The Impact of Posttransplant Lymphoproliferative Disease in High-risk Kidney Transplant Recipients: Benefits of Prevention
Bibliographic record
Abstract
Background. Posttransplant lymphoproliferative disease (PTLD) is increased in kidney transplant recipients who are Epstein-Barr virus (EBV) nonimmune (R–), particularly if the donor has prior EBV immunity (D+). PTLD is associated with very high mortality. The purpose of this study was to quantify the impact of PTLD on deceased donor EBV D+R– kidney transplant recipients. Methods. A Markov model was created to quantify remaining patient life years (LYs) and quality-adjusted LYs (QALYs) in EBV D+R– recipients compared with EBV R+ recipients. Different ages at transplant, incidence of PTLD within the first year, potential impact of therapeutic treatments to reduce PTLD, and costs were examined in a sensitivity analysis. Results. A baseline 40-y-old EBV D+R– recipient is projected to live 21.18 LYs. If there is no PTLD, the recipient lives 21.37 LYs, but if PTLD develops in the first year, the projected life remaining LYs are only 15.03. Each high-risk 40-y-old EBV D+R– recipient loses, on average, 0.192 LYs or 0.134 QALYs. LYs and QALYs gained with prevention depended on the effectiveness of the intervention, incidence of PTLD within the first year, and recipient age. Slightly fewer LYs are lost in younger recipients (age 10 y; 0.156 LF) and older recipients (age 60 y; 0.133 LY), likely due to lower case fatality rates and higher competing risks of death in the young and old, respectively. Strategies, such as rituximab, given at the time of transplant, could be cost-effective (<$50 000/QALY) if the reduction in PTLD was >50% and the cost of the intervention was <$3000. Conclusions. PTLD has a significant impact on survival in high-risk kidney transplant recipients. Preventive strategies may be cost-effective but would depend on the degree of effectiveness, safety, and cost.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".