Assessing the outcomes of coronary artery bypass graft surgery: how many risk factors are enough? Steering Committee of the Cardiac Care Network of Ontario
JV Tu,
K Sykora,
and
CD Naylor
Institute for Clinical Evaluative Sciences in Ontario, Sunnybrook Health Science Centre, North York, Canada. tu@ices.on.ca
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.
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