摘要目的:探讨基于冠状动脉CT血管成像（CCTA）的斑块定量分析和血流储备分数（CT-FFR）在预测冠状动脉斑块进展中的作用。方法:回顾性收集2013年12月至2017年12月在江南大学附属医院行两次CCTA检查的118例患者，分为斑块进展组37例，斑块无进展组81例。将所有患者的CCTA图像进行斑块定量分析，定量指标包括狭窄程度、斑块长度、斑块总体积、钙化斑块体积、非钙化斑块体积、最小管腔面积，重塑指数（RI）、斑块负荷。斑块进展定义为斑块负荷变化率>1%。采用cFFR软件对所有患者的CCTA数据进行测量，测量位置选取在斑块远端2~4 cm的位置。斑块进展组与斑块无进展组基线参数的比较采用 t检验、U检验和卡方检验。采用logistic回归模型分析CCTA各参数与斑块进展的关系，用受试者操作特征（ROC）曲线下面积（AUC）来计算不同CCTA参数建立的预测模型的效能。 结果:与斑块无进展组患者相比，斑块进展组患者年龄更大、高血脂发病率更高、服用他汀药物比例更小。斑块进展组在基线CCTA上表现出更重的狭窄程度、更小的最小管腔面积、更大的斑块体积和非钙化斑块体积、更大的重塑指数和更低的CT-FFR值（ P<0.05）。logistic回归分析显示RI（OR=2.714， 95%CI 1.078~6.836）和CT-FFR（OR=2.940， 95%CI 1.215~7.116）是斑块进展的独立预测因素。基于CCTA狭窄程度+定量斑块特征+CT-FFR的预测模型（AUC=0.83，95%CI 0.75~0.90； P<0.001）明显优于基于CCTA狭窄程度的模型（AUC为0.62，95%CI 0.52~0.70， P=0.049）和基于CCTA狭窄程度+定量斑块特征的模型（AUC为0.77，95%CI 0.68~0.84， P<0.001）。 结论:与基于CCTA狭窄程度相比，基于CCTA的斑块定量分析和CT-FFR有助于在基线水平识别出斑块进展。重塑指数和CT-FFR是斑块进展的重要预测因子。

abstractsObjective: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%CI:1.078-6.836)and CT-FFR (OR=2.940, 95%CI:1.215-7.116) 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.