Postoperative Biomarkers Predict Acute Kidney Injury and Poor Outcomes after Pediatric Cardiac Surgery
Bibliographic record
Abstract
Acute kidney injury (AKI) occurs commonly after pediatric cardiac surgery and associates with poor outcomes. Biomarkers may help the prediction or early identification of AKI, potentially increasing opportunities for therapeutic interventions. Here, we conducted a prospective, multicenter cohort study involving 311 children undergoing surgery for congenital cardiac lesions to evaluate whether early postoperative measures of urine IL-18, urine neutrophil gelatinase-associated lipocalin (NGAL), or plasma NGAL could identify which patients would develop AKI and other adverse outcomes. Urine IL-18 and urine and plasma NGAL levels peaked within 6 hours after surgery. Severe AKI, defined by dialysis or doubling in serum creatinine during hospital stay, occurred in 53 participants at a median of 2 days after surgery. The first postoperative urine IL-18 and urine NGAL levels strongly associated with severe AKI. After multivariable adjustment, the highest quintiles of urine IL-18 and urine NGAL associated with 6.9- and 4.1-fold higher odds of AKI, respectively, compared with the lowest quintiles. Elevated urine IL-18 and urine NGAL levels associated with longer hospital stay, longer intensive care unit stay, and duration of mechanical ventilation. The accuracy of urine IL-18 and urine NGAL for diagnosis of severe AKI was moderate, with areas under the curve of 0.72 and 0.71, respectively. The addition of these urine biomarkers improved risk prediction over clinical models alone as measured by net reclassification improvement and integrated discrimination improvement. In conclusion, urine IL-18 and urine NGAL, but not plasma NGAL, associate with subsequent AKI and poor outcomes among children undergoing cardiac surgery.
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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 | 0.005 |
| 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 | 0.000 |
| 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".