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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".