The Survival Benefit of Re-Kidney Transplantation in Older and Younger Patients with Graft Failure
Bibliographic record
Abstract
Background: The survival benefit of re-kidney transplantation (re-KT) has been demonstrated two decades ago in younger patients. The proportion of patients with graft failure is increasing, particularly among those aged≥65. We compared the survival benefit of re-KT by patient age. Methods: Using data from the Scientific Registry of Transplant Recipients, we identified 42,366 patients who experienced graft failure after their first KT and were listed for re-KT between 1990-2019. We treated re-KT as a time-dependent variable and used Cox regression to compare the risk of mortality between being listed for a re-KT and undergoing re-KT. We used the inverse probability weighting method to account for potential confounding. We also tested whether the risk of mortality differed by patient age at listing (18-64 versus ≥65 years) using a Wald test. Results: Overall, 42,366 patients were listed for re-KT and 47.5% underwent re-KT by 10/31/2020. The number of patients being listed for re-KT tripled between 1990 and 2019. The mortality rate was 6.6 per 100 person-years among patients being listed and 3.0 per 100 person-years among those retransplanted. Overall, the risk of mortality was lower after re-KT than during listing (adjusted hazard ratio [aHR]=0.420.430.45). However, the association differed by age (Pinteraction=0.03), but the survival benefit of retransplant was observed among both younger (aHR=0.410.420.44) and older patients (aHR=0.430.490.55). Conclusions: Our finding suggests that re-KT is associated with a significant survival benefit in younger and older patients. In addition, long-term outcomes in older re-KT recipients were reported comparable to those in older first KT recipients. Transplant centers should consider expanding re-KT to appropriate older adults. Funding: NIDDK Support, Other NIH Support - NIAID, NIAFigure 1.: Trends in being listed for retransplant and mortality rate per 100 person-years by calendar year of listing. Table 1. - Mortality rates per 100 person-years and adjusted hazard ratio for mortality comparing retransplant with being listed by age (18-64 versus ≥65 years) No retransplant (n=22,222) Retransplant (n=20,144) aHR (95% CI)‡ p for interaction Overall 6.6 3.0 0.420.430.45 - Age, years 0.03 18-64 (n=40,016) 6.3 2.8 0.410.420.44 - ≥65 (n=2,350) 8.3 6.3 0.430.490.55 - ‡Adjusted for age, sex, race/ethnicity, education level, insurance, body mass index, hypertension, diabetes, malignancy, lifetime of first allograft, listed before failure using the inverse probability weighting method.
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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 | 0.001 |
| 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) | 0.003 | 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".