title: Using biomarkers to improve the preoperative prediction of death in coronary artery bypass graft patients.
abstract: 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 potentially 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. We prospectively followed 1731 isolated CABG patients with preoperative blood collection at eight medical centers in Northern New England for a nested case-control study from 2003-2007. Preoperative blood samples were drawn at the center and then stored at a central facility. Frozen serum was analyzed at a central laboratory on an Elecsys 2010, at the same time for Cardiac Troponin T, N-Terminal pro-Brain Natriuretic Peptide, high sensitivity C-Reactive Protein, and blood glucose. We compared the strength of the prediction model for mortality using multivariable logistic regression, goodness of fit and tested the equality of the receiving operating characteristic curve (ROC) area. There were 33 cases (dead at discharge) and 66 randomly matched controls (alive at discharge).The ROC for the preoperative mortality model was improved from .83 (95% confidence interval: .74-.92) to .87 (95% confidence interval: .80-.94) with biomarkers (p-value for equality of ROC areas .09). The addition of biomarkers to the NNE preoperative risk prediction model did not significantly improve the prediction of mortality over patient and disease characteristics alone. The added measurement of multiple biomarkers outside of preoperative risk factors may be an unnecessary use of health care resources with little added benefit for predicting in-hospital mortality.
authors.affiliation: The Dartmouth Institute for Health Policy and Clinical Practice, Dartmouth College and Dartmouth Medical School, Lebanon, New Hampshire 03756, USA. jbrown@dartmouth.edu
authors.forename: Jeremiah R
authors.initials: JR
authors.lastname: Brown
authors.forename: Todd A
authors.initials: TA
authors.lastname: MacKenzie
authors.forename: Lawrence J
authors.initials: LJ
authors.lastname: Dacey
authors.forename: Bruce J
authors.initials: BJ
authors.lastname: Leavitt
authors.forename: John H
authors.initials: JH
authors.lastname: Braxton
authors.forename: Benjamin M
authors.initials: BM
authors.lastname: Westbrook
authors.forename: Robert E
authors.initials: RE
authors.lastname: Helm
authors.forename: John D
authors.initials: JD
authors.lastname: Klemperer
authors.forename: Carmine
authors.initials: C
authors.lastname: Frumiento
authors.forename: Gerald L
authors.initials: GL
authors.lastname: Sardella
authors.forename: Cathy S
authors.initials: CS
authors.lastname: Ross
authors.forename: Gerald T
authors.initials: GT
authors.lastname: O'Connor
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015415:Biomarkers / Q000097:blood*; D001026:Coronary Artery Bypass / Q000401:mortality*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009511:New England / Q000453:epidemiology; D017063:Outcome Assessment, Health Care / Q000379:methods*; D011300:Preoperative Care / Q000379:methods* / Q000706:statistics & numerical data*; D015995:Prevalence; D011379:Prognosis; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D012680:Sensitivity and Specificity; D016019:Survival Analysis; D015996:Survival Rate
chemical_list: D015415:Biomarkers