Immunogenicity of COVID-19 vaccines in solid organ transplant recipients: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Solid organ transplant (SOT) recipients are at increased risks of morbidity and mortality associated with COVID-19. OBJECTIVES: This study aimed to evaluate the immunogenicity of COVID-19 vaccines in SOT recipients. DATA SOURCES: Electronic databases were searched for eligible reports published from 1 December 2019 to 31 May 2022. STUDY ELIGIBILITY CRITERIA: We included reports evaluating the humoral immune response (HIR) or cellular immune response rate in SOT recipients after the administration of COVID-19 vaccines. PARTICIPANTS: SOT recipients who received COVID-19 vaccines. ASSESSMENT OF RISK OF BIAS: We used the Newcastle-Ottawa scale to assess bias in case-control and cohort studies. For randomised-controlled trials, the Jadad Scale was used. METHODS: We used a random-effects model to calculate the pooled rates of immune response with 95% CI. We used a risk ratio (RR) with 95% CI for a comparison of immune responses between SOT and healthy controls. RESULTS: A total of 91 reports involving 11 886 transplant recipients (lung: 655; heart: 539; liver: 1946; and kidney: 8746) and 2125 healthy controls revealed pooled HIR rates after the 1st, 2nd, and 3rd COVID-19 vaccine doses in SOT recipients were 9.5% (95% CI, 7-11.9%), 43.6% (95% CI, 39.3-47.8%) and 55.1% (95% CI, 44.7-65.6%), respectively. For specific organs, the HIR rates were still low after 1st vaccine dose (lung: 4.4%; kidney: 9.4%; heart: 13.2%; liver: 29.5%) and 2nd vaccine dose (lung: 28.4%; kidney: 37.6%; heart: 50.3%; liver: 64.5%). CONCLUSIONS: A booster vaccination enhances the immunogenicity of COVID-19 vaccines in SOT; however, a significant share of the recipients still has not built a detectable HIR after receiving the 3rd dose. This finding calls for alternative approaches, including the use of monoclonal antibodies. In addition, lung transplant recipients need urgent booster vaccination to improve the immune response.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".