Effect of Influenza Vaccination in Patients with Cardiovascular Disease: An Updated Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background: Influenza is a major cause of morbidity and mortality in patients with cardiovascular disease (CVD). The aim of this updated systematic review and meta-analysis was to evaluate the effect of influenza vaccination (IV) on morbidity and morbidity in adult patients with CVD. Methods: We conducted a systematic review and meta-analysis (PubMed, Cochrane Library, International Clinical Trials Registry Platform, and manual search of conference presentations) of randomized clinical trials published up to April 2022 analyzing whether IV reduced all-cause mortality in adult patients with CVD, including heart failure (HF) and coronary artery disease (CAD), compared with patients who were not vaccinated. Results: A total of six clinical trials comprising 9316 patients were analyzed. Five trials included CAD patients, and one trial included HF patients. Mean follow-up was 16 ± 9.7 months. Influenza vaccine was associated with a reduction of mortality compared to controls: relative risk (RR) 0.67, 95% confidence interval (95% CI), 0.47-0.95; p = 0.03; I2 = 53%, and with reduction of cardiovascular death compared to controls: RR 0.64; 95% CI 0.44-0.94; p = 0.02; I2 = 54%. There was a non-statistically significant reduction in myocardial infarction compared to control: RR 0.82, 95% CI 0.60-1.12; p = 0.57; I2 = 0%. Conclusion: In this meta-analysis of six randomized controlled clinical trials, IV was associated with a 33% and 36% relative risk reduction of all-cause mortality and cardiovascular death, respectively, in patients with CVD. We sought to promote consensus about the persistent benefits of influenza vaccination in patients with CVD by including two new clinical trials in CAD and HF, confirming the association of vaccination with risk reduction in subjects with CVD.
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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 | 0.023 | 0.052 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.053 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".