COVID-19 mRNA vaccination is associated with IgA nephropathy: an analysis of the Japanese adverse drug event report database
Bibliographic record
Abstract
Purpose: Coronavirus disease 2019 (COVID-19) mRNA vaccines are used worldwide to prevent severe symptoms of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. IgA nephropathy (IgAN) is the most common form of glomerular injury after COVID-19 vaccination; however, because of the low frequency of such events, only a few reports have been published. A large pharmacovigilance database of real-world spontaneous adverse event (AE) reports is essential for evaluating the drug-associated safety signals regarding rare AEs. Herein, we aimed to investigate the frequency of IgAN after the COVID-19 vaccination, using the Japanese Adverse Drug Event Report (JADER) database. Methods: Data on drug-associated AEs reported between April 2004 and May 2022 were obtained from the JADER database on the Pharmaceuticals and Medical Devices Agency website. To evaluate the safety signals for the targeted AEs, reporting odds ratios (RORs), information components (ICs), and their 95% confidence intervals (CIs) were calculated using two-by-two contingency tables. Results: A total of 697,885 cases were included in the analysis. Safety signals were detected for IgAN (ROR: 6.49, 95% CI: 4.38–9.61; IC: 2.27, 95% CI: 1.70–2.83). Of 30 cases for IgAN associated with COVID-19 mRNA vaccines, 16 had information available on time to onset. Of the 16 cases, 11 occurred ≤2 days after vaccination, and two occurred >28 days after vaccination. Conclusion: These results suggest that, compared with other drugs, COVID-19 vaccination is associated with a higher frequency of IgAN. Monitoring of gross hematuria following COVID-19 vaccination should be needed.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| 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) | 0.001 | 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".