Relationship of Kidney Injury Biomarkers with Long-Term Cardiovascular Outcomes after Cardiac Surgery
Bibliographic record
Abstract
Clinical AKI, measured by serum creatinine elevation, is associated with long-term risks of adverse cardiovascular (CV) events and mortality in patients after cardiac surgery. To evaluate the relative contributions of urine kidney injury biomarkers and plasma cardiac injury biomarkers in adverse events, we conducted a multicenter prospective cohort study of 968 adults undergoing cardiac surgery. On postoperative days 1–3, we measured five urine biomarkers of kidney injury (IL-18, NGAL, KIM-1, L-FABP, and albumin) and five plasma biomarkers of cardiac injury (NT-proBNP, H-FABP, hs-cTnT, cTnI, and CK-MB). The primary outcome was a composite of long-term CV events or death, which was assessed via national health care databases. During a median 3.8 years of follow-up, 219 (22.6%) patients experienced the primary outcome (136 CV events and 83 additional deaths). Compared with patients without postsurgical AKI, patients who experienced AKI Network stage 2 or 3 had an adjusted hazard ratio for the primary composite outcome of 3.52 (95% confidence interval, 2.17 to 5.71). However, none of the five urinary kidney injury biomarkers were significantly associated with the primary outcome. In contrast, four out of five postoperative cardiac injury biomarkers (NT-proBNP, H-FABP, hs-cTnT, and cTnI) strongly associated with the primary outcome. Mediation analyses demonstrated that cardiac biomarkers explained 49% (95% confidence interval, 1% to 97%) of the association between AKI and the primary outcome. These results suggest that clinical AKI at the time of cardiac surgery is indicative of concurrent CV stress rather than an independent renal pathway for long-term adverse CV outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".