Rituximab Toxicity after Preemptive or Therapeutic Administration for Post-Transplant Lymphoproliferative Disorder
Bibliographic record
Abstract
Rituximab is commonly used as prevention, preemption, or therapeutically for post-transplant lymphoproliferative disorder (PTLD) after hematopoietic cell transplantation (HCT). Although it is generally assumed that rituximab toxicity (ie, infections resulting from hypogammaglobulinemia and neutropenia) is negligible in relation to mortality due to PTLD, limited evidence supports the validity of this assumption. We sought to determine the impact of rituximab on immunoglobulin levels, neutrophil count, infection density, and mortality outcomes. This study retrospectively analyzed 349 HCT recipients, 289 of whom did not receive rituximab and 60 of whom received rituximab preemptively or therapeutically at a median of 55 days post-transplantation. IgM, IgG, and IgA levels at 6 months and 12 months post-transplantation were lower in patients who received rituximab compared with those who did not (significant at P < .05 for IgM and IgA at 6 months and for IgM and IgG at 12 months). Rituximab recipients also had a higher incidence of severe neutropenia (<.5/nl) between 3 and 24 months (subhazard ratio [SHR], 2.3; P = .020). Regarding non-Epstein-Barr viral infections/PTLD, the rituximab group had a higher infection density between 3 and 24 months compared with the no-rituximab group (3.8 versus 1.6 infections per 365 days at risk; incidence rate ratio, 2.2; P < .001). The rituximab group also had a higher incidence of fatal infections (SHR, 3.1; P = .026), higher nonrelapse mortality (SHR, 2.4; P = .006), and higher overall mortality (hazard ratio, 1.7; P = .033). There were no significant between-group differences in the incidence of clinically significant graft-versus-host disease, graft failure, or relapse. Based on this study, rituximab given for PTLD is associated with substantial morbidity and mortality. Whether the benefit of preemptive rituximab outweighs the risk remains to be determined. © 2022 American Society for Transplantation and Cellular Therapy. Published by Elsevier Inc.
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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 | 0.001 |
| 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".