Postoperative Biomarkers Predict Acute Kidney Injury and Poor Outcomes after Adult Cardiac Surgery
Why this work is in the frame
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Bibliographic record
Abstract
Acute kidney injury (AKI) is a frequent complication of cardiac surgery and increases morbidity and mortality. The identification of reliable biomarkers that allow earlier diagnosis of AKI in the postoperative period may increase the success of therapeutic interventions. Here, we conducted a prospective, multicenter cohort study involving 1219 adults undergoing cardiac surgery 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 patient outcomes. Urine IL-18 and urine and plasma NGAL levels peaked within 6 hours after surgery. After multivariable adjustment, the highest quintiles of urine IL-18 and plasma NGAL associated with 6.8-fold and 5-fold higher odds of AKI, respectively, compared with the lowest quintiles. Elevated urine IL-18 and urine and plasma NGAL levels associated with longer length of hospital stay, longer intensive care unit stay, and higher risk for dialysis or death. The clinical prediction model for AKI had an area under the receiver-operating characteristic curve (AUC) of 0.69. Urine IL-18 and plasma NGAL significantly improved the AUC to 0.76 and 0.75, respectively. Urine IL-18 and plasma NGAL significantly improved risk prediction over the clinical models alone as measured by net reclassification improvement (NRI) and integrated discrimination improvement (IDI). In conclusion, urine IL-18, urine NGAL, and plasma NGAL associate with subsequent AKI and poor outcomes among adults undergoing cardiac surgery.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it