Graft Steatosis and Donor Diabetes Mellitus Additively Impact on Recipient Outcomes After Liver Transplantation—A European Registry Study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Biopsy-proven severe graft steatosis is associated with adverse outcomes after liver transplantation. The concomitant presence of metabolic risk factors might further increase this risk. We studied the association between graft steatosis and metabolic risk factors in the donor, with recipient outcomes after liver transplantation. METHODS: We analyzed data from all consecutive first adult full-graft donation after brain death (DBD) liver transplantations performed in the Eurotransplant region between 2010 and 2020. The presence of graft steatosis and metabolic risk factors was assessed through a review of donor (imaging) reports, and associations with recipient retransplantation-free survival were studied through survival analyses. RESULTS: Of 12 174 transplantations, graft steatosis was detected in 2689 (22.1%), and donor diabetes mellitus (DM), hypertension, and dyslipidemia were present in 1245 (10.2%), 5056 (41.5%), and 524 (4.3%). In multivariable Cox regression analysis, graft steatosis (adjusted HR [aHR] 1.197, p < 0.001) and donor DM (aHR 1.157, p = 0.004) were independently associated with impaired retransplantation-free survival. Graft steatosis and donor DM conferred an additive risk of retransplantation or death (DM alone, aHR: 1.156 [p = 0.0185]; steatosis alone, aHR: 1.200 [p < 0.001]; both steatosis and DM, aHR: 1.381 [p < 0.001]). Findings were consistent in sensitivity analyses focusing on retransplantation-free survival within 7 days. CONCLUSIONS: Graft steatosis and donor diabetes mellitus additively increase the risk of retransplantation or death in adult DBD liver transplantation. Future studies should focus on methods to assess and improve the quality of these high-risk grafts. Until such time, caution should be exercised when considering these grafts for transplantation.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".