Association Between Liver Graft to Recipient Weight Ratio and Acute Kidney Injury Following Liver Transplantation: A Historical Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Acute kidney injury (AKI) is a frequent complication following liver transplantation (LT) that has a multifactorial etiology. While some perioperative risk factors have been associated with postoperative AKI, the impact of liver graft weight to recipient body weight ratio (GW/RBW) has been poorly explored. We hypothesized that a high GW/RBW ratio would be associated with AKI after LT. METHODS: This single-center historical cohort study included all consecutive adults who had LT at Paul Brousse Hospital between 2018 and 2022. Patients requiring preoperative renal replacement therapy, combined solid organ transplantation, retransplantation, split or domino graft were excluded, as well as those with missing graft weight and creatinine values during the first postoperative week. The primary exposure was GW/RBW ratio expressed as a proportion. The primary outcome was the incidence of postoperative AKI within 7 days after surgery, defined using the Kidney Disease: Improving Global Outcomes (KDIGO) criteria. The secondary outcome was the AKI severity (KDIGO grades). We estimated logistic and ordinal regression models adjusted for potential confounding factors and explored nonlinear associations. RESULTS: Of 467 patients analyzed, 211 (45%) developed AKI. A high GW/RBW ratio was associated with both the risk of postoperative AKI and the severity of AKI (KDIGO grades), especially above a threshold of 2.5% (non-linear effect). CONCLUSION: A high GW/RBW ratio was associated with an exponential increase in the risk of AKI after LT. A high GW/RBW ratio was also associated with an increased AKI severity. Our findings may help improve graft allocation in patients undergoing LT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".