Safety and Efficacy of Influenza Vaccination in Patients Receiving Immune Checkpoint Inhibitors. Systematic Review with Meta-Analysis
Bibliographic record
Abstract
The potential increased risk of immune-related adverse events (irAEs) post-influenza vaccine is a concern in patients receiving immune checkpoint inhibitors (ICI). We conducted a systematic review with meta-analysis of studies reporting the effects of influenza vaccination in patients with cancer during ICI treatment. We searched five electronic databases until 01/2022. Two authors independently selected studies, appraised their quality, and collected data. The primary outcome was the determination of pooled irAE rates. Secondary outcomes included determination of immunogenicity and influenza infection rates and cancer-related outcomes. Nineteen studies (26 publications, n = 4705) were included; 89.5% were observational. Vaccinated patients reported slighter lower rates of irAEs compared to unvaccinated patients (32% versus 41%, respectively). Seroprotection for influenza type A was 78%-79%, and for type B was 75%. Influenza and irAE-related death rates were similar between groups. The pooled proportion of participants reporting a laboratory-confirmed infection was 2% (95% CI 0% to 6%), and influenza-like illness was 14% (95% CI 2% to 32%). No differences were reported on the rates of laboratory-confirmed infection between vaccinated and unvaccinated patients. Longer progression-free and overall survival was also observed in vaccinated compared with unvaccinated patients. Current evidence suggests that influenza vaccination is safe in patients receiving ICIs, does not increase the risk of irAEs, and may improve survival.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".