Urinary Hepcidin-25 and Risk of Acute Kidney Injury Following Cardiopulmonary Bypass
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Acute kidney injury (AKI) complicating cardiopulmonary bypass (CPB) results in increased morbidity and mortality. Urinary hepcidin-25 has been shown to be elevated in patients who do not develop AKI after CPB using semiquantitative mass spectrometry (SELDI TOF-MS). The goals of this study were to quantitatively validate these findings with ELISA and evaluate the diagnostic performance of hepcidin-25 for AKI. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: A nested, case-control analysis of urinary hepcidin-25 in AKI (n = 22) and non-AKI (n = 22) patients was conducted to validate the SELDI TOF-MS data at the following times: preoperatively; the start of CPB; 1 hour on CPB; on arrival to the intensive care unit; and postoperative days (POD) 1 and 3 to 5. The diagnostic performance of hepcidin-25 was then evaluated in the entire prospective observational cohort (n = 338) at POD 1. AKI was defined as Cr >50% from baseline, within 72 hours postoperatively. RESULTS: Urinary hepcidin-25/Cr ratio was significantly elevated in all patients at POD 1 compared with baseline (P < 0.0005) and was also significantly elevated in non-AKI versus AKI patients at POD 1 (P < 0.0005). Increased log(10) hepcidin-25/Cr ratio was strongly associated with avoidance of AKI on univariate analysis. On multivariate analysis, the log(10) hepcidin-25/Cr ratio (P < 0.0001) was associated with avoidance of AKI with an area under the curve of 0.80, sensitivity 0.68, specificity 0.68, and negative predictive value 0.96. CONCLUSIONS: Elevated urinary hepcidin-25 on POD 1 is a strong predictor of avoidance of AKI beyond postoperative day 1.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".