Intraoperative hypotension during critical phases of liver transplantation and its impact on acute kidney injury: a retrospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Acute Kidney Injury (AKI) following Liver Transplantation (LT) is associated with prolonged ICU and hospital stay, increased risk of chronic renal disease, and decreased graft survival. Intraoperative hypotension is a modifiable risk factor associated with postoperative AKI. We aimed to determine in which phase of LT hypotension has the strongest association with AKI: the anhepatic or neohepatic phase. METHODS: This retrospective cohort study included adult patients undergoing LT between January 2010 and June 2022. Exclusion criteria were re-do or combined transplantations, preoperative dialysis, and early graft failure or death. Primary outcome was AKI as defined by KDIGO. Hypotension was Mean Arterial Pressure (MAP) below predefined thresholds in minutes. Risk adjusted logistic regression analysis considered hypotension in 3 periods: the total procedure, anhepatic phase, and neohepatic phase. RESULTS: Our cohort included 1153 patients. The median MELD-NA score was 19 (IQR 11-28), and 412 (35.9%) were living-related donations. AKI occurred in 544 patients (47.2%). The unadjusted model showed an association with AKI for MAP < 60 mmHg (OR = 1.011 [1.0, 1.022], p = 0.047) and MAP < 55 mmHg (OR = 1.023 [1.002, 1.047], p = 0.040) in the anhepatic phase, and for MAP < 60 mmHg (OR = 1.032 [1.01, 1.056], p = 0.006) in the neohepatic phase. The adjusted model did not reach significance in the subgroups but did in the total procedure: MAP < 60 mmHg (OR = 1.005 [1.002, 1.008], p < 0.001) and MAP < 55 mmHg (OR = 1.008 [1.003-1.013], p = 0.004). CONCLUSION: Intraoperative hypotension is independently associated with AKI following LT. This association is seen during the anhepatic phase. Maintaining MAP above 60 mmHg may improve kidney function after LT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 | 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".