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.