Survival Benefit of Aspirin in Patients With Congestive Heart Failure: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: There is no clear consensus on the use of aspirin (ASA) in patients with congestive heart failure (CHF) due to its reported interaction with other cardio-prudent medications. The aim was to evaluate the effect of ASA on all-cause mortality and the frequency of hospitalization for heart failure in patients with CHF using meta-analysis, as well as to study the potential variables interacting with this effect. METHODS: Eligible studies were identified via a PubMed search, the "related article" feature and a manual search of references. Studies were included if they had a study population with CHF of any etiology, compared ASA to no ASA or placebo, and reported one or both of the following outcomes: 1) all-cause mortality and 2) the frequency of hospitalization for heart failure. Data were extracted and verified. We used the inverse variance method in a random-effects model to combine effect sizes. RESULTS: A total of 14 studies with a combined study population of 64,550 patients were included in the final analysis. All-cause mortality was found to be significantly lower in patients who were taking ASA (P = 0.003). When examining the use of ASA, no significant difference was found in the frequency of hospitalization for heart failure. ASA use was demonstrated to be more beneficial against mortality in studies with a larger percentage of patients on nitrates (P = 0.008) and oral anticoagulants (P = 0.04). A significantly lower rate of hospitalization for heart failure was observed in patients who used oral anticoagulants and ASA concurrently (P = 0.02). CONCLUSIONS: ASA may have beneficial effects on mortality in patients with heart failure of all etiologies.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.055 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".