The role of quantitative plaque analysis and fractional flow reserve derived from coronary CT angiography in plaque progression / 中华放射学杂志

ABSTRACT

Objective:To explore the prognostic value of quantitative plaque analysis and coronary CT angiography (CCTA) derived fractional flow reserve (CT-FFR) in evaluating plaque progression (PP).Methods:A total of 118 consecutive patients who underwent serial CCTA examinations in Affiliated Hospital of Jiangnan University from December 2013 to December 2017 were retrospectively enrolled. There were 37 patients in the PP group and 81 patients in the non-PP group. All patients′ CCTA images were quantitatively analyzed using plaque analysis software. The quantitative analysis parameters included stenosis degree, plaque length, total plaque volume, calcified plaque volume, non-calcified plaque volume, minimum lumen area, remodeling index(RI) and plaque burden. Plaque progression was defined as plaque burden change rate>1%. CT-FFR analysis was performed using cFFR software and the CT-FFR value was measured at 2-4 cm distal to the coronary lesion. Baseline parameters between the two groups were evaluated using Students t-test, U-test, chi-square test. The logistic regression model was conducted to evaluate the relationship between CCTA derived parameters and PP. Receiver operating characteristic curve analysis with the areas under the curve (AUC) was used to determine the predictive performance of different CCTA parameters. Results:Compared with the non-PP group, the patients were older( t=2.391, P=0.018), the prevalence of hyperlipidemia was higher(χ2=4.550, P=0.033), and the proportion of statins use was lower (χ2=4.764, P=0.029) in the PP group. The PP group showed greater coronary stenosis, smaller minimum lumen area, larger plaque volume and non-calcified plaque volume, larger remodeling index and lower CT-FFR value on baseline CCTA (all P<0.05). Logistic regression analysis demonstrated that RI(OR=2.714, 95%CI1.078-6.836)and CT-FFR (OR=2.940, 95%CI1.215-7.116) were independent predictors of PP. The model based on CCTA stenosis degree, quantitative plaque features and CT-FFR (AUC 0.83, 95%CI 0.75-0.90; P<0.001) was significantly better than the model based on CCTA stenosis degree (AUC 0.62, 95%CI 0.52-0.70, P=0.049) and the model based on CCTA stenosis degree and quantitative plaque characteristics (AUC 0.77, 95%CI 0.68-0.84, P<0.001). Conclusions:Compared with the prediction model derived on stenosis degree, plaque quantitative markers and CT-FFR can improve the prediction value of PP.RI and CT-FFR were important predictors of PP.