Impact of Acute Kidney Injury Following Liver Transplantation On Long-Term Outcomes.
Bibliographic record
Abstract
Background: The incidence of acute kidney injury (AKI) after orthotopic liver transplantation (OLT) ranges from 17% to 64%. AKI is associated with prolonged hospitalization and increased early mortality. However, the long-term outcomes of AKI on mortality and chronic kidney disease (CKD) remain to be determined. Purpose: In our cohort study, we examined the impact of AKI on long-term patient (pt) survival and on the incidence of stage 4 and 5 CKD. Methods: We studied 491 OLT recipients at a single center between 01/1990 and 08/2012, and pts were followed for up to 20 years. We identified 278 pts (56.6%) with AKI defined as either an increase in serum creatinine (SCr) ≥26.5 μmol/L within 48-hr or elevation in SCr 1.5X above baseline within 7 days (KDIGO criteria). Results: In a multivariable Cox proportional hazards model, survival was worse in pts with AKI (HR: 1.40, 95% CI: 1.03-1.89, p=0.032).Figure: No Caption available.The median survival time was 13.2 yrs for pts with AKI, and 17.9 yrs in pts without AKI. Severe (stage 3) AKI was associated with worse pt survival (HR: 2.29, 95% CI: 1.46-3.58, p=0.001), while AKI stages 1 and 2 were not statistically different. The risk of developing stage 4-5 CKD was also higher in pts with AKI compared to non-AKI pts (17.5% vs. 9.1%) with a HR of 2.00 (95% CI: 1.13-3.59, p=0.020).Figure: No Caption available.Conclusions: Our findings suggest that AKI after OLT is associated with poor longterm outcomes, including worse pt survival and higher incidence of CKD stage 4-5. Strategies to prevent and manage OLT pts 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 | 0.002 | 0.004 |
| 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".