Identification of the Top 15 Drugs Associated With Anaphylaxis: A Pharmacovigilance Study
Bibliographic record
Abstract
ABSTRACT Background Drug‐associated anaphylaxis is a common condition with significant risks if not promptly addressed. Yet, systematic research on the distribution of associated drugs and risk comparison across drug classes is limited. This study aims to identify frequently reported drugs and evaluate the strength of their signal detections with drug‐associated anaphylaxis. Methods This study employed a global pharmacovigilance database to identify reports of drug‐associated anaphylaxis. Reports classified as anaphylaxis were analysed using the drug record number used in global pharmacovigilance database, leading to the identification of 15 frequently associated drugs. A disproportionality analysis was conducted to estimate signal detections between these selected drugs and anaphylaxis, utilising two metrics: the information component (IC) with a threshold of IC 0.25 and the reporting odds ratio (ROR) with 95% confidence intervals (CI). To account for the acute onset of anaphylaxis, a sensitivity analysis focused on reports with a time to onset of less than a day. Results We identified 15 drugs frequently associated with anaphylaxis, with diclofenac recording the highest number of reports at 34,413. The drug indicating the strongest signal detection with anaphylaxis was cefuroxime (ROR, 40.89 [95% CI, 40.18–41.61]; IC, 5.14 [IC 0.25 , 5.11]), followed by levofloxacin, ibuprofen, COVID‐19 vaccine, ceftriaxone, lidocaine, omalizumab, cefuroxime, benzylpenicillin, clindamycin, amoxicillin/clavulanate, cefazolin, ciprofloxacin, metronidazole, and paclitaxel. Sensitivity analysis indicated that the signal detection between the COVID‐19 vaccine and anaphylaxis was stronger than in the primary analysis (ROR, 2.49 [95% CI, 2.45–2.53]; IC, 1.23 [IC 0.25 , 1.21]). While most drugs reported that the majority of drug‐associated anaphylaxis reports occurred within 2.5 h, omalizumab was often associated with reactions occurring after 24 h. Conclusion All drugs frequently reported in association with anaphylaxis indicated a significant signal detection, but the strength of these signal detections did not align with the number of reports. Time‐to‐onset analysis showed distinct patterns for certain drugs, suggesting different mechanisms of anaphylaxis. Due to the limitations of spontaneous reporting databases with disproportionality analysis, our findings do not permit for causal inference.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".