Influenza Vaccination and Cardiovascular Events in Patients with Ischaemic Heart Disease and Heart Failure: A Meta-Analysis
Bibliographic record
Abstract
Abstract Aim Randomized controlled trials (RCTs) enrolling patients at high cardiovascular risk have found that influenza vaccination may reduce the incidence of cardiovascular events. We performed an updated meta-analysis assessing the effect of influenza vaccination on the incidence of cardiovascular events in patients with ischaemic heart disease or heart failure. Methods and results We searched PubMed, EMBASE and other sources to identify RCTs examining the effect of influenza vaccination on the incidence of cardiovascular events assessed as efficacy outcomes in patients with ischaemic heart disease or heart failure. Eligible studies followed patients for at least one influenza season, defined as a minimum duration of 6 months. The primary endpoint was a composite of cardiovascular death, acute coronary syndrome, stent thrombosis or coronary revascularization, stroke or heart failure hospitalization. The secondary endpoints were cardiovascular death and all-cause death. Two investigators independently identified and extracted data from studies. Results were compared using hazard ratios (HRs) in both random effects and fixed effects models. We included five peer-reviewed and one non peer-reviewed RCTs for a total of 9340 patients. Five trials included patients with ischaemic heart disease (n = 4211) and one trial included patients with heart failure (n = 5129). Influenza vaccination was associated with a reduced incidence of the primary composite endpoint (random effects HR [rHR] 0.74, 95% confidence interval [CI] 0.63–0.88, p < 0.001, I2 = 52%), cardiovascular death (rHR 0.63, 95% CI 0.42–0.95, p = 0.028, I2 = 58%) and all-cause death (rHR 0.72, 95% CI 0.54–0.95, p = 0.0227, I2 = 52%). Results were similar when non peer-reviewed data were excluded. Conclusion In this meta-analysis of available RCTs in patients at high cardiovascular risk, influenza vaccination was associated with a reduced incidence of cardiovascular events, cardiovascular death and all-cause death as compared to placebo or no treatment.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.052 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".