Impact of Antibiotic Exposure Before Immune Checkpoint Inhibitor Treatment on Overall Survival in Older Adults With Cancer: A Population-Based Study
Bibliographic record
Abstract
PURPOSE Antibiotic exposure before immune checkpoint inhibitor (ICI) treatment can negatively affect outcomes through alteration in the gut microbiome, but large-scale evaluations are lacking. We performed a population-level retrospective cohort study to evaluate the impact of antibiotic exposure before starting ICI on overall survival (OS). PATIENT AND METHODS Patients with cancer, age 65 years or older, who initiated treatment with ICIs between June 2012 and October 2018 in Ontario, Canada, were identified using systemic therapy administration data. The cohort was deterministically linked to other health care databases to obtain covariates and antibiotic prescription claim data at both 1 year and 60 days before ICI therapy. Multivariable Cox models evaluated the association between exposure and OS. RESULTS Among the 2,737 patients with cancer who received ICIs, 59% and 19% of patients received antibiotics 1 year and 60 days before ICI therapy, respectively. Median OS was 306 days. Any antibiotic exposure within 1 year before ICI was associated with worse OS (adjusted hazard ratio [aHR], 1.12; 95% CI, 1.12 to 1.23; P = .03). In antibiotic class analysis, exposure to fluoroquinolones within 1 year (aHR, 1.26; 95% CI, 1.13 to 1.40; P < .001) or 60 days before ICI (aHR, 1.20; 95% CI, 0.99 to 1.45; P = .06) was associated with worse OS, with a dose effect seen on the basis of total weeks of exposure over 1 year (aHR, 1.07 per week; 95% CI, 1.03 to 1.11; P < .001) and 60 days (aHR, 1.12 per week; 95% CI, 1.03 to 1.23; P = .01). CONCLUSION In this population-level study, exposure to antibiotics and specifically fluoroquinolones before ICI therapy was observed to be associated with worse OS among older adults with cancer. Interventions aimed at altering the gut microbiome to boost immunogenicity may help improve outcomes for patients receiving ICIs with prior antibiotic exposure. [Media: see text]
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".