IgA Vasculitis Following COVID-19 Vaccination: A French Multicenter Case Series Including 12 Patients
Bibliographic record
Abstract
OBJECTIVE: The worldwide coronavirus disease 2019 (COVID-19) vaccination campaign triggered several autoimmune diseases. We hereby aimed to describe IgA vasculitis (IgAV) following COVID-19 vaccination. METHODS: We conducted a national, multicenter, retrospective study in France of new-onset adult IgAV diagnosis following COVID-19 vaccination. RESULTS: In total, 12 patients with new-onset IgAV were included. Of these, 5 (41.7%) were women, and the median age was 52.5 (IQR 30.75-60.5) years. Of the 12 patients, 10 had received an mRNA vaccine and 2 had received a viral vector vaccine. The median time from vaccination to onset of symptoms was 11.5 (IQR 4.25-21.25) days. Vasculitis occurred after the first vaccine dose in most patients (n = 8). All patients had skin involvement, with skin necrosis in 4 patients. In total, 7 patients had joint involvement and 2 had arthritis. A total of 4 patients had nonsevere gastrointestinal involvement and 2 had nonsevere renal involvement. The median C-reactive protein level was 26 (IQR 10-66.75) mg/L, the median creatininemia level was 72 (IQR 65-81) μmol/L, and 1 patient had an estimated glomerular filtration rate of less than 60 mL/min at management. All patients received treatment, including 9 patients (75%) who received glucocorticoids. In total, 5 patients received a vaccine dose after developing IgAV, 1 of whom experienced a minor cutaneous relapse. CONCLUSION: The baseline presentation of IgAV following COVID-19 vaccination was mild to moderate, and outcomes were favorable. Thus, a complete COVID-19 vaccination regimen should be completed in this population. Of note, a fortuitous link cannot be ruled out, requiring a worldwide pharmacovigilance search to confirm these findings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".