Utility of Novel Cardiorenal Biomarkers in the Prediction and Early Detection of Congestive Kidney Injury Following Cardiac Surgery
Bibliographic record
Abstract
Acute Kidney Injury (AKI) in the context of right ventricular failure (RVF) is thought to be largely congestive in nature. This study assessed the utility of biomarkers high sensitivity cardiac troponin T (hs-cTnT), N-Terminal Pro-B-Type Natriuretic Peptide (NT-proBNP), and neutrophil gelatinase-associated lipocalin (NGAL) for prediction and early detection of congestive AKI (c-AKI) following cardiac surgery. This prospective nested case-control study recruited 350 consecutive patients undergoing elective cardiac surgery requiring cardiopulmonary bypass. Cases were patients who developed (1) AKI (2) new or worsening RVF, or (3) c-AKI. Controls were patients free of these complications. Biomarker levels were measured at baseline after anesthesia induction and immediately postoperatively. Patients with c-AKI had increased mean duration of mechanical ventilation and length of stay in hospital and in the intensive care unit (p < 0.01). For prediction of c-AKI, baseline NT-proBNP yielded an area under the curve (AUC) of 0.74 (95% CI, 0.60–0.89). For early detection of c-AKI, postoperative NT-proBNP yielded an AUC of 0.78 (0.66–0.91), postoperative hs-cTnT yielded an AUC of 0.75 (0.58–0.92), and ∆hs-cTnT yielded an AUC of 0.80 (0.64–0.96). The addition of baseline creatinine to ∆hs-cTnT improved the AUC to 0.87 (0.76–0.99), and addition of diabetes improved the AUC to 0.93 (0.88–0.99). Δhs-cTnT alone, or in combination with baseline creatinine or diabetes, detects c-AKI with high accuracy following 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".