Predictive Ability of Novel Cardiac Biomarkers ST2, Galectin‐3, and NT‐ProBNP Before Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Current preoperative models use clinical risk factors alone in estimating risk of in-hospital mortality following cardiac surgery. However, novel biomarkers now exist to potentially improve preoperative prediction models. An assessment of Galectin-3, N-terminal pro b-type natriuretic peptide (NT-ProBNP), and soluble ST2 to improve the predictive ability of an existing prediction model of in-hospital mortality may improve our capacity to risk-stratify patients before surgery. METHODS AND RESULTS: We measured preoperative biomarkers in the NNECDSG (Northern New England Cardiovascular Disease Study Group), a prospective cohort of 1554 patients undergoing coronary artery bypass graft surgery. Exposures of interest were preoperative levels of galectin-3, NT-ProBNP, and ST2. In-hospital mortality and adverse events occurring after coronary artery bypass graft were the outcomes. After adjustment, NT-ProBNP and ST2 showed a statistically significant association with both their median and third tercile categories with NT-ProBNP odds ratios of 2.89 (95% confidence interval [CI]: 1.04-8.05) and 5.43 (95% CI: 1.21-24.44) and ST2 odds ratios of 3.96 (95% CI: 1.60-9.82) and 3.21 (95% CI: 1.17-8.80), respectively. The model receiver operating characteristic score of the base prediction model (0.80 [95% CI: 0.72-0.89]) varied significantly from the new multi-marker model (0.85 [95% CI: 0.79-0.91]). Compared with the Northern New England (NNE) model alone, the full prediction model with biomarkers NT-proBNP and ST2 shows significant improvement in model classification of in-hospital mortality. CONCLUSIONS: This study demonstrates a significant improvement of preoperative prediction of in-hospital mortality in patients undergoing coronary artery bypass graft and suggests that biomarkers can be used to identify patients at higher risk.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".