Biomarkers of AKI Progression after Pediatric Cardiac Surgery
Bibliographic record
Abstract
Background As children progress to higher stages of AKI, the risk for adverse outcomes dramatically increases. No reliable methods exist to predict AKI progression in hospitalized children. To determine if biomarkers of inflammation and kidney injury can predict AKI progression, we conducted a three-center prospective cohort study of children undergoing cardiopulmonary bypass. Methods On the first day of serum creatinine–defined AKI, we measured urine biomarkers (neutrophil gelatinase–associated lipocalin [NGAL], IL-18, kidney injury molecule 1, liver fatty acid binding protein [L-FABP], albumin, and cystatin C) and plasma biomarkers (IFN, IL-1, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12, IL-13, TNF- α , NGAL, and cystatin C). We defined AKI progression as a worsening of AKI stage or persisting stage 3 AKI (≥2 consecutive days). Results In all, 176 of 408 (43%) children developed postoperative AKI. Among the children with AKI, we diagnosed stages 1, 2, and 3 AKI in 145 (82.5%), 25 (14%), and six (3.5%) children, respectively, on the first day of AKI; 28 (7%) children had AKI progression. On the first day of AKI, nine of 17 biomarkers were significantly higher in patients with than without AKI progression. Urine L-FABP (among injury biomarkers) and plasma IL-8 (among inflammatory biomarkers) had the highest discrimination for AKI progression: optimism-corrected area under the curve, 0.70; 95% confidence interval, 0.58 to 0.81 and optimism-corrected area under the curve, 0.80; 95% confidence interval, 0.69 to 0.91, respectively. Conclusions If validated in additional cohorts, plasma IL-8 could be used to improve clinical care and guide enrollment in therapeutic trials of AKI.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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".