Real-world Outcomes With Rituximab-based Therapy for Posttransplant Lymphoproliferative Disease Arising After Solid Organ Transplant
Bibliographic record
Abstract
BACKGROUND: Optimal upfront therapy for posttransplant lymphoproliferative disease (PTLD) arising after solid organ transplant remains contentious. Rituximab monotherapy (R-Mono) in unselected patients has shown a lack of durable remissions. Cyclophosphamide, doxorubicin, vincristine, and prednisolone (CHOP)-based chemotherapy confers improved response rates, although concerns exist about toxicity. METHODS: This multicenter retrospective study reports outcomes for adults with biopsy-proven B-cell PTLD treated initially with R-Mono or Rituximab plus CHOP (R-CHOP). Selection of therapy was made according to physician preference. RESULTS: Among 101 patients, 41 received R-Mono and 60 had R-CHOP. Most (93%) had undergone renal or liver transplantation. R-CHOP showed a trend toward improved complete (53% versus 71%; P = 0.066) and overall (75% versus 90%; P = 0.054) response rates. In the R-Mono group, 13 of 41 (32%) subsequently received chemotherapy, while 25 of 41 (61%) remained progression-free without further therapy. With median follow-up of 47 months, overall survival (OS) was similar for R-Mono and R-CHOP, with 3-year OS of 71% and 63%, respectively (P = 0.722). Non-PTLD mortality was 3 of 41 (7%) and 4 of 60 (7%) within 12 months of R-Mono or R-CHOP, respectively. The International Prognostic Index was statistically significant, with low- (0-2 points) and high-risk (≥3 points) groups exhibiting 3-year OS of 78% and 54%, respectively (P = 0.0003). In low-risk PTLD, outcomes were similar between therapies. However, in high-risk disease R-Mono conferred an inferior complete response rate (21% versus 68%; P = 0.006), albeit with no impact on survival. CONCLUSIONS: Our data support R-Mono as initial therapy for PTLD arising after renal or liver transplantation. However, upfront R-CHOP may benefit selected high-risk cases in whom rapid attainment of response is desirable.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".