Prolonged warm ischemia time leads to severe renal dysfunction of donation-after-cardiac death kidney grafts
Bibliographic record
Abstract
Kidney transplantation with grafts procured after donation-after-cardiac death (DCD) has led to an increase in incidence of delayed graft function (DGF). It is thought that the warm ischemic (WI) insult encountered during DCD procurement is the cause of this finding, although few studies have been designed to definitely demonstrate this causation in a transplantation setting. Here, we use a large animal renal transplantation model to study the effects of prolonged WI during procurement on post-transplantation renal function. Kidneys from 30 kg-Yorkshire pigs were procured following increasing WI times of 0 min (Heart-Beating Donor), 30 min, 60 min, 90 min, and 120 min (n = 3-6 per group) to mimic DCD. Following 8 h of static cold storage and autotransplantation, animals were followed for 7-days. Significant renal dysfunction (SRD), resembling clinical DGF, was defined as the development of oliguria < 500 mL in 24 h from POD3-4 along with POD4 serum potassium > 6.0 mmol/L. Increasing WI times resulted in incremental elevation of post-operative serum creatinine that peaked later. DCD120min grafts had the highest and latest elevation of serum creatinine compared to all groups (POD5: 19.0 ± 1.1 mg/dL, p < 0.05). All surviving animals in this group had POD4 24 h urine output < 500 cc (mean 235 ± 172 mL) and elevated serum potassium (7.2 ± 1.1 mmol/L). Only animals in the DCD120min group fulfilled our criteria of SRD (p = 0.003), and their renal function improved by POD7 with 24 h urine output > 500 mL and POD7 serum potassium < 6.0 mmol/L distinguishing this state from primary non-function. In a transplantation survival model, this work demonstrates that prolonging WI time similar to that which occurs in DCD conditions contributes to the development of SRD that resembles clinical DGF.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".