Cardiac Biomarkers and Acute Kidney Injury After Cardiac Surgery
Bibliographic record
Abstract
OBJECTIVES: To examine the relationship of cardiac biomarkers with postoperative acute kidney injury (AKI) among pediatric patients undergoing cardiac surgery. METHODS: Data from TRIBE-AKI, a prospective study of children undergoing cardiac surgery, were used to examine the association of cardiac biomarkers (N-type pro-B-type natriuretic peptide, creatine kinase-MB [CK-MB], heart-type fatty acid binding protein [h-FABP], and troponins I and T) with the development of postoperative AKI. Cardiac biomarkers were collected before and 0 to 6 hours after surgery. AKI was defined as a ≥ 50% or 0.3 mg/dL increase in serum creatinine, within 7 days of surgery. RESULTS: Of the 106 patients included in this study, 55 (52%) developed AKI after cardiac surgery. Patients who developed AKI had higher median levels of pre- and postoperative cardiac biomarkers compared with patients without AKI (all P < .01). Preoperatively, higher levels of CK-MB and h-FABP were associated with increased odds of developing AKI (CK-MB: adjusted odds ratio 4.58, 95% confidence interval [CI] 1.56-13.41; h-FABP: adjusted odds ratio 2.76, 95% CI 1.27-6.03). When combined with clinical models, both preoperative CK-MB and h-FABP provided good discrimination (area under the curve 0.77, 95% CI 0.68-0.87, and 0.78, 95% CI 0.68-0.87, respectively) and improved reclassification indices. Cardiac biomarkers collected postoperatively did not significantly improve the prediction of AKI beyond clinical models. CONCLUSIONS: Preoperative CK-MB and h-FABP are associated with increased risk of postoperative AKI and provide good discrimination of patients who develop AKI. These biomarkers may be useful for risk stratifying patients undergoing cardiac surgery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".