Plaque Characterization Analysis Based On Coronary CTA Combined With FFR_CT And FAI On Plaque Progression

Objective（s）:To investigate the value of plaque features,blood flow reserve fraction(FFR_CT)and pericoronary fat attenuation index（FAI）on coronary computed tomography angiography（CCTA）in assessing coronary plaque progression.Methods:Patients who underwent two or more CCTA examinations from January2015 to January 2022 were included and were divided into plaque progression group（annual TPB change rate>1%）and plaque-free group（annual TPB change rate≤1%）according to whether the annual total plaque load（TPB）change rate was>1%.The clinical baseline data of patients were collected,and the CCTA images of all patients were imported into the post-processing software,and the parameters such as plaque length,luminal stenosis,total volume of plaque（TPV）,volume of calcified plaque（CPV）,volume of non-calcified plaque（NCPV）,minimum luminal area（MLA）,remodeling index（RI）,total plaque load（TPB）,calcified plaque load（CPB）,non-calcified plaque load（NCPB）,total vascular volume,FFR_CT and pericoronary FAI were measured.The t-test,χ²test,and Wilcoxon rank and test were used to compare the differences between the above parameters.The parameters of P<0.05 compared between groups were included in binary logistic regression analysis to determine the independent influencing factors of plaque progression.The performance of each model in predicting plaque progression was evaluated by the receiver operating characteristic（ROC）curve.The Delong test was used to evaluate the difference in the prediction performance of each model.P<0.05 The difference was considered statistically significant.Results:Finally,112 patients were enrolled,including 61 cases in the plaque progression group and 51 cases in the plaque-free progression group.（1）The comparison of clinical baseline data of patients in the two groups showed that the proportion of diabetes mellitus and the proportion of taking statins were statistically significant between the two groups,and compared with the plaque-free group,the proportion of patients with diabetes in the plaque progression group was higher（χ²=5.566,P=0.018）,the proportion of taking statins was smaller（χ²=4.263,P=0.039）,and the remaining indicators were not statistically significant between the two groups（P>0.05）.（2）The comparison of plaque characteristics on baseline CCTA in the two groups showed that the difference in plaque length,stenosis,MLA,TPB,NCPB and RI between the two groups was statistically significant,and compared with the plaque-free group,the plaque length of the plaque progression group was longer（Z=2.507,P=0.012）,the degree of stenosis was greater（Z=3.962,P<0.001）,the MLA was smaller（Z=5.502,P<0.001）,TPB（Z=3.655,P<0.001）,NCPB was larger（t=2.501,P=0.014）,RI was higher（Z=4.558,P<0.001）,and there was no significant difference between the remaining indexes（P>0.05）.（3）There was a statistically significant difference in FFR_CT values（Z=6.545,P<0.001）in baseline CCTA between the two groups,and smaller FFR_CT values in the plaque progression group.（4）The difference in FAI value（t=4.466,P<0.001）on baseline CCTA between the two groups was statistically significant,and the FAI value was greater in the plaque progression group.（5）Binary logistic regression analysis showed that total plaque load（OR=0.137,95%CI 0.024～0.796,P=0.027）,RI（OR=9.032,95%CI 2.138～38.159,P=0.003）,FFR_CT（OR=8.272,95%CI 1.769～38.685,P=0.007）and FAI（OR=10.462,95%CI2.741～39.924,P=0.001）was an independent risk factor for plaque progression.（6）The AUC results showed that the AUC was model 3:CCTA-based plaque feature+FAI+FFRCT model（AUC=0.907,P<0.001）,model 2:CCTA-based plaque feature+FAI model（AUC=0.825,P<0.001）,model 1:CCTA-based plaque feature model（AUC=0.771,P<0.001）.Further Delong test showed that the AUC difference between model 1 and model 3（Z=3.502,P<0.001）and model 2 and model 3（Z=2.760,P=0.006）was statistically significant,while the AUC difference between model 1 and model 2（Z=1.867,P=0.062）was not statistically significant.Conclusion（s）:（1）Patients with diabetes and no statins are more likely to have plaque progression.（2）TPB,RI,FFR_CT,and FAI were independent predictors of plaque progression.（3）After combining the plaque features based on CCTA with FAI and FFR_CT,the joint model had better predictive performance for plaque progression than a single plaque feature index.