Urinary Biomarkers of AKI and Mortality 3 Years after Cardiac Surgery
Bibliographic record
Abstract
Urinary biomarkers of AKI provide prognostic value for in-hospital outcomes, but little is known about their association with longer-term mortality after surgery. We sought to assess the association between kidney injury biomarkers and all-cause mortality in an international, multicenter, prospective long-term follow-up study from six clinical centers in the United States and Canada composed of 1199 adults who underwent cardiac surgery between 2007 and 2009 and were enrolled in the Translational Research in Biomarker Endpoints in AKI cohort. On postoperative days 1-3, we measured the following five urinary biomarkers: neutrophil gelatinase-associated lipocalin, IL-18, kidney injury molecule-1 (KIM-1), liver fatty acid binding protein, and albumin. During a median follow-up of 3.0 years (interquartile range, 2.2-3.6 years), 139 participants died (55 deaths per 1000 person-years). Among patients with clinical AKI, the highest tertiles of peak urinary neutrophil gelatinase-associated lipocalin, IL-18, KIM-1, liver fatty acid binding protein, and albumin associated independently with a 2.0- to 3.2-fold increased risk for mortality compared with the lowest tertiles. In patients without clinical AKI, the highest tertiles of peak IL-18 and KIM-1 also associated independently with long-term mortality (adjusted hazard ratios [95% confidence intervals] of 1.2 [1.0 to 1.5] and 1.8 [1.4 to 2.3] for IL-18 and KIM-1, respectively), and yielded continuous net reclassification improvements of 0.26 and 0.37, respectively, for the prediction of 3-year mortality. In conclusion, urinary biomarkers of kidney injury, particularly IL-18 and KIM-1, in the immediate postoperative period provide additional prognostic information for 3-year mortality risk in patients with and without clinical AKI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".