Favorable Living Donor Kidney Transplantation Outcomes within a National Kidney Exchange Program
Bibliographic record
Abstract
Key Points KEP recipients have comparable long-term graft survival to direct living donor kidney transplantation recipients, which underscores the need to prioritize KEP over other's therapies. Our outcomes can be achieved regardless of whether the donor travels or the graft is transported, offering flexibility in program implementation. Background KEPs (kidney exchange programs) facilitate living donor kidney transplantations (LDKTs) for patients with incompatible donors, who are typically at higher risk than non-KEP patients because of higher sensitization and longer dialysis vintage. We conducted a comparative analysis of graft outcomes and risk factors for both KEP and non-KEP living donor kidney transplants. Methods All LDKTs performed in The Netherlands between 2004 and 2021 were included. The primary outcome measures were 1-, 5-, and 10-year death-censored graft survival. The secondary outcome measures were delayed graft function, graft function, rejection rates, and patient survival. We used a propensity score–matching model to account for differences at baseline. Results Of 7536 LDKTs, 694 (9%) were transplanted through the KEP. Ten-year graft survival was similar for KEP (0.916; 95% confidence interval, 0.894 to 0.939) and non-KEP (0.919; 0.912 to 0.926, P = 0.82). We found significant differences in 5-year rejection (12% versus 7%) and 5-year patient survival (KEP: 84%, non-KEP: 90%), which was nonsignificant after propensity score matching. Significant risk factors of lower graft survival included high donor age, retransplantations, extended dialysis vintage, higher panel reactive antibodies, and nephrotic syndrome as the cause of ESKD. Conclusions Transplantation through KEP offers a viable alternative for patients lacking compatible donors, avoiding specific and invasive pre- and post-transplant treatments. KEP's similar survival rate to non-KEPs suggests prioritizing KEP LDKTs over deceased donor kidney transplantation, desensitization, and dialysis. However, clinicians should consider the identified risk factors when planning and managing pre- and post-transplant care to enhance patient outcomes. Thus, we advocate for the broad adoption of KEP and establishment in regions lacking such programs, alongside initiation and expansion of international collaborations.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".