Preoperative Serum Brain Natriuretic Peptide and Risk of Acute Kidney Injury After Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) after cardiac surgery is associated with poor outcomes and is difficult to predict. We conducted a prospective study to evaluate whether preoperative brain natriuretic peptide (BNP) levels predict postoperative AKI among patients undergoing cardiac surgery. METHODS AND RESULTS: The Translational Research Investigating Biomarker Endpoints in Acute Kidney Injury (TRIBE-AKI) study enrolled 1139 adults undergoing cardiac surgery at 6 hospitals from 2007 to 2009 who were selected for high AKI risk. Preoperative BNP was categorized into quintiles. AKI was common with the use of Acute Kidney Injury Network definitions; at least mild AKI was a ≥0.3-mg/dL or 50% rise in creatinine (n=407, 36%), and severe AKI was either a doubling of creatinine or the requirement of acute renal replacement therapy (n=58, 5.1%). In analyses adjusted for preoperative characteristics, preoperative BNP was a strong and independent predictor of mild and severe AKI. Compared with the lowest BNP quintile, the highest quintile had significantly higher risk of at least mild AKI (risk ratio, 1.87; 95% confidence interval, 1.40-2.49) and severe AKI (risk ratio, 3.17; 95% confidence interval, 1.06-9.48). After adjustment for clinical predictors, the addition of BNP improved the area under the curve to predict at least mild AKI (0.67-0.69; P=0.02) and severe AKI (0.73-0.75; P=0.11). Compared with clinical parameters alone, BNP modestly improved risk prediction of AKI cases into lower and higher risk (continuous net reclassification index; at least mild AKI: risk ratio, 0.183; 95% confidence interval, 0.061-0.314; severe AKI: risk ratio, 0.231; 95% confidence interval, 0.067-0.506). CONCLUSIONS: Preoperative BNP level is associated with postoperative AKI in high-risk patients undergoing cardiac surgery. If confirmed in other types of patients and surgeries, preoperative BNP may be a valuable component of future efforts to improve preoperative risk stratification and discrimination among surgical candidates.
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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 | 0.001 | 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".