Effects of Intraoperative Fluid Balance During Liver Transplantation on Postoperative Acute Kidney Injury: An Observational Cohort Study
Bibliographic record
Abstract
BACKGROUND: Liver transplant recipients suffer many postoperative complications. Few studies evaluated the effects of fluid management on these complications. We conducted an observational cohort study to evaluate the association between intraoperative fluid balance and postoperative acute kidney injury (AKI) and other postoperative complications. METHODS: We included consecutive adult liver transplant recipients who had their surgery between July 2008 and December 2017. Our exposure was intraoperative fluid balance, and our primary outcome was the grade of AKI at 48 hours after surgery. Our secondary outcomes were the grade of AKI at 7 days, the need for postoperative renal replacement therapy, postoperative red blood cell transfusions, time to first extubation, time to discharge from the intensive care unit (ICU), and 1-year survival. Every analysis was adjusted for potential confounders. RESULTS: We included 532 transplantations in 492 patients. We observed no effect of fluid balance on either 48-hour AKI, 7-day AKI, or on the need for postoperative renal replacement therapy after adjustments for confounders. A higher fluid balance increased the time to ICU discharge, and increased the risk of dying (hazard ratio = 1.21 [1.04,1.40]). CONCLUSIONS: We observed no association between intraoperative fluid balance and postoperative AKI. Fluid balance was associated with longer time to ICU discharge and lower survival. This study provides insight that might inform the design of a clinical trial on fluid management strategies in this population.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".