Abstract
OBJECTIVE: The current risk model for long-term prediction in coronary artery disease (CAD) is complicated, while a simple useful model is still lacking. We aim to investigate if CHADS(2) and R(2)CHADS(2) scores could predict long-term outcome for patients with CAD. PATIENTS AND METHODS: We enrolled 3,700 patients with CAD between November 2010 and September 2014 at the Department of Cardiology from Chinese PLA General Hospital. The CHADS(2) and R(2)CHADS(2) scores were calculated. All cases were followed to track the incidence of composite end point consisting of cardiovascular (CV) death, myocardial infarction (MI), stroke, heart failure, and all-cause death. RESULTS: During a median 2.9-year follow-up, 443 patients experienced at least one element of the composite end point of CV death (n=168 [4.6%]), MI (n=59 [1.6%]), stroke (n=96 [2.6%]), heart failure (n=101 [2.8%]), and all-cause death (n=240 [6.6%]). Multivariate Cox regression analyses showed that the CHADS(2) score (hazard ratio [HR]: 2.18, 95% CI: 2.00-2.38, p<0.0001) and the R(2)CHADS(2) score (HR: 1.93, 95% CI: 1.83-2.04, p<0.0001) were independently associated with composite outcome. Receiver-operating characteristic analysis showed that compared with the CHADS(2) score, the R(2)CHADS(2) score had better discrimination for the prediction of long-term combined outcome (0.772 vs 0.791, p=0.0013). CONCLUSION: CHADS(2) and R(2)CHADS(2) scores provide a quick and useful tool in predicting long-term outcome for patients with CAD.