Cardiac Biomarkers for Risk Stratification of Acute Kidney Injury After Pediatric Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Children undergoing a cardiac surgical procedure are at increased risk for acute kidney injury (AKI). Novel biomarkers are needed to improve risk stratification of AKI after cardiac surgery. METHODS: We enrolled children aged 1 month to 18 years old from July 2007 to December 2010 undergoing cardiopulmonary bypass. Three United States Food and Drug Administration-approved plasma biomarkers of cardiac stretch, N-terminal pro B-type natriuretic peptide (NTproBNP), inflammation (ST2), or fibrosis (galectin-3), were measured preoperatively and postoperatively within 6 hours of cardiac surgery. All analyses were stratified by age (<2 or ≥2 years old) to account for changing biomarker distributions during childhood and due to a significant interaction between biomarker and age for galectin-3 and NTproBNP (P < .05). RESULTS: Postoperatively, AKI, defined by a doubling of baseline serum creatinine, was diagnosed in 51 of 194 children <2 years and in 28 of 201 children ≥2 years. After multivariable adjustment, for children <2 years, none of the biomarkers were independently associated with AKI, whereas for children ≥2 years, the highest tertile of preoperative galectin-3 and NTproBNP as well as the postoperative galectin-3 and ST2 were associated with AKI. CONCLUSIONS: Preoperative plasma galectin-3 and NTproBNP and the first postoperative galectin-3 and ST2 levels were independently associated with AKI in children ≥2 years old. The performance of cardiac biomarkers after cardiac surgical procedure is affected by age, and research is required to develop biomarkers for children <2 years old.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".