Acetazolamide and Hydrochlorothiazide in Patients With Acute Decompensated Heart Failure: Insights From Recent Trials
Bibliographic record
Abstract
Acetazolamide and thiazide diuretics have been combined with loop diuretics to overcome diuretic resistance in heart failure patients. However, recent studies have assessed the upfront combination of acetazolamide and hydrochlorothiazide with loop diuretics in hospitalized patients with acute decompensated heart failure without loop diuretic resistance. We reviewed two recent randomized controlled trials on the upfront use of acetazolamide and thiazide diuretics in acute decompensated heart failure, respectively. When the two trials on acetazolamide are considered together, adding oral or intravenous acetazolamide to loop diuretics in decompensated heart failure patients resulted in increased diuresis and natriuresis. However, the effects were significantly higher in patients with serum bicarbonate ≥ 27 mmol/L and those with higher baseline glomerular filtration rate (GFR). Similarly, when the two trials on thiazide diuretics are considered together, adding hydrochlorothiazide to loop diuretics in decompensated heart failure patients resulted in increased diuresis and weight loss. However, it increases the risk of impaired renal function. When all the trials are considered together, the upfront use of acetazolamide may be helpful in carefully selected patients, including patients with underlying elevated bicarbonate levels (≥ 27 mmol/L) and those with good renal function (GFR > 50). Conversely, though the upfront use of thiazide diuretic added to intravenous furosemide improved diuretic response in acute decompensated heart failure, it causes an increased risk of worsening renal function and lack of clear evidence of reducing hospital length of stay.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".