目的:确定颈动脉狭窄干预的金标准是基于狭窄百分比和症状状态的组合。很少有研究将斑块形态评估为中风预测的附加工具。我们的目标是建立30天中风和死亡的预测模型和风险评分,包括斑块形态。
方法:我们的分析包括2010-2021年在单一机构进行头颈部CT血管造影并诊断为颈动脉狭窄的患者。使用每个CT来创建基于离线图像识别软件的颈动脉斑块的3D图像。使用逐步向后回归来选择包含在我们的预测模型中的变量。用受试者工作特征曲线(AUC)评估模型鉴别。此外,进行校准,并选择具有最少Akaike信息标准(AIC)的模型。风险评分来自Framingham研究。主要结果是死亡率/卒中。
结果:我们创建了三个模型来预测366名患者的死亡率/卒中:模型A仅使用临床变量,模型B仅使用斑块形态,模型C使用临床和斑块形态变量。模型A使用年龄,性别,PAD,高脂血症,BMI,COPD,和TIA/卒中病史,AUC为0.737,AIC为285.4。模型B使用血管周围脂肪组织体积,管腔面积,钙化体积,和目标病变长度,AUC为0.644,AIC为304.8。最后,模型C结合了年龄的临床和软件变量,性别,基质体积,TIA/卒中病史,BMI,血管周围脂肪组织,富含脂质的坏死核心,COPD和高脂血症的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。
结论:我们的研究表明,结合临床因素和斑块形态可以最好地预测患者因颈动脉狭窄导致全因死亡或卒中的风险。此外,我们发现,在我们的风险评分模型中,即使有3分的患者,卒中/死亡的几率也有20%.需要进一步的前瞻性研究来验证我们的发现。
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.