UC Davis

BACKGROUND: As US health care increasingly focuses on outcomes as a means for quantifying quality, there is a growing demand for risk models that can account for the variability of patients treated at different hospitals so that equitable comparisons between institutions can be made. We sought to apply aspects of prior risk-standardization methodology to begin development of a risk-standardization tool for the National Cardiovascular Data Registry (NCDR) IMPACT (Improving Pediatric and Adult Congenital Treatment) Registry. METHODS AND RESULTS: Using IMPACT, we identified all patients undergoing diagnostic or interventional cardiac catheterization between January 2011 and March 2013. Multivariable hierarchical logistic regression was used to identify patient and procedural characteristics predictive of experiencing a major adverse event after cardiac catheterization. A total of 19,608 cardiac catheterizations were performed between January 2011 and March 2013. Among all cases, a major adverse event occurred in 378 of all cases (1.9%). After multivariable adjustment, 8 variables were identified as critical for risk standardization: patient age, renal insufficiency, single-ventricle physiology, procedure-type risk group, low systemic saturation, low mixed venous saturation, elevated systemic ventricular end-diastolic pressure, and elevated main pulmonary artery pressures. The model had good discrimination (C statistic, 0.70), confirmed by bootstrap validation (validation C statistic, 0.69). CONCLUSIONS: Using prior risk-standardization efforts as a foundation, we developed and internally validated a model to predict the occurrence of a major adverse event after cardiac catheterization for congenital heart disease. Future efforts should be directed toward further refinement of the model variables within this large, multicenter data set.