Combination of biomarkers for diagnosis of acute kidney injury after cardiopulmonary bypass
Bibliographic record
Abstract
Novel acute kidney injury (AKI) biomarkers offer promise of earlier diagnosis and risk stratification, but have yet to find widespread clinical application. We measured urinary α and π glutathione S-transferases (α-GST and π-GST), urinary l-type fatty acid-binding protein (l-FABP), urinary neutrophil gelatinase-associated lipocalin (NGAL), urinary hepcidin and serum cystatin c (CysC) before surgery, post-operatively and at 24 h after surgery in 93 high risk patient undergoing cardiopulmonary bypass (CPB) and assessed the ability of these biomarkers alone and in combination to predict RIFLE-R defined AKI in the first 5 post-operative days. Twenty-five patients developed AKI. π-GST (ROCAUC = 0.75), lower urine Hepcidin:Creatine ratio at 24 h (0.77), greater urine NGAL:Cr ratio post-op (0.73) and greater serum CysC at 24 h (0.72) best predicted AKI. Linear combinations with significant improvement in AUC were: Hepcidin:Cr 24 h + post-operative π-GST (AUC = 0.86, p = 0.01), Hepcidin:Cr 24 h + NGAL:Cr post-op (0.84, p = 0.03) and CysC 24 h + post-operative π-GST (0.83, p = 0.03), notably these significant biomarkers combinations all involved a tubular injury and a glomerular filtration biomarker. Despite statistical significance in receiver-operator characteristic (ROC) analysis, when assessed by ability to define patients to two groups at high and low risk of AKI, combinations failed to significantly improve classification of risk compared to the best single biomarkers. In an alternative approach using Classification and Regression Tree (CART) analysis a model involving NGAL:Cr measurement post-op followed by Hepcidin:Cr at 24 h was developed which identified high, intermediate and low risk groups for AKI. Regression tree analysis has the potential produce models with greater clinical utility than single combined scores.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.000 | 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 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".