Intraoperative Vasoactive Medications and Perioperative Outcomes in Liver Transplantation: A Systematic Review and Network Meta-analyses
Bibliographic record
Abstract
We conducted a systematic review and network meta-analyses evaluating the effects of different intraoperative vasoactive drugs on acute kidney injury (AKI) and other perioperative outcomes in adult liver transplant recipients. We searched multiple electronic databases using words from the "liver transplantation" and "vasoactive drug" domains. We included all randomized controlled trials conducted in adult liver transplant recipients comparing 2 different intravenous vasoactive drugs or 1 against a standard of care that reported AKI, intraoperative blood loss, or any other postoperative outcome. We conducted 4 frequentist network meta-analyses using random effect models, based on the interventions' mechanism of action, and evaluated the quality of evidence (QoE) using Grading of Recommendations, Assessment, Development, and Evaluations recommendations. We included 9 randomized controlled trials comparing different vasopressor drugs (vasoconstrictor or inotrope), 3 comparing a somatostatin infusion (or its analogues) to a standard of care, 11 comparing different vasodilator infusions together or against a standard of care, and 2 comparing vasoconstrictor boluses at graft reperfusion. Intravenous clonidine was associated with shorter duration of mechanical ventilation, intensive care unit, and hospital length of stay (very low QoE), and some vasodilators were associated with lower creatinine level 24 h after surgery (low to very low QoE). Phenylephrine and terlipressin were associated with less intraoperative blood loss when compared with norepinephrine (low and moderate QoE). None of the vasoactive drugs improve any other postoperative outcomes, including AKI. There is still important equipoise regarding the best vasoactive drug to use in liver transplantation for most outcomes. Further studies are required to better inform clinical practice.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.041 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".