Abstract
OBJECTIVE: Although various risk stratification models are available and currently being used, the performance of these models in different populations is still controversial. We aimed to investigate the relation between the Framingham and SCORE models and the presence and severity of coronary artery disease, which is detected using the SYNTAX score. METHODS: The observational cross-sectional study population consisted of 227 patients with a mean age of 63.3±9.2 years. The patients were classified into low- and high-risk groups in the Framingham and SCORE models separately. Following coronary angiography, the patients were classified into SYNTAX=0 (SYNTAX score 0), low SYNTAX (SYNTAX score 1-22), and high SYNTAX (SYNTAX score>22) groups. The relation between the risk models and SYNTAX score was evaluated by student t test, Mann-Whitney U test or Kruskal-Wallis test and Receiver operating characteristic analysis were used to detect the discrimination ability in the prediction of SYNTAX score>0 and a high SYNTAX score. RESULTS: Both the Framingham and SCORE models were found to be effective in predicting the presence of coronary artery disease, and neither of the two models had superiority over each other [AUC=0.819 (0.767, 0.871) vs. 0.811 (0.757, 0.861), p=0.881]. Furthermore, both models were also effective in predicting the extent and severity of coronary artery disease [AUC=0.724 (0.656, 0.798) vs. 0.730 (0.662, 0.802), p=0.224]. When the subgroups were analyzed, the SCORE model was found to be better in predicting coronary artery disease extent and severity in subgroups of men and diabetics {[AUC=0.737 (0.668, 0.844) vs. 0.665 (0.560, 0.790), p=0.019], [AUC=0.733 (0.684, 0.798) vs. 0.680 (0.654, 0.750) p=0.029], respectively). CONCLUSION: In addition to their role in predicting cardiovascular events, the use of the Framingham and SCORE models may also have utility in predicting the extent and severity of coronary artery disease. The SCORE risk model has a slightly better performance than the Framingham risk model.