Assessment of coronary computed tomography angiography-derived plaque features in the diagnosis of optical coherence tomography-defined vulnerable plaques

冠状动脉计算机断层扫描血管造影衍生斑块特征在光学相干断层扫描定义的易损斑块诊断中的评估

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Abstract

BACKGROUND: Identification of vulnerable plaque is essential for pre-estimation of the risk of cardiovascular disease (CVD) and stratification of major adverse cardiac events (MACEs) risks. This study aimed to evaluate the diagnostic ability of coronary computed tomography angiography (CCTA)-derived qualitative and quantitative plaque features in detecting optical coherence tomography (OCT)-defined vulnerable plaques. METHODS: A total of 31 patients who underwent both CCTA and OCT were retrospectively included in this study. The results of OCT and CCTA were blindly analyzed on a segment-to-segment comparison. The qualitative and quantitative plaque parameters derived by CCTA were recorded. Univariate analysis and multivariate logistic regression analysis were performed to reveal the independent predictors. The diagnostic efficacy of quantitative parameters was evaluated by receiver operating characteristic (ROC) curve and area under the curve (AUC). RESULTS: A total of 76 plaques in 31 patients were included for analysis, of which 15/76 plaques (19.7%, 10 patients) were vulnerable plaques. Low-density plaques, spotty calcification (SC), positive remodeling (PR), number of high-risk plaque signs, non-calcified fraction, and lipid fraction displayed significant differences between vulnerable and non-vulnerable plaques (P<0.05). Fibrotic plaque volume (FPV) [odds ratio (OR) =1.013; 95% confidence interval (CI): 1.003-1.024] and low attenuation plaque (LAP) (OR =23.416; 95% CI: 4.725-116.055) were shown to be independent predictors of vulnerable plaques. Compared with qualitative and quantitative models, the mixed model integrating all significant CCTA-derived plaque characteristics achieved the highest AUC and accuracy (mixed model AUC =0.87, 95% CI: 0.808-0.979; qualitative model AUC =0.80, 95% CI: 0.654-0.941; quantitative model AUC =0.64, 95% CI: 0.528-0.866). CONCLUSIONS: The CCTA-derived plaque characteristics were able to detect the OCT-defined vulnerable plaques and show great potential as a non-invasive biomarker for early diagnosis of coronary vulnerable plaques.

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