Cardiovascular events and safety outcomes associated with remdesivir using a World Health Organization international pharmacovigilance database
Bibliographic record
Abstract
On October 2020, the US Food and Drug Administration (FDA) approved remdesivir as the first drug for the treatment of coronavirus disease 2019 (COVID-19), increasing remdesivir prescriptions worldwide. However, potential cardiovascular (CV) toxicities associated with remdesivir remain unknown. We aimed to characterize the CV adverse drug reactions (ADRs) associated with remdesivir using VigiBase, an individual case safety report database of the World Health Organization (WHO). Disproportionality analyses of CV-ADRs associated with remdesivir were performed using reported odds ratios and information components. We conducted in vitro experiments using cardiomyocytes derived from human pluripotent stem cell cardiomyocytes (hPSC-CMs) to confirm cardiotoxicity of remdesivir. To distinguish drug-induced CV-ADRs from COVID-19 effects, we restricted analyses to patients with COVID-19 and found that, after adjusting for multiple confounders, cardiac arrest (adjusted odds ratio [aOR]: 1.88, 95% confidence interval [CI]: 1.08-3.29), bradycardia (aOR: 2.09, 95% CI: 1.24-3.53), and hypotension (aOR: 1.67, 95% CI: 1.03-2.73) were associated with remdesivir. In vitro data demonstrated that remdesivir reduced the cell viability of hPSC-CMs in time- and dose-dependent manners. Physicians should be aware of potential CV consequences following remdesivir use and implement adequate CV monitoring to maintain a tolerable safety margin.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".