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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".