.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".