Impact of antibiotic (ATB) exposure prior to immune checkpoint inhibitor (ICI) treatment on overall survival (OS): A population-based study.
Bibliographic record
Abstract
303 Background: ICIs are a common therapeutic option for many solid tumors. While prior studies have shown that ATB exposure may negatively impact ICI outcomes through gut microbiome changes, many were small studies with heterogeneity in ATB classes and exposure windows. Here, we performed a population level retrospective cohort study to evaluate the impact of ATB exposure prior to ICI on OS. Methods: We used administrative data to identify a cohort of cancer patients > 65 years of age receiving ICIs from June 2012 to October 2018 in Ontario, Canada and deterministically linked with databases to obtain socio-demographic and clinical co-variates and ATB prescription claims. Multivariable cox-proportional hazard models evaluated the impact of ATB exposure both within 1 year and 60 days prior to starting ICI on OS, adjusted for age, gender, body mass index, comorbidities, autoimmune history, hospitalization in the past year and treatment facility level at start of ICI therapy. Results: Among 2737 patients, median age 73; 43% received Nivolumab, 41% Pembrolizumab and 13% Ipilimumab; 53% were lung cancer, 34% melanoma. Median ATB treatment duration for patients receiving ATB within 1 year (59%) and 60 days (19%) prior to ICI were 14 days (SD = 32) and 9 days (SD = 13) respectively. Median OS estimate was 306 days. Any ATB exposure within 1 year prior to ICI was associated with worse OS (aHR = 1.12 95% CI [1.12-1.23] p = 0.03). A non significant dose effect was seen based on weeks of ATB exposure 1 year prior to ICI (aHR = 1.01 per week [1.00-1.02] p = 0.10). ATB class analysis identified fluoroquinolone exposure within 1 year (aHR = 1.26 [1.13-1.40] p < 0.001) and 60 days before ICI (aHR = 1.20 [0.99-1.45] p = 0.06) were associated with worse OS; with a dose effect based on total weeks of exposure over 1 year (aHR = 1.07 per week [1.03-1.11] p < 0.001) and 60 days (aHR = 1.12 per week [1.03-1.23] p = 0.01). Subgroup analysis showed similar results for patients receiving anti-PD1 ICIs, where patients exposed to fluoroquinolone both 1 year (aHR = 1.28 [1.15-1.44] p < 0.001) and 60 days (aHR = 1.19 [0.98-1.44] p = 0.08) before ICIs had poorer OS with dose effects observed based on weeks of fluoroquinolone exposure. Similarly, subgroup analyses based on disease site identified that lung cancer patients exposed to fluoroquinolones 1 year before starting ICIs (aHR = 1.22 [1.06-1.39] p = 0.005) and melanoma patients exposed to fluoroquinolones 60 days before starting ICIs (aHR = 1.66 [1.12-2.47] p = 0.01) had poorer OS. Conclusions: Exposure to ATBs and specifically fluoroquinolones prior to ICI therapy is associated with worse OS. Interventions aimed at altering the gut microbiome may be required to help improve outcomes for patients on ICIs with prior ATB exposure.
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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 | 0.003 |
| 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) | 0.002 | 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".