Urinary biomarkers predict progression and adverse outcomes of acute kidney injury in critical illness
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) is common in hospitalized patients and is associated with high morbidity and mortality. The Dublin Acute Biomarker Group Evaluation study is a prospective cohort study of critically ill patients (n = 717). We hypothesized that novel urinary biomarkers would predict progression of AKI and associated outcomes. METHODS: The primary (diagnostic) analysis assessed the ability of biomarkers levels at the time of early Stage 1 or 2 AKI to predict progression to higher AKI stage, renal replacement therapy (RRT) or death within 7 days of intensive care unit admission. In the secondary (prognostic) analysis, we investigated the association between biomarker levels and RRT or death within 30 days. RESULTS: In total, 186 patients had an AKI within 7 days of admission. In the primary (diagnostic) analysis, 8 of the 14 biomarkers were independently associated with progression. The best predictors were cystatin C [adjusted odds ratio (aOR) 5.2; 95% confidence interval (CI) 1.3-23.6], interleukin-18 (IL-18; aOR 5.1; 95% CI 1.8-15.7), albumin (aOR 4.9; 95% CI 1.5-18.3) and neutrophil gelatinase-associated lipocalin (NGAL; aOR 4.6; 95% CI 1.4-17.9). Receiver-operating characteristics and net reclassification index analyses similarly demonstrated improved prediction by these biomarkers. In the secondary (prognostic) analysis of Stages 1-3 AKI cases, IL-18, NGAL, albumin and monocyte chemotactic protein-1 were also independently associated with RRT or death within 30 days. CONCLUSIONS: Among 14 novel urinary biomarkers assessed, cystatin C, IL-18, albumin and NGAL were the best predictors of Stages 1-2 AKI progression. These biomarkers, after further validation, may have utility to inform diagnostic and prognostic assessment and guide management of AKI in critically ill patients.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".