Postoperative B-type Natriuretic Peptide for Prediction of Major Cardiac Events in Patients Undergoing Noncardiac Surgery
Bibliographic record
Abstract
BACKGROUND: It is unclear whether postoperative B-type natriuretic peptides (i.e., BNP and N-terminal proBNP) can predict cardiovascular complications in noncardiac surgery. METHODS: The authors undertook a systematic review and individual patient data meta-analysis to determine whether postoperative BNPs predict postoperative cardiovascular complications at 30 and 180 days or more. RESULTS: The authors identified 18 eligible studies (n = 2,051). For the primary outcome of 30-day mortality or nonfatal myocardial infarction, BNP of 245 pg/ml had an area under the curve of 0.71 (95% CI, 0.64-0.78), and N-terminal proBNP of 718 pg/ml had an area under the curve of 0.80 (95% CI, 0.77-0.84). These thresholds independently predicted 30-day mortality or nonfatal myocardial infarction (adjusted odds ratio [AOR] 4.5; 95% CI, 2.74-7.4; P < 0.001), mortality (AOR, 4.2; 95% CI, 2.29-7.69; P < 0.001), cardiac mortality (AOR, 9.4; 95% CI, 0.32-254.34; P < 0.001), and cardiac failure (AOR, 18.5; 95% CI, 4.55-75.29; P < 0.001). For greater than or equal to 180-day outcomes, natriuretic peptides independently predicted mortality or nonfatal myocardial infarction (AOR, 3.3; 95% CI, 2.58-4.3; P < 0.001), mortality (AOR, 2.2; 95% CI, 1.67-86; P < 0.001), cardiac mortality (AOR, 2.1; 95% CI, 0.05-1,385.17; P < 0.001), and cardiac failure (AOR, 3.5; 95% CI, 1.0-9.34; P = 0.022). Patients with BNP values of 0-250, greater than 250-400, and greater than 400 pg/ml suffered the primary outcome at a rate of 6.6, 15.7, and 29.5%, respectively. Patients with N-terminal proBNP values of 0-300, greater than 300-900, and greater than 900 pg/ml suffered the primary outcome at a rate of 1.8, 8.7, and 27%, respectively. CONCLUSIONS: Increased postoperative BNPs are independently associated with adverse cardiac events after noncardiac surgery.
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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.021 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".