The safety of seasonal influenza vaccination among patients prescribed immune checkpoint inhibitors: A self-controlled case series study using administrative data.
Bibliographic record
Abstract
6606 Background: Immune checkpoint inhibitor (ICI) therapy for cancer patients carries a risk of severe immune-related adverse events (IRAEs). Since vaccines are immunomodulatory, administering seasonal influenza vaccinations to individuals on ICI therapy may exacerbate this risk. However, these vaccines provide substantial benefit to this at-risk population. As severe IRAEs are rare, previous vaccine safety studies in this population have been underpowered and have shown conflicting results. Methods: We used health administration data on adult Ontarians who initiated ICI therapy and received an influenza vaccine between January 1, 2012, and December 31, 2019. We conducted a self-controlled case series study, with a pre-vaccine control period (12-weeks post-ICI initiation to 14 days before vaccine), risk period (42 days post-vaccine), and a post-vaccine control period (until observation end). Each individual contributed a maximum of 2 years of person-time. Emergency department (ED) visit(s) and/or hospitalization for any cause was used as a surrogate measure of severe IRAE frequency. We fitted a fixed effect Poisson regression model incorporating seasonality and time to estimate incidence. Results: We identified 1133 patients who received an influenza vaccine on ICI therapy. The majority were aged ≥66 years (72.7%), male (62.8%), and had lung cancer (53.9%). A quarter (25.9%) experienced an ED visit and/or hospitalization during the observation period. The rate of ED visits and/or hospitalization per person-days in the risk and control periods were similar, with an incidence rate ratio of 1.04 (95% CI: 0.75, 1.45). Subgroup and sensitivity analyses revealed similar findings. Conclusions: Receipt of a seasonal influenza vaccine was not associated with an increased incidence of ED visit or hospitalization among adults on ICI therapy. The results from this analysis suggest no significant safety concern with administering influenza vaccines to this patient population.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".