Delayed Graft Function Is Associated with an Increased Risk of Early Death Post-Transplant in Elderly Kidney Transplant Recipients
Bibliographic record
Abstract
The proportion of elderly kidney transplant recipients worldwide continues to grow over time. Even in patients aged over 70, transplantation confers a long-term survival benefit compared to dialysis. However, there remains a higher risk of death in the first 125 days post-transplant in these patients. Therefore, understanding risk factors for death early post-transplant is important in the elderly. In most regions, elderly patients are more likely to receive a deceased donor transplant kidney from older donors, which may confer a DGF. We sought to examine the impact of DGF on early post-transplant survival in elderly transplant recipients. Methods: Using data from the USRDS, we utilized Cox PH models to examine the association of DGF with death within the first 6 months post-transplant in deceased donor kidney transplant recipients aged 18-39 (n=17,366), 40-49 (n=18,047), 50-59 (n=21,333), 60-69 (n=15,333), and 70+ years (n=4308) during 1997-2007. Results: The incidence of DGF was similar in all age groups, ranging from 20-23%. The table outlines the hazards for death in the first 6 months post-transplant in those with DGF compared to those without DGF in each age group, unadjusted and adjusted for donor, recipient, and transplant factors. In each model, DGF was associated with a higher hazard of death within the first 6 months in recipients aged 70 and older, whereas this was not seen in other age groups.Table: [Hazard for Death (including death after graft loss]Conclusions: DGF is associated with a higher risk of death in the early post-transplant period in elderly recipients. These results highlight the need to tailor allocation of organs and peri-operative management in the elderly to minimize the risk DGF and its associated impact on early death post-transplant.
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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 | 0.003 |
| 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) | 0.001 | 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".