Study on the Value of CAC Score Combined with Clinical Characteristics in Predicting CCTA in Coronary Heart Disease
Objective To investigate the predictive value of coronary artery calcification(CAC)score for coronary CT angiography(CCTA)in patients with atypical chest pain.Methods Data from 953 patients undergoing CCTA and CAC scans for atypical chest pain were included.63 variables,including cardiovascular risk factors,CAC scores,etc.,were used to establish a random forest(RF)model.70％of the participants served as training models and 30％as validation models.The predictive performance of RF model was compared with two traditional Logistic regression models.Results The incidence of obstructive coronary heart disease was 16.4％.The subject area under characteristic was 0.841 in the radio-frequency model,0.746 in the CACS model,and 0.810 in the clinical model.RF model was significantly better than the other two models(P＜0.05).In addition,calibration curve and Hosmer-Lemesow test show that the RF model has good classification performance(P=0.556).CAC score,age,blood glucose,homocysteine,and neutrophils were the five most important variables in the RF model.Conclusion RF model is better than traditional model in predicting obstructive CAD.In clinical practice,the RF model can improve risk stratification and optimize individual management.
Random Forest ModelCoronary Artery Calcification ScoreObstructive Coronary Artery Disease
万方数据
维普
