Immunoglobulin A Vasculitis Following COVID-19: A French Multicenter Case Series
Bibliographic record
Abstract
OBJECTIVE: Immunoglobulin A vasculitis (IgAV) usually occurs following viral respiratory tract infection. In the context of the global coronavirus disease 2019 (COVID-19) pandemic, we describe a case series of patients who developed IgAV following SARS-CoV-2 infection. METHODS: This national multicenter retrospective study included patients with IgAV following SARS-CoV-2 infection from January 1, 2020, to January 1, 2022. Patients had histologically proven IgAV and reverse transcription PCR (RT-PCR)-proven SARS-CoV-2 infection. The interval between infection and vasculitis onset had to be < 4 weeks. RESULTS: We included 5 patients, 4 of whom were women with a mean age of 45 years. Four patients had paucisymptomatic infections and 1 required a 48-hour low-flow oxygen treatment. All 5 patients had purpuric skin involvement. Arthritis was observed in 2 patients, 3 had IgA glomerulonephritis, and 2 had digestive involvement. Three renal biopsies were performed and showed mesangial IgA deposits without any extracapillary proliferation. Median C-reactive protein was 180 (range 15.1-225) mg/L, median serum creatinine level was 65 (range 41-169) µmol/L, and 2 patients had a glomerular filtration rate < 60 mL/min. Four patients received first-line treatment with glucocorticoids. All patients had a favorable progression and 2 patients experienced minor skin relapses, one after COVID-19 vaccination. CONCLUSION: This series describes the emergence of IgAV closely following COVID-19; we were not able to eliminate an incidental link between these events. Their disease outcomes were favorable. In most of our patients, the SARS-CoV-2 infection was paucisymptomatic, and we recommend RT-PCR tests to look for COVID-19 in patients without any evident triggers for IgAV.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".