Urinary Biomarkers and Progression of AKI in Patients with Cirrhosis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: AKI is a common and severe complication in patients with cirrhosis. AKI progression was previously shown to correlate with in-hospital mortality. Therefore, accurately predicting which patients are at highest risk for AKI progression may allow more rapid and targeted treatment. Urinary biomarkers of structural kidney injury associate with AKI progression and mortality in multiple settings of AKI but their prognostic performance in patients with liver cirrhosis is not well known. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: A multicenter, prospective cohort study was conducted at four tertiary care United States medical centers between 2009 and 2011. The study comprised patients with cirrhosis and AKI defined by the AKI Network criteria evaluating structural (neutrophil gelatinase-associated lipocalin, IL-18, kidney injury molecule-1 [KIM-1], liver-type fatty acid-binding protein [L-FABP], and albuminuria) and functional (fractional excretion of sodium [FENa]) urinary biomarkers as predictors of AKI progression and in-hospital mortality. RESULTS: Of 188 patients in the study, 44 (23%) experienced AKI progression alone and 39 (21%) suffered both progression and death during their hospitalization. Neutrophil gelatinase-associated lipocalin, IL-18, KIM-1, L-FABP, and albuminuria were significantly higher in patients with AKI progression and death. These biomarkers were independently associated with this outcome after adjusting for key clinical variables including model of end stage liver disease score, IL-18 (relative risk [RR], 4.09; 95% confidence interval [95% CI], 1.56 to 10.70), KIM-1 (RR, 3.13; 95% CI, 1.20 to 8.17), L-FABP (RR, 3.43; 95% CI, 1.54 to 7.64), and albuminuria (RR, 2.07; 95% CI, 1.05-4.10) per log change. No biomarkers were independently associated with progression without mortality. FENa demonstrated no association with worsening of AKI. When added to a robust clinical model, only IL-18 independently improved risk stratification on a net reclassification index. CONCLUSIONS: Multiple structural biomarkers of kidney injury, but not FENa, are independently associated with progression of AKI and mortality in patients with cirrhosis. Injury marker levels were similar between those without progression and those with progression alone.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".