300.6: Long-term outcomes of kidney transplant recipients from deceased donors with circulatory determination of death.
Bibliographic record
Abstract
Background: As per the Global Observatory on Donation and Transplantation, < 10% of the global needs for kidney transplantation (KTx) are met. Donors with circulatory determination of death (DCD) represented 23% of the transplanted organs in 2022. Increased KTx from these donors has the potential to increase the donor pool; however, concerns remain regarding the long-term outcomes associated with their use. Aim: To compare clinical outcomes in recipients of KTx from brain death donors (DBD) vs. DCD at a single academic center. Methods: We performed a retrospective cohort study of all adult KTx recipients from deceased donors, from Sept 1, 2011 to Sept 30, 2022. Patients were classified into four subgroups: DBD-SCD (standard criteria donor), DBD-ECD (expanded criteria donor), DCD non-ECD, and DCD-ECD. The primary outcome was death censored graft survival (DCGS). Secondary outcomes included: Primary non-function (PNF), delayed graft function (DGF), death with graft function, incidence of first acute rejection and estimated glomerular filtration (eGFR) rate over time. Results: We included 940 patients, 757 (80.4%) with KTx from DBD and 183 (19.6%) from DCD. 65.2% were male. We excluded 170 living donor recipients. Amongst the DBD kidneys, 369 (48.7%) were from SCD and 388 (51.3%) from ECD. Among the DCD kidneys, 114 (62.3%) were from non-ECD, and 69 (37.7%) from ECD. DCD kidneys included 37 (20%) from donors having medical assistance in dying (MAiD). Immunosuppression consisted of alemtuzumab induction and maintenance tacrolimus and mycophenolate sodium. Maintenance prednisone was used in highly sensitized patients. At 10 years, DCGS differed significantly among the four subgroups: 78.8% in DBD-SCD, 70.2% in DBD-ECD, 80.4% in DCD non-ECD, and 69.8% in DCD-ECD (p<0.01). PNF did not differ significantly between groups: 1.63% in DBD-SCD, 4.38% in DBD-ECD, 3.51% in DCD non-ECD, and 5.8% in DCD-ECD (p=0.11). The incidence of DGF was as follows: 22.8% in DBD-SCD, 23.2% in DBD-ECD, 45.6% in DCD non-ECD, and 44.9% in DCD-ECD (p<0.01). Death with graft function at 10-years differed significantly among the four subgroups: 21.5% in DBD-SCD, 48.8% in DBD-ECD, 11.6% in DCD non-ECD, and 34.6% in DCD-ECD (p<0.01). The incidence of acute rejection at 1-year was 8.5% in DBD-SCD, 15.4% in DBD-ECD, 14.2% in DCD non-ECD, and 10.7% in DCD-ECD (p=0.09). Median eGFR (ml/min/1.73 m2) at 1-, 5- and 10-years differed between subgroups: DBD-SCD (60, 55, 43), DCD-ECD (40, 35, 22), DCD non-ECD (53, 59, 53), and DBD-ECD (41, 38, 44) (1-year: p<0.01, 5-years: p<0.01, and 10-years: p=0.23). Among the MAiD donor recipients, DCGS was 79.3% and death with graft function was 8% at 5-years.Conclusions: DCD donors remain a critical source of transplantable kidneys to address global needs. Our findings contribute important long-term data to support their use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".