Pre-Operative Kidney Biomarkers and Risks for Death, Cardiovascular and Chronic Kidney Disease Events: The TRIBE-AKI Study
Bibliographic record
Abstract
ABSTRACT Background Soluble tumor necrosis factor receptor (sTNFR)1, sTNFR2, and plasma kidney injury molecule-1 (KIM-1) are associated with kidney events in patients with and without diabetes. However, their associations with clinical outcomes when obtained pre-operatively have not been explored. Methods The TRIBE-AKI cohort study is a prospective, multicenter, cohort study of high-risk adults undergoing cardiac surgery. We assessed the associations between pre-operative concentrations of plasma sTNFR1, sTNFR2, and KIM-1 and post-operative long-term outcomes including mortality, cardiovascular events, and chronic kidney disease (CKD) incidence or progression, ascertained after discharge. Results Among 1378 participants included in the analysis with a median follow-up period was 6.7 (IQR 4.0-7.9), 434 (31%) patients died, 256 (19%) experienced cardiovascular events and out of 837 with available long-term kidney function data, 30% developed CKD. After adjustment for clinical covariates, each log increase in biomarker concentration was independently associated with mortality with 95%CI adjusted hazard ratios (aHRs) of 3.0 (2.3-4.0), 2.3 (1.8-2.9) and 2.0 (CI 1.6-2.4) for sTNFR1, sTNFR2 and KIM-1, respectively. For cardiovascular events, the 95%CI aHRs were 2.1 (1.5 – 3.1), 1.9 (1.4 – 2.6) and 1.6 (1.2 – 2.1) for sTNFR1, sTNFR2 and KIM-1, respectively. For CKD events, the aHRs were 2.2 (1.5 – 3.1) for sTNFR1, 1.9 (1.3 – 2.7) for sTNFR2, and 1.7 (1.3 – 2.3) for KIM-1. Despite the associations, each of the biomarkers alone or in combination failed to result in robust discrimination on an absolute basis or compared to a clinical model. Conclusion sTNFR1, sTNFR2, and KIM-1 were independently associated with longitudinal outcomes after discharge from a cardiac surgery hospitalization including death, cardiovascular and CKD events when obtained pre-operatively in high-risk individuals. Pre-operative plasma biomarkers could serve to assist during the evaluation of patients in whom cardiac surgery is planned.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".