Vol 10, No 4 (2004)
Research paper
Published online: 2004-11-23
Ultrasonographic morphology of carotid plaque and the risk of perioperative complications vs. the degree of carotid stenosis: a prospective controlled study
Acta Angiologica 2004;10(4):161-171.
Abstract
Background. The aim of the study was to analyze the correlation between
the degree of internal carotid artery stenosis and the carotid plaque morphology
(based on US exam), and to assess whether the degree of carotid stenosis influences
the risk of perioperative complications.
Material and methods. the analysis included 97 patients treated with carotid endarterectomy (CEA) in the Department of Vascular Surgery and Angiology. Carotid plaque morphology was assessed by pixel distribution analysis of the acquired ultrasound pictures. The plaques were further evaluated intraoperatively and sent for histopathological examination. All patients’ blood flow in the middle cerebral artery (MCA) was monitored by transcranial Doppler ultrasonography (TCD). In all necessary cases, computer tomography or nuclear magnetic resonance were performed in order to verify any suspicion of possible neurological event. The data was statistically analyzed with the application of a multivariate analysis, Pearson’s chi2 and Kruskal-Willis tests.
Results. The multivariate analysis revealed an extreme accuracy of pixel distribution analysis in the US plaque morphology assessment. The criteria for high-risk plaque regarding the perioperative complications were established (p = 0.029; OR 0,05 [95% C.I]). The correlation of this method with the intraoperative plaque assessment was very high (p < 0.005). No correlation was found between the degree of carotid stenosis and the histopathological analysis or the risk of perioperative complications.
Conclusions. The changes in the degree of carotid stenosis do not correlate with the changes in the echogenicity of the carotid plaque. The degree of stenosis is not therefore an independent risk factor related to carotid plaque morphology, and it may not prove to be an independent perioperative risk factor related directly to CEA at the preclamping stage. Detailed pixel distribution analysis of the grayscale proves to be of great value in the evaluation of the carotid plaque morphology.
Material and methods. the analysis included 97 patients treated with carotid endarterectomy (CEA) in the Department of Vascular Surgery and Angiology. Carotid plaque morphology was assessed by pixel distribution analysis of the acquired ultrasound pictures. The plaques were further evaluated intraoperatively and sent for histopathological examination. All patients’ blood flow in the middle cerebral artery (MCA) was monitored by transcranial Doppler ultrasonography (TCD). In all necessary cases, computer tomography or nuclear magnetic resonance were performed in order to verify any suspicion of possible neurological event. The data was statistically analyzed with the application of a multivariate analysis, Pearson’s chi2 and Kruskal-Willis tests.
Results. The multivariate analysis revealed an extreme accuracy of pixel distribution analysis in the US plaque morphology assessment. The criteria for high-risk plaque regarding the perioperative complications were established (p = 0.029; OR 0,05 [95% C.I]). The correlation of this method with the intraoperative plaque assessment was very high (p < 0.005). No correlation was found between the degree of carotid stenosis and the histopathological analysis or the risk of perioperative complications.
Conclusions. The changes in the degree of carotid stenosis do not correlate with the changes in the echogenicity of the carotid plaque. The degree of stenosis is not therefore an independent risk factor related to carotid plaque morphology, and it may not prove to be an independent perioperative risk factor related directly to CEA at the preclamping stage. Detailed pixel distribution analysis of the grayscale proves to be of great value in the evaluation of the carotid plaque morphology.
Keywords: unstable carotid plaqueultrasonographyGSM analysis