Association with carotid plaque parameters detected on contrast-enhanced ultrasound and coronary artery plaque progression in non-culprit lesions: A retrospective study.

International journal of cardiology(2023)

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Abstract
AIM:To investigate the correlation between carotid plaque parameters detected on contrast-enhanced ultrasound(CEUS) and the plaque progression in non-culprit coronary lesions (NCCLs) after percutaneous coronary intervention (PCI). METHODS:In this retrospective cross-sectional study, we analyzed 173 patients who underwent PCI. Patients were stratified into two groups (progression and non-progression groups) by comparing the coronary angiography (CAG) results at baseline and follow-up. The correlation between carotid plaque parameters and plaque progression in NCCLs was analyzed by multivariate logistic regression analysis. A logistic regression model was established to predict NCCLs progression. RESULTS:Overall, 55 of 173 patients exhibited NCCLs progression (31.79%). Univariate comparisons showed that plaque thickness, plaque length, and IPN score were significantly higher in the progressive group than in the non-progressive group (P < 0.01). Multivariate logistic regression analysis revealed that carotid plaque length (OR = 3.418, 95% CI =1.101-10.610) and IPN score (OR = 7.395, 95% CI =3.154-17.342) were strongly associated with plaque progression in NCCLs. After adjusting for confounders, the history of previous PCI, plaque length, and IPN score were independent predictors of the NCCLs progression (P < 0.05). The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the logistic regression model in predicting the NCCLs progression were 62.50%, 90.53%, 81.12%, 76.92%, and 82.69%, respectively, and the area under the receiver operating characteristic (ROC) curve was 0.882 (95% CI: 0.826-0.939). CONCLUSIONS:Carotid plaque length and IPN score were strongly correlated with plaque progression in NCCLs. Combining the history of previous PCI can reasonably predict the NCCLs progression.
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