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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".