Renal Dysfunction After Liver Transplantation: Effect of Donor Type
Bibliographic record
Abstract
Recipients of donation after circulatory death (DCD) grafts are reportedly at higher risk of developing renal dysfunction after liver transplantation (LT). We compared the development of acute kidney injury (AKI) and chronic kidney disease (CKD) after LT in recipients of DCD versus donation after brain death (DBD) or living donor liver transplantation (LDLT) livers. Adult recipients of DBD, LDLT, and DCD between 2012 and 2016 at Toronto General Hospital were included. AKI was defined as a post-LT increase of serum creatinine (sCr) ≥26.5 µmol/L within 48 hours or a ≥50% increase from baseline, and CKD was defined as an estimated glomerular filtration rate <60 mL/minute for >3 months. A total of 681 patients (DCD, n = 57; DBD, n = 446; and LDLT, n = 178) with similar baseline comorbidities were included. Perioperative AKI (within the first 7 postoperative days) was observed more frequently in the DCD group (61%; DBD, 40%; and LDLT, 44%; P = 0.01) and was associated with significantly higher peak AST levels (P < 0.001). Additionally, patients in the DCD group had a significantly higher peak sCr (P < 0.001) and a trend toward higher rates of AKI stage 3 (DCD, 33%; DBD, 21%; LDLT, 21%; P = 0.11). The proportions of recovery from AKI (DCD, 77%; DBD, 72%; LDLT, 78%; P = 0.45) and patients developing CKD (DCD, 33%; DBD, 32%; LDLT, 32%; P = 0.99) were similar. Nevertheless, patients who received DCD or DBD LT and required perioperative renal replacement therapy showed significantly lower patient survival in multivariate analysis (hazard ratio, 7.90; 95% confidence interval, 4.51-13.83; P < 0.001). In conclusion, recipients of DCD liver grafts experience higher rates of short-term post-LT renal dysfunction compared with DBD or LDLT. Additional risk factors for the development of severe kidney injury, such as high Model for End-Stage Liver Disease score, massive transfusions, or donor age ≥60 years should be avoided.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".