A Mate Kidney Analysis to Determine the Impact of Preemptive Transplantation on Outcomes of High Kidney Donor Profile Index Deceased Donor Transplants
Bibliographic record
Abstract
Background: There is an inadequate supply of kidneys for transplant. The kidney donor profile index (KDPI) combines donor factors into a percentile that summarizes the likelihood of deceased donor transplant failure. High KDPI kidneys are frequently discarded. Pre-emptive transplantation is associated with improved patient and graft survival, but it is unknown if this benefit is preserved with high KDPI kidneys. Methods: Using the SRTR database, N = 7,232 donors were identified where one donor kidney was transplanted pre-emptively (before the recipient required dialysis) and the other was used non-pre-emptively (after the recipient has initiated dialysis). We compared all cause graft loss (ACGL), death censored graft loss (DCGL), and death with function (DWF) between the pre-emptive and non-pre-emptive recipients using univariable and multivariable time to event analyses adjusted for differences in recipient factors. Results: Pre-emptive transplantation was associated with improved outcomes of ACGL, DCGL, and DWF (Fig 1). These results were consistent in the subgroup where the donor KDPI was >= 91%. Furthermore, the risk of ACGL with a pre-emptive transplant from a KDPI >= 91% donor (HR: 1.65, CI: 1.51 - 1.81) was similar to the risk of ACGL from a non-pre-emptive transplant from a KDPI 51-80% donor (HR: 1.57 CI: 1-48 - 1.66) (Fig 2).Figure 1:: Kaplan-Meier curves of all cause graft loss, death censored graft loss and death with a functioning graft in a mate kidney cohort.Figure 2.: Plot of the results of multivarlable Cox-proportional hazards model of all cause graft loss. The hazard ratios represented show the relative hazard of all cause graft loss compared to a kidney transplant from a non-pre-amptive donor with a KDPI of 0-20% (horizontal dotled line). The hazard ratios in blue represent the hazard ratio for a pre-emptive kidney transplant, while the hazard ratios in red represent the hazard ratio for a non-pre-emptive kidney transplant. The 95% confidence intervats are represented by the error bars.Conclusions: In this mate kidney analysis, outcomes after a pre-emptive transplant were superior compared to a non-pre-emptive transplant, even among kidneys from donors with very high KDPI. Pre-emptive transplantation of high KDPI kidneys is an opportunity to safely increase the number of kidney transplants from the limited supply of deceased donor kidneys.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".