The impact of high-risk cases on hospitals' risk-adjusted coronary artery bypass grafting mortality rankings.

BACKGROUND: Risk-adjusted mortality (RAM) models are increasingly used to evaluate hospital performance, but the validity of the RAM method has been questioned. Providers are concerned that these methods might not adequately account for the highest levels of risk and that treating high-risk cases will have a negative impact on RAM rankings. METHODS: Using cases of isolated coronary artery bypass grafting (CABG) performed at 1002 sites in the United States participating in The Society of Thoracic Surgeons (STS) Adult Cardiac Surgery Database from 2008 to 2010 (N = 494,955), the STS CABG RAM model performance in high-risk patients was assessed. The ratios of observed to expected (O/E) perioperative mortality were compared among groups of hospitals with varying expected risks. Finally, RAM rates during the overall study period for each site were compared with its performance in a simulated "nightmare year" in which the site's highest risk cases over a 3-year period were concentrated into a 1-year period of exceptional risk. RESULTS: The average predicted mortality for center risk groups ranged from 1.46% for the lowest risk quintile to 2.87% for the highest. The O/E ratios for center risk quintiles 1 to 5 during the overall period were 1.01 (95% confidence interval, 0.96% to 1.06%), 1.00 (0.95% to 1.04%), 0.98 (0.94% to 1.03%), 0.97 (0.93% to 1.01%), and 0.80 (0.77% to 0.84%), respectively. The sites' risk-adjusted mortality rates were not increased when the centers' highest risk cases were concentrated into a single "nightmare year." CONCLUSIONS: Our results show that the current risk-adjusted models accurately estimate CABG mortality and that hospitals accepting more high-risk CABG patients have equal or better outcomes than do those with predominately lower-risk patients.

Duke Scholars

Author Sean Michael O'Brien Biostatistics & Bioinformatics, Division of Biostatistics

Author J. Matthew Brennan Medicine, Cardiology