First Post-Operative Urinary Kidney Injury Biomarkers and Association with the Duration of AKI in the TRIBE-AKI Cohort
Bibliographic record
Abstract
BACKGROUND: We previously demonstrated that assessment of the duration of AKI, in addition to magnitude of rise in creatinine alone, adds prognostic information for long-term survival. We evaluated whether post-operative kidney injury biomarkers in urine collected immediately after cardiac surgery associate with duration of serum creatinine elevation. METHODS: We studied 1199 adults undergoing cardiac surgery in a prospective cohort study (TRIBE-AKI) and examined the association between the levels of five urinary biomarkers individually at 0-6 hours after surgery: interleukin-18 (IL-18), neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), liver fatty acid binding protein (L-FABP) and albumin with duration of serum creatinine-based AKIN criteria for AKI (0 (no AKI), 1-2, 3-6, ≥7 days). RESULTS: Overall, 407 (34%) patients had at least stage 1 AKI, of whom 251 (61.7%) had duration of 1-2 days, 118 (28.9%) had duration 3-6 days, and 38 (9.3%) had duration of ≥7 days. Higher concentrations of all biomarkers (per log increase) were independently associated with a greater odds of a longer duration of AKI; odds ratios and 95% confidence intervals using ordinal logistic regression were the following: IL-18: 1.22, 1.13-1.32; KIM-1: 1.36, 1.21-1.52; albumin 1.20, 1.09-1.32; L-FABP 1.11, 1.04-1.19; NGAL 1.06, 1.00-1.14). AKI duration of 7 days or longer was associated with a 5-fold adjusted risk of mortality at 3 years. CONCLUSIONS: There was an independent dose-response association between urinary levels of injury biomarkers immediately after cardiac surgery and longer duration of AKI. Duration of AKI was also associated with long term mortality. Future studies should explore the potential utility of these urinary kidney injury biomarkers to enrich enrollment of patients at risk for longer duration of AKI into trials of interventions to prevent or treat post-operative AKI.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".