Early Persistent Progressive Acute Kidney Injury and Graft Failure Post Liver Transplantation
Bibliographic record
Abstract
Background Acute kidney injury (AKI) in the setting of liver transplantation is a common and multifaceted complication. Studies in the general population have demonstrated worse prognosis with AKI episodes that persist for a longer duration. Our primary objective was to evaluate the impact of early AKI episodes that are persistent or progressive in nature, on patient outcomes and graft survival. Methods This was a retrospective cohort study including all patients who received a liver transplant between 2011 and 2015 at our center. Moderate to severe AKI episodes (AKIN II or III) were recorded immediately before transplantation and after surgery until hospital discharge. We evaluated the incidence density rate (IDR) of graft failure and the time to graft failure in patients with persistent or progressive AKI (ppAKI) as compared to controls. Results Two hundred seventy-nine patients received 301 deceased donor liver allografts. Progressive or persistent AKI was documented in more than half of transplant cases (152/301). The rate of graft loss was 3 times higher in the ppAKI group (25%) versus the controls (8.7%). The IDR of graft failure was 13.79 per 100 case-years in the ppAKI group as compared with 3.79 per 100 case-years in the controls (IDR ratio, 3.64; 95 % confidence interval, 1.88–7.50). After adjusting for hepatic artery thrombosis, ischemic cholangiopathy, infectious complications and Model for End-stage Liver Disease, ppAKI was associated with a decreased graft survival time. Conclusions Persistent or progressive AKI after liver transplantation is associated with an increased incidence rate of graft failure and is an independent predictor of decreased graft survival time.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".