Department of Cardiovascular Sciences, University of Leicester, Leicester, UK.
Department of Cardiovascular Sciences, University of Leicester, Leicester, UK; NIHR Leicester Cardiovascular Biomedical Research Unit, Leicester, UK.
Eur J Vasc Endovasc Surg. 2014 Aug;48(2):118-25. doi: 10.1016/j.ejvs.2014.05.015. Epub 2014 Jun 16.
In patients with carotid stenosis the risk of stroke is highest in the first few days after onset of symptoms and it is low in asymptomatic patients. The ability to identify patients with a high (or low) probability of having a histologically unstable plaque might become a complimentary method that can refine the indications for surgical intervention.
Two histopathologists, using validated American Heart Association criteria, independently graded plaques harvested during carotid endarterectomy. Preoperative Duplex images were independently assessed for juxtaluminal black area, plaque type, plaque area, and grey-scale median (GSM) following image normalization. Logistic regression analysis was then performed to create a model for predicting predominantly histologically unstable or stable plaques.
A total of 126 patients were included in the study. Based on the presence and extent of histological features including haemorrhage, thrombus, fibrous tissue, lipid core, inflammation, neovascularity, foam cells, and cap rupture, 39 plaques were graded as predominantly stable, while 87 were predominantly unstable. Unstable plaques were associated with a plaque area >95 mm(2) (OR 4.15; 95% CI 1.34-12.8 p = .009), a juxtaluminal black area >6 mm(2) (OR 2.77; 95% CI 1.24 to 6.17 p = .01) and a GSM <25 (OR 3.76; 95% CI 1.14-12.39). Logistic regression indicated that patients with the first two features had a 90% probability of having a histologically unstable plaque. The model was used to calculate the probability of having an unstable plaque in each patient. The receiver operating characteristic curve using the p value was 0.68 (95% CI 0.59-0.78).
Computerized plaque analysis has the potential to identify patients with histologically unstable carotid plaques. This model requires validation, but offers the potential to influence patient selection for emergency interventions and the monitoring of medical therapy.
在颈动脉狭窄患者中，症状发作后的最初几天中风风险最高，而无症状患者的风险较低。识别具有高（或低）发生组织学不稳定斑块可能性的患者可能成为一种补充方法，可以完善手术干预的适应证。
两位病理学家使用经验证的美国心脏协会标准，独立对颈动脉内膜切除术切除的斑块进行分级。术前使用 Duplex 成像，通过图像归一化后独立评估管腔下黑色区域、斑块类型、斑块面积和灰度中位数（GSM）。然后进行逻辑回归分析，建立预测主要组织学不稳定或稳定斑块的模型。
共有 126 例患者纳入研究。根据包括出血、血栓、纤维组织、脂质核心、炎症、新生血管、泡沫细胞和帽破裂在内的组织学特征的存在和程度，39 个斑块被评为主要稳定型，87 个斑块被评为主要不稳定型。不稳定斑块与斑块面积>95mm²（OR 4.15；95%CI 1.34-12.8，p=0.009）、管腔下黑色区域>6mm²（OR 2.77；95%CI 1.24-6.17，p=0.01）和 GSM<25（OR 3.76；95%CI 1.14-12.39）相关。逻辑回归表明，具有前两个特征的患者发生组织学不稳定斑块的概率为 90%。该模型用于计算每位患者发生不稳定斑块的概率。使用 p 值的受试者工作特征曲线为 0.68（95%CI 0.59-0.78）。
计算机斑块分析有可能识别出具有组织学不稳定颈动脉斑块的患者。该模型需要验证，但有可能影响紧急干预和药物治疗监测的患者选择。
