Association of T Cell–Derived Inflammatory Cytokines With Acute Kidney Injury and Mortality After Cardiac Surgery
Bibliographic record
Abstract
INTRODUCTION: Animal models of renal ischemia-reperfusion injury (IRI) demonstrate that interferon (IFN)-γ producing T-helper (Th)-1 cells worsen acute kidney injury (AKI), whereas interleukin (IL)-4- and IL-13-producing Th2 cells lead to repair. We tested the association of these cytokines with AKI and mortality in patients who underwent cardiac surgery. METHODS: In 1444 participants of a multicenter, prospective, observational cohort, we measured 10 plasma biomarkers before and after cardiac surgery (IFN-γ, IL-4, IL-13, tumor necrosis factor [TNF]-α, IL-1β, IL-2, IL-6, IL-8, IL-10, and IL-12p70) and combined these biomarkers using principal component analysis (PCA). We also tested independent associations of Th1 (IFN-γ) and Th2 (IL-4 and IL-13) biomarkers with clinical outcomes of postoperative AKI and 1-year mortality. RESULTS: AKI occurred in 492 participants (34%), and 1-year mortality occurred in 81 participants (6%). Within 6 hours after surgery, IFN-γ, IL-4, and IL-13 increased 2.1-, 6.0-, and 4.6-fold, respectively, from their preoperative levels. Patients with higher levels of IFN-γ had higher odds of AKI (adjusted odds ratio per log change, 1.35 [1.13, 1.6]) and mortality (1.51 [1.17, 1.94]). Patients with higher levels of IL-4 and IL-13 also had higher odds of AKI (1.26 [1.09, 1.46] and 1.4 [1.16, 1.69], respectively) and mortality (1.46 [1.18, 1.82] and 1.71 [1.27, 2.31], respectively). Adding biomarkers to the clinical variables through use of PCA improved the area under the curve by 0.01 for AKI and 0.04 for mortality, resulting in final areas under the curve of 0.85 (0.83-0.87) and 0.76 (0.70-0.81), respectively. CONCLUSION: Both Th1 and Th2 cytokines increased immediately after cardiac surgery and were associated with AKI and 1-year mortality. Our findings indicate activation of both Th1 and Th2 pathways after cardiac surgery rather than predominance of either pathway.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".