Exploring the Utility of the Reynolds and Framingham Risk Stratification Models in Predicting the Presence and Severity of Coronary Artery Disease in Chinese Patients

Coronary artery disease (CAD) is one of the leading causes of mortality and morbidity worldwide. Thus, a simple and practical method to identify it is urgently needed. This study aims to explore the correlation between the Reynolds and Framingham risk scores and the Gensini score (GS), along with their utility in predicting the presence and severity of CAD.This research represents a single-center retrospective study. A total of 13,824 Chinese patients were enrolled in our study. GS was used to assess and group the presence and severity of CAD. The Spearman rank test and the logistic regression analysis were then performed to explore the correlation between the Reynolds/Framingham risk scores and the GS. The receiver-operating characteristic curve analysis was used to evaluate the performance of the Reynolds and Framingham risk stratification models.Both the Reynolds and Framingham risk scores showed statistically significant positive correlations with the presence (r (Reynolds): 0.179; r (Framingham): 0.182) and severity (r (Reynolds): 0.232; r (Framingham): 0.259) of CAD. Both scores had statistically significant powers of predicting the presence (cut-off value [Reynolds]: 4.20%; cut-off value [Framingham]: 12.33%) and severity (cut-off value [Reynolds]: 8.94%; cut-off value [Framingham]: 20.59%) of CAD. The Reynolds risk score showed a better performance compared to the Framingham risk score for both the presence (Reynolds area under the curve (AUC): 0.649 versus Framingham AUC: 0.637 P < 0.05) and severity (Reynolds AUC: 0.656 versus Framingham AUC: 0.645 P < 0.05) of CAD.Our study suggests that the Reynolds and Framingham risk scores can be used to predict the presence and severity of CAD in the Chinese population. The Reynolds risk score showed great superiority in the women's group, while the Framingham risk score had a better performance in predicting severity as a whole.