Assessing the Outcomes of Coronary Artery Bypass Graft Surgery: How Many Risk Factors Are Enough? fn1fn1Dr. Naylor is supported by a Career Scientist Award from the Ontario Ministry of Health, Toronto, Ontario, Canada. This work was supported by an operating grant from the Sunnybrook Trust for Medical Research, North York, Ontario, Canada.fn2fn2To discuss this article on-line, visit the ACC Home page at www.acc.org/membersand click on the JACC Forum

OBJECTIVES: We sought to determine whether more comprehensive risk-adjustment models have a significant impact on hospital risk-adjusted mortality rates after coronary artery bypass graft surgery (CABG) in Ontario, Canada. BACKGROUND: The Working Group Panel on the Collaborative CABG Database Project has categorized 44 clinical variables into 7 core, 13 level 1 and 24 level 2 variables, to reflect their relative importance in determining short-term mortality after CABG. METHODS: Using clinical data for all 5,517 patients undergoing isolated CABG in Ontario in 1993, we developed 12 increasingly comprehensive risk-adjustment models using logistic regression analysis of 6 of the Panel's core variables and 6 of the Panel's level 1 variables. We studied how the risk-adjusted mortality rates of the nine cardiac surgery hospitals in Ontario changed as more variables were included in these models. RESULTS: Incorporating six of the core variables in a risk-adjustment model led to a model with an area under the receiver operating characteristic (ROC) curve of 0.77. The ROC curve area slightly improved to 0.79 with the inclusion of six additional level 1 variables (p = 0.063). Hospital risk-adjusted mortality rates and relative rankings stabilized after adjusting for six core variables. Adding an additional six level 1 variables to a risk-adjustment model had minimal impact on overall results. CONCLUSIONS: A small number of core variables appear to be sufficient for fairly comparing risk-adjusted mortality rates after CABG across hospitals in Ontario. For efficient interprovider comparisons, risk-adjustment models for CABG could be simplified so that only essential variables are included in these models.