Predictive ability of novel cardiac biomarkers ST2 Galectin‐3 and NT‐ProBNP before cardiac surgery
Polineni S, Parker DM, Alam SS, Thiessen-Philbrook H, McArthur E, DiScipio AW, Malenka DJ, Parikh CR, Garg AX, Brown JR. J Am Heart Assoc. 2018; 7(14):e008371. Epub 2018 Jul 7.
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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