Five-year experience with donation after cardiac death kidney transplantation in a Canadian transplant program: Factors affecting outcomes
Bibliographic record
Abstract
Background: Donation after cardiac death (DCD) has led to an increase of up to 40% in the number of kidney transplants in some programs. Unfortunately, the increase in warm ischemic time results in higher rates of delayed graft function (DGF). The purpose of our study was to examine our initial 5-year experience with DCD kidney transplantation and to determine the factors involved in early postoperative function and function at 1 year.Methods: This retrospective study included a review of the recipient and donor charts of 63 DCD kidneys retrieved and transplanted by the London Multi-Organ Transplant Program between July 2006 and October 2011. Comparisons were carried out between our early (n=31, July 2006 to January 2009) and our recent experience (n=32, March 2009 to October 2011). DGF and creatinine clearance at 3, 7 and 365 days were examined with regression analyses.Results: DGF was seen in 65% of transplanted kidneys. Mean creatinine clearance (CrCl) at 1 year was 66.7 mL/min. Low pre-transplant recipient daily urine output was the most statistically significant predictor of DGF in multivariate analysis (p < 0.001). In comparisons between our early and more recent results, improvements were noted in time from asystole to flush (16.0 vs. 12.0 minutes, p = 0.003), while cold ischemic time increased (464 vs.725 minutes, p = 0.006). Experience contributed to a significant reduction in hospital length of stay (16 vs. 13 days, p = 0.035) and improved early renal function (CrCl at 3 days 7.8 vs. 11.9 mL/min, p = 0.027). The use of machine cold perfusion and higher recipient preoperative daily urine output predicted improved early renal function, while increasing donor age predicted poorer funcion at 1 year.Discussion: Despite early DGF, our results justify the continued transplantation of kidneys from DCD donors.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".