Donor-Specific Antibodies and Primary Graft Failure in Allogeneic Hematopoietic Stem Cell Transplantation: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
With the increasing number of non-matched donor hematopoietic stem cell transplantations (HSCTs) has come increasing evidence regarding factors affecting graft outcomes. One factor affecting graft outcomes currently being evaluated is anti-HLA donor-specific antibodies (DSAs). In this, we analyzed the clinical relevance of anti-HLA DSAs in patients who have undergone HSCT at a population level by conducting a systematic review of existing literature. A comprehensive search was conducted through PubMed, Embase, the Cochrane library, and Web of Science from inception to January 1, 2021. A meta-analysis was performed of the association between anti-HLA DSAs and primary graft failure (PGF) with further subgroup analyses. The search was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 920 eligible citations were identified, out of which 15 studies were included in the final meta-analyses after application of rigorous selection criteria and independent review. A total of 2436 patients were included in these 15 studies. Patients with anti-HLA DSAs prior to undergoing HSCT had a 7.47-fold increased risk of PGF failure compared with patients without anti-HLA DSAs (odds ratio, 7.47; 95% confidence interval, 4.54 to 12.28, P < .001; I2= 28.91%, P = .1315). In subgroup and meta-regression analyses, area, Newcastle Ottawa Scale score, mean fluorescence intensity cutoff, primary disease, HSCT type, graft source, and pretransplantation desensitization did not affect the impact of anti-HLA DSAs on PGF. There also was no significant difference in impact between HLA class I and II on PGF. We conclude that the prior presence of anti-HLA DSAs has a negative impact on graft outcomes in recipients of haploidentical and umbilical cord blood HSCT.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.044 |
| Bibliometrics | 0.007 | 0.008 |
| 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".