Monday, October 20, 2014: 10:15 AM

Jarrod E. Dalton, PhD1, Jesse D. Schold, PhD1, Michael W. Kattan, PhD1, Daniel I. Sessler, MD1 and Neal V. Dawson, MD2, (1)Cleveland Clinic, Cleveland, OH, (2)Case Western Reserve University at MetroHealth Medical Center, Cleveland, OH


The decision to perform coronary artery bypass grafting (CABG) or percutaneous coronary intervention (PCI) is complex and depends on many factors, including age, disease morphology, and patient comorbidities. Previously, we developed a prediction model which used patients' age, sex, year of treatment, and present-on-admission diagnosis codes to generate predicted probabilities of in-hospital mortality with each treatment. The model recommended whichever treatment minimized the predicted probability of in-hospital mortality. Management was deemed discordant with the model when the actual treatment differed from the model-recommended treatment. In this study, we evaluated the association between hospital discordance rates (HDRs) and risk-adjusted in-hospital mortality.


We analyzed 550,958 discharges with either PCI or CABG as a primary procedure, using the 2009-11 AHRQ State Inpatient Databases for six states (AZ, CA, FL, MD, MI, NJ). Hospitals were included if they had >300 total such discharges; those not performing CABG were excluded. Hospital observed-to-expected (O/E) in-hospital mortality ratios were modeled using mixed effects logistic regression with random intercepts. Expected mortality was defined as the predicted probability of mortality under the recommended treatment, under the hypothesis that increasing HDR is associated with increasing O/E ratios. As a negative control, we redefined expected mortality as the predicted probability of mortality under the actual treatment administered, under the hypothesis that, using this definition, there is no association between HDR and O/E ratio.


283 hospitals met inclusion criteria. HDRs ranged from 5.2% to 61.9%, with a mean (SD) of 29.7% (8.0%). Using recommended treatment to define expected mortality rates, we found a significant association between HDR and hospital O/E ratio (see Figure): each increase of HDR of 10% associated with a factor [95% CI] of 1.12 [1.05, 1.19] change in hospital O/E ratio. In the negative control analysis, there was no such association: the change in O/E ratio associated with a 10% increase in HDR was a factor of 1.02 [0.96, 1.08].


We found heterogeneity in decision making among hospitals at least in comparison with model recommendations which was significantly associated with risk-adjusted mortality. Large-scale decision models, if accurate and calibrated, may help improve outcomes by empirically expressing risk under competing treatments, for patients' specific sets of comorbidities.