Interleukin-8 and Acute Kidney Injury following Cardiopulmonary Bypass: A Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Cardiopulmonary bypass (CPB) elicits an inflammatory response mediated partly by neutrophils, which are activated and recruited by interleukin-8 (IL-8). We hypothesized that acute kidney injury (AKI) following CPB might be mediated by IL-8 and examined the association of perioperative plasma IL-8 levels with AKI in a prospective cohort. METHODS: Plasma IL-8 was measured before, and 2, 24 and 48 h following CPB. Two AKI definitions, a serum creatinine increase of > or = 0.3 mg/dl or 50% (AKI Network [AKIN] stage-1) or > or = 50% alone (AKI-50%), within the first 72 h, were used. Area under the receiver operator characteristic curves (AUCs) were generated and multivariable logistic regression analyses performed. RESULTS: A total of 143 patients were enrolled. The baseline mean serum creatinine was 1.1 mg/ dl (SD = 0.3), the CPB perfusion time was 112 min (SD = 43). Twenty-nine percent of the patients developed AKIN stage-1 and 13% AKI-50%. The plasma IL-8 level 2 h after CPB was higher in AKIN stage-1 (p = 0.03) and AKI-50% (p < 0.01), and predicted AKIN stage-1 (AUC = 0.62; p = 0.02) and AKI-50% (AUC = 0.72; p < 0.01). On multivariable analysis, the 2-hour plasma IL-8 level was associated with 1.36- and 1.59-fold higher odds for AKIN stage-1 and AKI-50%, respectively (p = 0.05). CONCLUSION: Plasma IL-8 predicts the development of AKI following CPB, supporting a potential involvement for this chemokine in the pathogenesis of AKI.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".