Acute Kidney Injury After Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) after cardiac surgery is a major health issue. Lacking effective therapies, risk factor modification may offer a means of preventing this complication. The objective of the present study was to identify and determine the prognostic importance of such risk factors. METHODS AND RESULTS: Data from a multicenter cohort of 3500 adult patients who underwent cardiac surgery at 7 hospitals during 2004 were analyzed (using multivariable logistic regression modeling) to determine the independent relationships between 3 thresholds of AKI (>25%, >50%, and >75% decrease in estimated glomerular filtration rate within 1 week of surgery or need for postoperative dialysis) with death rates, as well as to identify modifiable risk factors for AKI. The 3 thresholds of AKI occurred in 24% (n=829), 7% (n=228), and 3% (n=119) of the cohort, respectively. All 3 thresholds were independently associated with a >4-fold increase in the odds of death and could be predicted with several perioperative variables, including preoperative intra-aortic balloon pump use, urgent surgery, and prolonged cardiopulmonary bypass. In particular, 3 potentially modifiable variables were also independently and strongly associated with AKI. These were preoperative anemia, perioperative red blood cell transfusions, and surgical reexploration. CONCLUSIONS: AKI after cardiac surgery is highly prevalent and prognostically important. Therapies aimed at mitigating preoperative anemia, perioperative red blood cell transfusions, and surgical reexploration may offer protection against this complication.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".