Review of Early Immune Response to SARS-CoV-2 Vaccination Among Patients With CKD
Bibliographic record
Abstract
The effects of the coronavirus disease-2019 (COVID-19) pandemic, particularly among those with chronic kidney disease (CKD), who commonly have defects in humoral and cellular immunity, and the efficacy of vaccinations against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) are uncertain. To inform public health and clinical practice, we synthesized published studies and preprints evaluating surrogate measures of immunity after SARS-CoV-2 vaccination in patients with CKD, including those receiving dialysis or with a kidney transplant. We found 35 studies (28 published, 7 preprints), with sample sizes ranging from 23 to 1140 participants and follow-up ranging from 1 week to 1 month after vaccination. Seventeen of these studies enrolled a control group. In the 22 studies of patients receiving dialysis, the development of antibodies was observed in 18% to 53% after 1 dose and in 70% to 96% after 2 doses of mRNA vaccine. In the 14 studies of transplant recipients, 3% to 59% mounted detectable humoral or cellular responses after 2 doses of mRNA vaccine. After vaccination, there were a few reported cases of relapse or de novo glomerulonephritis, and acute transplant rejection, suggesting a need for ongoing surveillance. Studies are needed to better evaluate the effectiveness of SARS-CoV-2 vaccination in these populations. Rigorous surveillance is necessary for detection of long-term adverse effects in patients with autoimmune disease and transplant recipients. For transplant recipients and those with suboptimal immune responses, alternate vaccination platforms and strategies should be considered. As additional data arise, the NephJC COVID-19 page will continue to be updated (http://www.nephjc.com/news/covid-vaccine).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".