Low incidence of de novo HLA antibodies after COVID‐19 vaccination: A cohort study of patients awaiting kidney transplantation
Bibliographic record
Abstract
BACKGROUND: Antibodies against human leukocyte antigen (anti-HLA Abs) are associated with an increased risk of allograft loss. Herein, we report the prospective follow-up for anti-HLA Abs formation in 103 patients with end-stage kidney disease on the waiting list for transplantation who underwent COVID-19 vaccination. PATIENTS AND METHODS: Sera were tested before and after vaccination using Luminex technology. The cohort comprised of 62 males and 41 females with a mean age of 56 ± 14 years. The patients received BNT162b2 (80.4%), mRNA-1273 (18.5%), AZD1222 (0.40%), or ChAdOx1-S (0.80%) vaccine. Patients were tested before and within 119 ± 50, 95 ± 46 and 25 ± 26 days after the first, second, and third dose of the vaccine, respectively. RESULTS: No significant change in calculated panel reactive antibody (cPRA) after vaccination was seen. Although 98.1% of patients had no change in anti-HLA Abs profile or cPRA after vaccination, two patients (1.9%) developed de novo anti-HLA Abs against class I or II HLA antigens. In those two patients, the cPRA changed from 0% and 63% at baseline to 9% and 90% after vaccination, respectively. Both patients received the BNT162b2 mRNA-based vaccine. The earliest detected anti-HLA Abs was 18 days after the first dose. CONCLUSION: In rare cases, new anti-HLA antibodies were observed after COVID-19 vaccination, with potential implications for transplantation. The low incidence of this phenomenon is outweighed by the clinical benefits of vaccination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".