SARS-CoV-2 Vaccine Non-response among Hematopoietic Stem Cell Transplant Patients: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background Hematopoietic stem cell transplant (HSCT) recipients are uniquely vulnerable to adverse outcomes of SARS-CoV-2 infection. Small, mostly observational studies suggest that some HSCT recipients may not generate protective antibody responses following SARS-CoV-2 vaccination. We conducted a meta-analysis to estimate the prevalence and identify predictors of vaccine non-response. Methods A comprehensive search of electronic databases, including MEDLINE (Ovid), Embase (Elsevier), Web of Science Core Collection (Clarivate), the Cochrane Central Register of Controlled Trials (Wiley), and the Cochrane COVID-19 Study Register was conducted on January 20, 2023. We defined a non-response as not achieving a seroconversion (positive anti-S IgG titer) after receiving at least two vaccine doses, indicated by study-specific assay cut-off value. Only studies assessing COVID-19 vaccine induced antibody (anti-S IgG) responses in adult (≥ 18 years) HSCT recipients were included. With 95% confidence intervals (CI) across all studies, a random-effects model was used to combine the pooled effect sizes. Quality and risk of bias assessment were determined using the Newcastle-Ottawa scale and ROBINS-I tool, respectively. Results Out of 903 unique articles identified and 439 screened, 45 were included in this analysis comprising 4568 participants. Pooled absent sero-conversion was 20% (95% CI: 17% − 24%) with significant heterogeneity (I2 = 95.10%) among included studies (1 clinical trial, 1 cross-sectional study, 1 case-control study, and 42 observational cohort studies). Subgroup analyses showed no difference between autologous [0.21 (95%CI 0.12–0.31)] and allogeneic [0.20 (95%CI 0.17–0.24)] transplant recipients. Identified predictors of non-response included time interval between transplantation and vaccination (< 12 months), concurrent anti-CD20 therapy, and specific treatments (high-dose glucocorticosteroid, calcineurin inhibitor, and anti-thymocyte globulin) for graft versus host disease. No publication bias was observed but the Galbraith’s plot asymmetry showed evidence of small-study effects. Conclusion Our findings emphasize the significant prevalence of non-responsiveness to SARS-CoV-2 vaccination in HSCT recipients and underscore need for close monitoring and aggressive risk factor management in this immunocompromised population.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.040 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".