Use of azithromycin and risk of ventricular arrhythmia
Bibliographic record
Abstract
BACKGROUND: There are conflicting findings from observational studies of the arrhythrogenic potential of azithromycin. Our aim was to quantify the association between azithromycin use and the risk of ventricular arrhythmia. METHODS: We conducted a nested case-control study within a cohort of new antibiotic users identified from a network of 7 population-based health care databases in Denmark, Germany, Italy, the Netherlands and the United Kingdom for the period 1997-2010. Up to 100 controls per case were selected and matched by age, sex and database. Recency of antibiotic use and type of drug (azithromycin was the exposure of interest) at the index date (occurrence of ventricular arrhythmia) were identified. We estimated the odds of ventricular arrhythmia associated with current azithromycin use relative to current amoxicillin use or nonuse of antibiotics (≥ 365 d without antibiotic exposure) using conditional logistic regression, adjusting for confounders. RESULTS: We identified 14 040 688 new antibiotic users who met the inclusion criteria. Ventricular arrhythmia developed in 12 874, of whom 30 were current azithromycin users. The mean age of the cases and controls was 63 years, and two-thirds were male. In the pooled data analyses across databases, azithromycin use was associated with an increased risk of ventricular arrhythmia relative to nonuse of antibiotics (adjusted odds ratio [OR] 1.97, 95% confidence interval [CI] 1.35-2.86). This increased risk disappeared when current amoxicillin use was the comparator (adjusted OR 0.90, 95% CI 0.48-1.71). Database-specific estimates and meta-analysis confirmed results from the pooled data analysis. INTERPRETATION: Current azithromycin use was associated with an increased risk of ventricular arrhythmia when compared with nonuse of antibiotics, but not when compared with current amoxicillin use. The decreased risk with an active comparator suggests significant confounding by indication.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".