Efficacy and Tolerability of Adding an Angiotensin Receptor Blocker in Patients with Heart Failure Already Receiving an Angiotensin-Converting Inhibitor Plus Aldosterone Antagonist, With or Without a Beta Blocker. Findings from the Candesartan in Heart Failure: Assessment of Reduction in Mortality and Morbidity (CHARM)-Added Trial
Bibliographic record
Abstract
BACKGROUND: The efficacy and safety of adding an angiotensin receptor blocker (ARB) in heart failure (HF) patients already taking an angiotensin-converting enzyme-inhibitor (ACE-I) plus an aldosterone antagonist is uncertain (especially if taking a beta blocker as well). The CHARM-Added trial describes the largest experience of using multiple inhibitors of the renin-angiotensin-aldosterone system (RAAS) together. METHODS AND RESULTS: 2548 HF patients, taking an ACE-I (936 no spironolactone/no beta blocker; 1175 no spironolactone/beta blocker; 199 spironolactone/no beta blocker; 238 sprionolactone/beta blocker), were randomized to placebo or candesartan and followed for 41 months (median). The primary outcome was cardiovascular death or HF hospitalization. In patients taking both a beta blocker and spironolactone (in addition to an ACE-I) at baseline, the candesartan:placebo hazard ratio was 0.85(95% CI 0.56, 1.29), compared to 0.85(95% CI 0.75, 0.96) in all randomized patients (interaction p value 0.49). The relative risk of discontinuation of candesartan (compared to placebo) because of hypotension, increased serum creatinine or hyperkalemia was not increased in patients taking spironolactone at baseline. CONCLUSIONS: An ARB may provide added benefit, at acceptable risk, in HF patients already taking spironolactone as well as an ACE-I and beta blocker. These findings must be confirmed in a prospective randomized trial before this approach can be recommended, routinely.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".