Impact of acute kidney injury following liver transplantation on long‐term outcomes
Bibliographic record
Abstract
BACKGROUND: The incidence of acute kidney injury (AKI) after liver transplantation (LTx) ranges from 17% to 94%. AKI is associated with prolonged hospitalization and increased early mortality. In our cohort study, we examined the impact of AKI on long-term patient survival and on the incidence of stage 4-5 chronic kidney disease (CKD). METHODS: We studied 491 LTx recipients at a single center between 1990 and 2012. We identified 278 pts (56.6%) with AKI defined as either an increase in serum creatinine (SCr) ≥26.5 μmol/L within 48 hour or elevation in SCr 1.5× baseline within 7 days (KDIGO criteria). RESULTS: In a multivariable Cox proportional hazards model, survival was worse in patients with AKI (HR: 1.41, 95% CI 1.03-1.92). Severe (stage 3) AKI was associated with worse patient survival (HR: 2.29, 95% CI 1.46-3.58). The risk of developing stage 4-5 CKD was also higher in patients with AKI (17.5% vs 9.1%) with a HR of 2.39 (95% CI 1.27-4.47). Delaying initiation of calcineurin inhibitors >48H was not associated with a decreased risk of CKD. CONCLUSIONS: Our findings suggest that AKI after LTx is associated with poor long-term outcomes, including worse survival and higher incidence of CKD stage 4-5. Strategies to prevent and manage LTx patients with AKI need to be developed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".