A Population-Based Analysis of the Risk of Glomerular Disease Relapse after COVID-19 Vaccination
Bibliographic record
Abstract
BACKGROUND: Although case reports have described relapses of glomerular disease after COVID-19 vaccination, evidence of a true association is lacking. In this population-level analysis, we sought to determine relative and absolute risks of glomerular disease relapse after COVID-19 vaccination. METHODS: In this retrospective population-level cohort study, we used a centralized clinical and pathology registry (2000-2020) to identify 1105 adult patients in British Columbia, Canada, with biopsy-proven glomerular disease that was stable on December 14, 2020 (when COVID-19 vaccines first became available). The primary outcome was disease relapse, on the basis of changes in kidney function, proteinuria, or both. Vaccination was modeled as a 30-day time-varying exposure in extended Cox regression models, stratified on disease type. RESULTS: During 281 days of follow-up, 134 (12.1%) patients experienced a relapse. Although a first vaccine dose was not associated with relapse risk (hazard ratio [HR]=0.67; 95% confidence interval [95% CI], 0.33 to 1.36), exposure to a second or third dose was associated with a two-fold risk of relapse (HR=2.23; 95% CI, 1.06 to 4.71). The pattern of relative risk was similar across glomerular diseases. The absolute increase in 30-day relapse risk associated with a second or third vaccine dose varied from 1%-2% in ANCA-related glomerulonephritis, minimal change disease, membranous nephropathy, or FSGS to 3%-5% in IgA nephropathy or lupus nephritis. Among 24 patients experiencing a vaccine-associated relapse, 4 (17%) had a change in immunosuppression, and none required a biopsy. CONCLUSIONS: In a population-level cohort of patients with glomerular disease, a second or third dose of COVID-19 vaccine was associated with higher relative risk but low absolute increased risk of relapse.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".