Plasma IL-6 and IL-10 Concentrations Predict AKI and Long-Term Mortality in Adults after Cardiac Surgery
Bibliographic record
Abstract
Inflammation has an integral role in the pathophysiology of AKI. We investigated the associations of two biomarkers of inflammation, plasma IL-6 and IL-10, with AKI and mortality in adults undergoing cardiac surgery. Patients were enrolled at six academic centers (n = 960). AKI was defined as a ≥ 50% or ≥ 0.3-mg/dl increase in serum creatinine from baseline. Pre- and postoperative IL-6 and IL-10 concentrations were categorized into tertiles and evaluated for associations with outcomes of in-hospital AKI or postdischarge all-cause mortality at a median of 3 years after surgery. Preoperative concentrations of IL-6 and IL-10 were not significantly associated with AKI or mortality. Elevated first postoperative IL-6 concentration was significantly associated with higher risk of AKI, and the risk increased in a dose-dependent manner (second tertile adjusted odds ratio [OR], 1.61 [95% confidence interval (95% CI), 1.10 to 2.36]; third tertile adjusted OR, 2.13 [95% CI, 1.45 to 3.13]). First postoperative IL-6 concentration was not associated with risk of mortality; however, the second tertile of peak IL-6 concentration was significantly associated with lower risk of mortality (adjusted hazard ratio, 0.75 [95% CI, 0.57 to 0.99]). Elevated first postoperative IL-10 concentration was significantly associated with higher risk of AKI (adjusted OR, 1.57 [95% CI, 1.04 to 2.38]) and lower risk of mortality (adjusted HR, 0.72 [95% CI, 0.56 to 0.93]). There was a significant interaction between the concentration of neutrophil gelatinase-associated lipocalin, an established AKI biomarker, and the association of IL-10 concentration with mortality (P = 0.01). These findings suggest plasma IL-6 and IL-10 may serve as biomarkers for perioperative outcomes.
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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 | 0.003 |
| 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 | 0.001 | 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".