The role of pathogen‐reduced platelet transfusions on HLA alloimmunization in hemato‐oncological patients
Bibliographic record
Abstract
BACKGROUND: Platelet transfusions can induce alloimmunization against HLA antigens. The use of pathogen-reduced platelet concentrates (PCs) was suggested to reduce HLA alloimmunization and concomitant transfusion refractoriness. METHODS: This study investigated HLA alloimmunization in available samples from 448 hemato-oncological patients who were randomized for the Pathogen Reduction Evaluation and Predictive Analytical Rating Score (PREPAReS) trial to receive either untreated or pathogen-reduced PCs (Mirasol, Terumo BCT Inc.). Anti-HLA Class I and II antibodies were determined before the first platelet transfusion and weekly thereafter using multiplex assay with standard cutoffs to detect low- as well as high-level antibodies. RESULTS: When using the lower cutoff, in patients who were antibody negative at enrollment, 5.4% (n = 12) developed anti-HLA Class I antibodies after receiving untreated PCs, while this was significantly higher in patients receiving pathogen-reduced PCs, 12.8% (n = 29; p = 0.009, intention-to-treat [ITT] analysis). A similar but nonsignificant trend was observed in the per-protocol (PP) analysis (5.4% vs. 10.1%; p = 0.15). HLA class II antibody formation was similar between both types of PCs in the ITT analysis, while the PP analysis showed a trend toward lower immunization after receiving pathogen-reduced PCs. Multivariate analysis identified receiving pathogen-reduced platelets as an independent risk factor for HLA Class I alloimmunization (ITT: odds ratio [95% confidence interval] = 3.02 [1.42-6.51], PP: odds ratio [95% confidence interval] = 2.77 [1.00-5.40]), without affecting HLA Class II alloimmunization. When using the high cutoff value, the difference in HLA Class I alloimmunization between study arms remained significant in the ITT analysis and again was not significant in the PP analysis. CONCLUSION: Our data clearly indicate that Mirasol pathogen inactivation does not prevent HLA Class I or II alloimmunization after platelet transfusions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".