Prior antibiotic exposure is associated with worse outcomes in adults with COVID-19
Bibliographic record
Abstract
BACKGROUND: Antibiotic-induced perturbations of the gut microbiome impair immunologic responses but whether they influence disease severity is unknown. The COVID-19 pandemic provided a unique opportunity to explore this question given widespread testing for SARS-CoV-2 infections. OBJECTIVE: To determine whether prior antibiotic exposure was associated with outcomes in patients with COVID-19. METHODS: Retrospective cohort study of all community-dwelling adults in Alberta, Canada with COVID-19 between March 2020 and June 2023. Subjects with antibiotic dispensations in the prior 3 months were compared (using multivariable logistic regression and propensity score (PS)-matching) to those without antibiotic exposure for differences in 30-day outcomes. RESULTS: Of 445,646 adults with COVID-19, 49,581 (11.1%) were exposed to at least one antibiotic course in the prior 3 months. Those exposed to antibiotics were more likely to present to an emergency department (13.4% vs. 7.4%, aOR 1.52, 95%CI 1.48-1.57, PS-matched OR 1.48, 1.42-1.54), be hospitalised (5.8% vs. 2.8%, aOR 1.40,1.33-1.46, PS-matched OR 1.37, 1.29-1.45), or die (1.7% vs. 0.6%, aOR 1.28, 1.18-1.40, PS-matched OR 1.27, 1.14-1.42) than patients without prior antibiotic exposure. The associations were similar whether the antibiotic prescriptions were appropriate or not or whether antibiotic exposure periods were 6 weeks, 6 months, or 12 months prior to the positive RT-PCR test. The associations were stronger in those individuals with the highest tertile of antibiotic exposure, or those exposed to broad-spectrum antibiotics, or younger patients. CONCLUSION: Prior antibiotic exposure is associated with worsened disease severity in patients infected with SARS-CoV-2. These findings support efforts to reduce antibiotic use.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".