Abstract 4349: Using Biomarkers to Improve the Pre-operative Prediction of Death in Coronary Artery Bypass Graft Patients
Background: The current risk prediction models for mortality following coronary artery bypass graft (CABG) surgery have been developed on patient and disease characteristics alone. Improvements to these models may be made through the analysis of biomarkers of unmeasured risk. We hypothesize that preoperative biomarkers reflecting myocardial damage, inflammation, and metabolic dysfunction are associated with an increased risk of mortality following CABG surgery and the use of biomarkers associated with these injuries will improve the Northern New England (NNE) CABG mortality risk prediction model.
Methods: We prospectively followed 1,731 isolated CABG patients with pre-operative blood collection at 8 medical centers in the northern New England for a nested case-control study from 2003–2007. Preoperative blood samples were drawn and stored at a central facility. Frozen serum was analyzed at a central laboratory, at the same time for Cardiac Troponin T (cTnT), N-Terminal pro-Brain Natriuretic Peptide (BNP), high sensitivity C-Reactive Protein (CRP), and blood glucose. We compared the strength of the prediction model for mortality using multivariable logistic regression, goodness of fit and receiving operating curve (ROC) analysis.
Results: There were 33 cases (dead at discharge) and 66 randomly matched controls (alive at discharge). The ROC for the pre-operative mortality model was improved from 0.83 (95%CI: 0.74 – 0.92) to 0.87 (95%CI: 0.80 – 0.94) with biomarkers (p=0.09).
Conclusions: The addition of biomarkers to the NNE pre-operative risk prediction model did not significantly improve the prediction of mortality over patient and disease characteristics alone.
This research has received full or partial funding support from the American Heart Association, Founders Affiliate (Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Rhode Island, Vermont).