Use, tolerability and compliance of spironolactone in the treatment of heart failure
Bibliographic record
Abstract
BACKGROUND: Risk of morbidity and mortality in patients with severe heart failure (HF) is reduced by blockade of aldosterone receptors with spironolactone. However, benefits of spironolactone are potentially limited by treatment compliance and adverse events profile. The aim of this study was to estimate use of spironolactone by patients with HF, incidence of key adverse events, and patient compliance. METHODS: This study was performed using data from the Quebec provincial medical and drug plans (Régie de l'Assurance Maladie du Québec, RAMQ) for patients who had a diagnosis of HF. Relative incidence of gynecomastia and hyperkalemia was estimated for users and non-users of spironolactone. Treatment adherence was estimated for users of spironolactone and compared to adherence with angiotensin converting enzyme (ACE) inhibitors, beta-blockers (β-blockers), and angiotensin receptor blockers (ARBs). RESULTS: RAMQ data were obtained for a total of 82,018 patients with a diagnosis of HF. Of these patients, 59.9% used an ACE inhibitor, 59.5% used a beta-blocker, 28.4% used an ARB, and 15.1% (n = 12,344) used spironolactone. Despite underestimation due to limitation of the database, the documented incidence of hyperkalemia (3.3% versus 1.4%) and gynecomastia (1.8% versus 0.7%) was significantly higher in spironolactone users than non-users (p < 0.001). Treatment compliance was significantly lower with spironolactone compared to ACE inhibitors, β-blockers, and ARBs (45.6% versus 56.1%, 59.7%, and 57.0%, respectively; p < 0.001). Persistence to treatment over a one-year period was also lower with spironolactone compared to ACE inhibitors, β-blockers, and ARBs (50.7% versus 64.5%, 70.4%, and 66.3%, respectively; p < 0.001). CONCLUSION: Use of spironolactone is associated with an incidence of adverse events, which may have an impact on treatment compliance.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".