Thin Calcification Predicts Lipid Component in Carotid Plaque-Relationship Between Lipid Distribution and Thin Calcification.

World neurosurgery(2024)

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摘要
BACKGROUND:Accurately evaluating plaque characteristics is essential because lesions with lipid-rich plaque put patients at risk of thromboembolic complications from carotid artery stenting. Near-infrared spectroscopy (NIRS) is a diagnostic imaging modality that identifies lipid components from the near-infrared absorption pattern but does not reveal the distribution of calcification. The purpose of this study was to investigate the calcification characteristics of unstable carotid plaques, focusing on relationships between the calcification characteristics revealed by computed tomography angiography and the lipid core distribution derived from NIRS. METHODS:Participants in this retrospective analysis comprised 35 patients (29 men, 6 women; mean age, 76.0 years; range, 52-89 years) who underwent carotid artery stenting in our institute between January 2021 and December 2022. We evaluated the thickness and length of carotid calcifications at the minimal lumen level from preoperative computed tomography angiography and analyzed the relationship with maximum lipid core burden index (max-LCBI) from NIRS. RESULTS:Strong negative linear correlations were observed between the thickness of calcification and max-LCBI at Area (any segment in a target lesion) (r = -0.795, P < 0.001), max-LCBI at minimal lumen area (r = -0.795, P < 0.001) and lipid core burden index (LCBI) at lesion (rate of LCBI in entire plaque lesion) (r = -0.788, P < 0.001), respectively. Significant negative linear correlations were observed between distribution of calcification length and max-LCBI at area (r = -0.429, P = 0.01), max-LCBI at minimal lumen area (r = -0.373, P = 0.027), and LCBI at lesion (r = -0.443, P = 0.008). CONCLUSIONS:Thin and ubiquitous carotid calcification was associated with LCBI values derived from NIRS indicative of carotid lipid plaque distribution, implying the possibility of predicting lesion instability.
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