Predictors of long-term survival after coronary artery bypass grafting surgery: results from the Society of Thoracic Surgeons Adult Cardiac Surgery Database (the ASCERT study).

BACKGROUND: Most survival prediction models for coronary artery bypass grafting surgery are limited to in-hospital or 30-day end points. We estimate a long-term survival model using data from the Society of Thoracic Surgeons Adult Cardiac Surgery Database and Centers for Medicare and Medicaid Services. METHODS AND RESULTS: The final study cohort included 348 341 isolated coronary artery bypass grafting patients aged ≥65 years, discharged between January 1, 2002, and December 31, 2007, from 917 Society of Thoracic Surgeons-participating hospitals, randomly divided into training (n=174 506) and validation (n=173 835) samples. Through linkage with Centers for Medicare and Medicaid Services claims data, we ascertained vital status from date of surgery through December 31, 2008 (1- to 6-year follow-up). Because the proportional hazards assumption was violated, we fit 4 Cox regression models conditional on being alive at the beginning of the following intervals: 0 to 30 days, 31 to 180 days, 181 days to 2 years, and >2 years. Kaplan-Meier-estimated mortality was 3.2% at 30 days, 6.4% at 180 days, 8.1% at 1 year, and 23.3% at 3 years of follow-up. Harrell's C statistic for predicting overall survival time was 0.732. Some risk factors (eg, emergency status, shock, reoperation) were strong predictors of short-term outcome but, for early survivors, became nonsignificant within 2 years. The adverse impact of some other risk factors (eg, dialysis-dependent renal failure, insulin-dependent diabetes mellitus) continued to increase. CONCLUSIONS: Using clinical registry data and longitudinal claims data, we developed a long-term survival prediction model for isolated coronary artery bypass grafting. This provides valuable information for shared decision making, comparative effectiveness research, quality improvement, and provider profiling.

Duke Scholars

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

Author Elizabeth Ray DeLong Biostatistics & Bioinformatics, Division of Biostatistics