Predicting long-term survival after coronary artery bypass graft surgery.

Abstract

To develop a model for predicting long-term survival following coronary artery bypass graft surgery. This study included 46 573 patients from the Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZCTS) registry, who underwent isolated coronary artery bypass graft surgery between 2001 and 2014. Data were randomly split into development (23 282) and validation (23 291) samples. Cox regression models were fitted separately, using the important preoperative variables, for 4 'time intervals' (31-90 days, 91-365 days, 1-3 years and >3 years), with optimal predictors selected using the bootstrap bagging technique. Model performance was assessed both in validation data and in combined data (development and validation samples). Coefficients of all 4 final models were estimated on the combined data adjusting for hospital-level clustering. The Kaplan-Meier mortality rates estimated in the sample were 1.7% at 90 days, 2.8% at 1 year, 4.4% at 2 years and 6.1% at 3 years. Age, peripheral vascular disease, respiratory disease, reduced ejection fraction, renal dysfunction, arrhythmia, diabetes, hypercholesterolaemia, cerebrovascular disease, hypertension, congestive heart failure, steroid use and smoking were included in all 4 models. However, their magnitude of effect varied across the time intervals. Harrell's C-statistics was 0.83, 0.78, 0.75 and 0.74 for 31-90 days, 91-365 days, 1-3 years and >3 years models, respectively. Models showed excellent discrimination and calibration in validation data. Models were developed for predicting long-term survival at 4 time intervals after isolated coronary artery bypass graft surgery. These models can be used in conjunction with the existing 30-day mortality prediction model.