A pharmacovigilance study to quantify the strength of association between the combination of antimalarial drugs and azithromycin and cardiac arrhythmias: implications for the treatment of COVID-19.
Bibliographic record
Abstract
Background: Hydroxychloroquine, an antimalarial drug, combined with azithromycin has been considered a potential treatment for COVID-19. However, these drugs may cause electrocardiogram QT prolongation (QTp) and torsade de Pointes (TdP). We examined potential safety signals for these cardiac arrhythmias.Methods: Using the OpenVigil 2.1 MedDRA platform, we mined data from the U.S. Food and Drug Administration’s Adverse Event Reporting System (FAERS) from December 2019 to June 2020. We extracted individual case safety reports based on exposures of seven antimalarial drugs, azithromycin, and combinations. All other drugs in FAERS served as controls. Events of interest included QTp and TdP, with associations between drug exposures and events expressed as adjusted Reporting-Odds-Ratios (aRORs) and confidence intervals. The lower end of aROR 95% confidence interval >1 was used as the statistically significant signal detection threshold.Results: QTp safety signals were found for hydroxychloroquine[aROR:11.70 (10.40–13.16)], chloroquine[aROR:18.97 (11.30–31.87)], quinine[aROR:16.66 (10.18–27.25)], atovaquone[aROR:6.91 (4.14–11.56)], azithromycin alone [aROR:28.02 (22.87–34.32)] and hydroxychloroquine + azithromycin [aROR:75.23 (51.15–110.66)]. TdP safety signals were found for hydroxychloroquine [aROR: 5.62 (4.94–6.38)], chloroquine[aROR:49.37 (30.63–79.58)], and hydroxychloroquine + azithromycin[aROR:33.09 (21.22–51.61)].Conclusion: Hydroxychloroquine/chloroquine and/or azithromycin was associated with QTp/TdP safety signals and their use should be monitored carefully.
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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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".