| pubmed-article:19962465 | pubmed:abstractText | The currently available sudden cardiac death (SCD) risk prediction tools fail to identify most at-risk patients and cannot delineate a specific patient's SCD risk. We sought to develop a tool to improve the risk stratification of patients with coronary artery disease. Clinical, demographic, and angiographic characteristics were evaluated among 37,258 patients who had undergone coronary angiography from January 1, 1985 to May 31, 2005, and who were found to have at least one native coronary artery stenosis of > or =75%. After a median follow-up of 6.2 years, SCD had occurred in 1,568 patients, 14,078 patients had died from other causes, and 21,612 patients remained alive. A Cox proportional hazards model identified 10 independent patient characteristic variables significantly associated with SCD. A simplified model accounting for 97% of the predictive capacity of the full model included the following 7 variables: depressed left ventricular ejection fraction, number of diseased coronary arteries, diabetes mellitus, hypertension, heart failure, cerebrovascular disease, and tobacco use. The Duke SCD risk score was created from the simplified model to predict the likelihood of SCD among patients with coronary artery disease. It was internally validated with bootstrapping (c-index = 0.75, chi-square = 1,220.8) and externally validated in patients with ischemic cardiomyopathy from the Sudden Cardiac Death Heart Failure Trial (SCD-HeFT) database (c-index = 0.64, chi-square = 14.1). In conclusion, the Duke SCD risk score represents a simple, validated method for predicting the risk of SCD among patients with coronary artery disease and might be useful for directing treatment strategies designed to mitigate the risk of SCD. | lld:pubmed |
