Prediction Of Myocardial Infarction Based On Multiple Functional Parameters Of Computed Coronary Angiography

Objective To explore the risk factors of CCTA parameters in patients with myocardial infarction(MI),including stenosis degree,qualitative and quantitative characteristics of plaque,fractional flow reserve(FFR)and fat attenuation index(FAI)based on CCTA,and establish a model,and finally observe the effect of the model on the prediction of myocardial infarction(MI).Methods Patients admitted to our hospital from January 2020 to June 2022 who experienced CCTA were collected.Patients diagnosed with MI were defined as the case group,while those without MI were 1:1 propensity score matched in the control group.The degree of coronary artery stenosis,qualitative and quantitative characteristics of plaque,flow reserve fraction(FFR-CT)and fat attenuation index(FAI)obtained by CT angiography were compared between the two groups.The independent risk factors for MI prediction were obtained through univariate and multivariate regression analysis,and then a multi-parameter model was established to predict MI.Independent risk factors were obtained by univariate and multivariate regression analysis,and multi-parameter models were established.Results A total of 150 patients were successfully analyzed.There was no significant difference in baseline data between case group and control group(all P values were > 0.05 except HDL).The results showed that Gensini score,quantitative and qualitative characteristics of plaques,FFR-CT and FAI were significantly different between the MI group and the control group.The combined predictive values of coronary artery stenosis degree,qualitative and quantitative characteristics of plaque and coronary hemodynamics were(0.879(0.826-0.933),P<0.001)and(0.967(0.949-0.984,P<0.001).This combined model was the best predictor of myocardial infarction.The independent risk factors for predicting myocardial infarction derived from the model in this study were Gensini score,low-density plaque,positive remodeling and FFR-CT.Conclusions The one-stop multi-parameter CCTA model with Gensini score,low-density plaque,positive remodeling and FFR-CT as independent risk factors has a good predictive effect on myocardial infarction.The multi-parameter combination model can improve the MI prediction performance,so it represents a reliable MI prediction model.