Urine Biomarkers and Risk of Long-Term Kidney Outcomes After Cardiac Surgery: the TRIBE-AKI Study
Bibliographic record
Abstract
Background: The urine biomarkers epidermal growth factor (EGF) and monocyte chemoattractant protein-1 (MCP-1) show promise as biomarkers of chronic kidney disease (CKD) progression in settings such as diabetes mellitus, but their role in the transition from AKI to CKD remains unclear. EGF is produced specifically by renal tubular epithelium of the thick ascending limb and MCP-1 is extensively studied as a marker of kidney inflammation. We evaluated the associations of urine EGF and MCP-1 with CKD incidence or progression after cardiac surgery. Methods: In this sub-study of the prospective TRIBE-AKI cohort, we evaluated 865 adult patients who underwent cardiac surgery from 2007-2010 at two sites in Canada and the US. We tested the association of first post-operative urine EGF and MCP-1, and the ratio EGF/MCP-1, with the composite outcome of CKD incidence or progression. We assessed for interaction by peri-operative AKI status. Results: Over a median (IQR) follow-up of 5.8 (4.2-7.1) years, 266 (30.8%) patients developed the composite outcome at an event rate (95% CI) of 55.4 (49.2-62.5) per 1,000 person-years. Elevated levels of first post-operative urinary EGF and MCP-1 were each independently associated with the composite outcome, in opposing directions (Table 1), and the ratio (EGF/MCP-1) was strongly associated with decreased risk of CKD incidence or progression in both continuous and categorical analysis (aHR 0.50 [0.33-0.74] for T3 compared to T1). There was no interaction by AKI status. Conclusions: Urine EGF and MCP-1 measured post-cardiac surgery were independently associated with CKD incidence and progression. The ratio of urine EGF/MCP-1 may be useful for risk prediction of future CKD outcomes after peri-operative injury in cardiac surgery. Funding: NIDDK Support
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".