Center for Learning and Excellence in Vascular & Endovascular Research (CLEVER), Division of Vascular and Endovascular Surgery, Department of Surgery, University of California San Diego, San Diego, CA.
Center for Learning and Excellence in Vascular & Endovascular Research (CLEVER), Division of Vascular and Endovascular Surgery, Department of Surgery, University of California San Diego, San Diego, CA.
Ann Vasc Surg. 2024 Dec;109:63-76. doi: 10.1016/j.avsg.2024.05.016. Epub 2024 Jul 14.
The gold standard for determining carotid artery stenosis intervention is based on a combination of percent stenosis and symptomatic status. Few studies have assessed plaque morphology as an additive tool for stroke prediction. Our goal was to create a predictive model and risk score for 30-day stroke and death inclusive of plaque morphology.
Patients with a computed tomographic angiography head/neck between 2010 and 2021 at a single institution and a diagnosis of carotid artery stenosis were included in our analysis. Each computed tomography was used to create a three-dimensional image of carotid plaque based off image recognition software. A stepwise backward regression was used to select variables for inclusion in our prediction models. Model discrimination was assessed with area under the receiver operating characteristic curves (AUCs). Additionally, calibration was performed and the model with the least Akaike Information Criterion (AIC) was selected. The risk score was modeled from the Framingham Study. Primary outcome was mortality/stroke.
We created 3 models to predict mortality/stroke from 366 patients: model A using only clinical variables, model B using only plaque morphology and model C using both clinical and plaque morphology variables. Model A used age, sex, peripheral arterial disease, hyperlipidemia, body mass index (BMI), chronic obstructive pulmonary disease (COPD), and history of transient ischemia attack (TIA)/stroke and had an AUC of 0.737 and AIC of 285.4. Model B used perivascular adipose tissue (PVAT) volume, lumen area, calcified volume, and target lesion length and had an AUC of 0.644 and AIC of 304.8. Finally, model C combined both clinical and software variables of age, sex, matrix volume, history of TIA/stroke, BMI, PVAT, lipid rich necrotic core, COPD and hyperlipidemia and had an AUC of 0.759 and an AIC of 277.6. Model C was the most predictive because it had the highest AUC and lowest AIC.
Our study demonstrates that combining both clinical factors and plaque morphology creates the best predication of a patient's risk for all-cause mortality or stroke from carotid artery stenosis. Additionally, we found that for patients with even 3 points in our risk score model has a 20% chance of stroke/death. Further prospective studies are needed to validate our findings.
确定颈动脉狭窄介入治疗的金标准是基于狭窄百分比和症状状态的结合。很少有研究评估斑块形态作为中风预测的附加工具。我们的目标是创建一个包含斑块形态的 30 天内中风和死亡的预测模型和风险评分。
我们的分析纳入了 2010 年至 2021 年在一家单机构进行的计算机断层血管造影头部/颈部检查并诊断为颈动脉狭窄的患者。对每例计算机断层扫描进行分析，使用图像识别软件创建颈动脉斑块的三维图像。采用逐步向后回归法选择纳入预测模型的变量。采用受试者工作特征曲线下面积（AUC）评估模型的判别能力。此外，进行了校准，并选择了 Akaike 信息准则（AIC）最小的模型。风险评分来自弗雷明汉研究。主要结局是死亡率/中风。
我们从 366 名患者中创建了 3 个预测死亡率/中风的模型：仅使用临床变量的模型 A、仅使用斑块形态的模型 B 和同时使用临床和斑块形态变量的模型 C。模型 A 使用年龄、性别、外周动脉疾病、高脂血症、体重指数（BMI）、慢性阻塞性肺疾病（COPD）和短暂性脑缺血发作（TIA）/中风史，AUC 为 0.737，AIC 为 285.4。模型 B 使用血管周围脂肪组织（PVAT）体积、管腔面积、钙化体积和靶病变长度，AUC 为 0.644，AIC 为 304.8。最后，模型 C 综合了年龄、性别、基质体积、TIA/中风史、BMI、PVAT、富含脂质的坏死核心、COPD 和高脂血症等临床和软件变量，AUC 为 0.759，AIC 为 277.6。模型 C 是最具预测性的，因为它具有最高的 AUC 和最低的 AIC。
我们的研究表明，将临床因素和斑块形态结合起来，可以更好地预测颈动脉狭窄患者全因死亡率或中风的风险。此外，我们发现即使在我们的风险评分模型中患者有 3 分，也有 20%的中风/死亡风险。需要进一步的前瞻性研究来验证我们的发现。
