A disproportionality analysis on proprotein convertase subtilisin/kexin type 9 inhibitors and hypersensitivity and anaphylaxis
Bibliographic record
Abstract
Recent case reports linked use of the lipid-lowering class of proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors to severe hypersensitivity reactions. Therefore, our pharmacovigilance study assessed the association between reporting of PCSK9 inhibitors and hypersensitivity or anaphylaxis. We analyzed the US Food and Drug Administration Adverse Event Reporting System (FAERS) extracting spontaneous reports from 2015 to 2023. We calculated reporting odds ratios (ROR), proportional reporting ratios, and information components (IC) as measures of disproportionate reporting of hypersensitivity or anaphylaxis with PCSK9 inhibitors overall and with specific compounds (alirocumab, evolocumab) using the entire FAERS as comparator. In sensitivity analyses, we adjusted for demographic characteristics, used statins as active comparator, and applied alternate outcome definitions. Among all reports in FAERS during the study period involving PCSK9 inhibitors, we identified 12,591 cases of hypersensitivity and 17,214 cases of anaphylaxis. Across disproportionality analysis methods, we did not observe an association between the reporting of PCSK9 inhibitors or evolocumab specifically and the outcomes. However, there were associations with alirocumab and hypersensitivity (ROR, 1.14; 95% confidence interval [CI], 1.11-1.18 / IC, 0.19; 95% credible interval [CrI], 0.14-0.24) and with alirocumab and anaphylaxis (ROR, 1.30; 95% CI, 1.26-1.33; IC, 0.37; 95% CrI, 0.33-0.42). Sensitivity analyses corroborated the lack of an association with PCSK9 inhibitors or with evolocumab and were inconsistent regarding alirocumab. Our large pharmacovigilance study showed no signal of disproportionate reporting for hypersensitivity or anaphylaxis with PCSK9 inhibitors overall. The inconsistencies in alirocumab related findings argue against a compound specific signal.
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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 | 0.060 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.014 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".