Risk factors for in-hospital postoperative complications and 6-month graft survival after liver transplantation: A multicenter cohort study
Bibliographic record
Abstract
Liver transplantation (LT) is a high-risk surgery requiring costly hospital resources. Robust multicenter data on the incidence of postoperative complications and their risk factors remain very limited. The objectives of this study were to describe the incidence and variability of postoperative complications in adult LT recipients and identify their determinants. We conducted a cohort study that included consecutive LT recipients over at least 1 year between January 2021 and May 2023 in 8 LT centers in Canada and France. Our primary outcome was 7-day early allograft dysfunction or primary graft non-function. Our secondary outcomes included acute kidney injury (AKI) and severe complications. We measured the incidence and variability of these outcomes and their association with potential preoperative determinants using multivariable models. We reported incidences and risk ratios (RR) with 95% CIs. We included 852 patients. The incidence of our primary outcome, AKI, and severe complications was, respectively 28% [95% CI, 25%-31%], 50% [95% CI, 47%-54%], and 59% [95% CI, 55%-62%]. Most outcomes were variable across centers. The primary outcome was mostly determined by donor age, body mass index, static cold ischemia time, and type of donation (RR from 0.64 to 1.21). MELD 3.0 score and preoperative requirement for organ support were important determinants of transfusions, AKI, and severe complications. The incidence of most outcomes was variable across centers. In conclusion, postoperative complications, such as graft dysfunction, AKI, and severe complications, were frequent after LT. We identified risk factors, such as donor and graft characteristics, MELD 3.0, and preoperative requirement for organ support, that may inform transplant risk evaluation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".