Preoperative Plasma TNFR1, TNFR2, and KIM-1 and Long-Term Adverse Events After Cardiac Surgery: The TRIBE-AKI Study
Bibliographic record
Abstract
Background: Plasma TNFR1, TNFR2, and KIM-1 have been associated with CKD progression in ambulatory patients with/without diabetes. However, their role as predictors of long-term outcomes and their ability to discriminate such outcomes compared to clinical parameters prior to cardiac surgery is unknown. Methods: Prospective, multicenter cohort study of high-risk adults undergoing cardiac surgery (2007-2010). We assessed the association between pre-operative levels of TNFR1, TNFR2, and KIM-1 (natural log-transformed) and long-term mortality, CKD (incidence/progression), and cardiovascular (CV) events. We also examined the potential effect modification of DM status on the relationship between these biomarkers and outcomes. C-statistic analysis was used to quantify the discriminatory ability of the biomarkers beyond the clinical model. Results: 1378 participants (69.1% male) with a mean age: 71.9 ± 9.7, were followed for a median of 5.6 (IQR 4.3-8.6) years. 434 (31.5%) died within the study timeframe, 251 (30%) developed CKD, & 256 (19%) had CV events. After adjustment for covariates, each natural log increase in biomarker concentration was associated with mortality [adjusted HR: TNFR1, 3.0 (95% CI 2.3-4.0); TNFR2, 2.3 (95% CI 1.8-2.9); KIM-1, 2.0 (95% CI 1.6-2.4)]. Similar effect sizes were seen for all 3 biomarkers in their association with CV & CKD events (Figure 1). Baseline DM status did not modify the association between biomarkers and clinical outcomes. The addition of all 3 biomarkers improved discrimination for the 3 outcomes. Conclusions: Preoperative plasma TNFR1, TNFR2, and KIM-1 were independently associated with long-term outcomes after cardiac surgery and improved discrimination compared to standard clinical models. Pre-operative plasma biomarkers may serve with timely risk-stratification and planning to prevent clinical sequela. Funding: Other NIH Support - NIH/NHLB instituteHRs were adjusted for age, sex, race, pre-op clinical & kidney-related parameters.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".