Adar Adem, Erkan Hakan, Gokdeniz Tayyar, Karadeniz Aysegul, Cavusoglu Ismail G, Onalan Orhan
Karabuk University Hospital, Department of Cardiology, Turkey.
Ahi Evren Thoracic and Cardiovascular Surgery Training and Research Hospital, Department of Cardiology, Turkey.
Vasa. 2015 Mar;44(2):106-14. doi: 10.1024/0301-1526/a000415.
We aimed to investigate the association between aortic arch and coronary artery calcification (CAC). We postulated that low- and high-risk CAC scores could be predicted with the evaluation of standard chest radiography for aortic arch calcification (AAC).
Consecutive patients who were referred for a multidetector computerized tomography (MDCT) examination were enrolled prospectively. All patients were scanned using a commercially available 64-slice MDCT scanner for the evaluation of CAC score. A four-point grading scale (0, 1, 2 and 3) was used to evaluate AAC on the standard posterior-anterior chest radiography images.
The study group consisted of 248 patients. Median age of the study group was 52 (IQR: 10) years, and 165 (67 %) were male. AAC grades (r = 0.676, p < 0.0001) and age (r = 0.518, p < 0.0001) were significantly and positively correlated with CAC score. Presence of AAC was independently associated with the presence of CAC (OR: 11.20, 95 % CI 4.25 to 29.52). An AAC grade of ≥ 2 was the strongest independent predictor of a high-risk CAC score (OR: 27.42, 95 % CI 6.09 to 123.52). Receiver operating characteristics curve analysis yielded a strong predictive ability of AAC grades for a CAC score of ≥ 100 (AUC = 0.892, P < 0.0001), and ≥ 400 (AUC = 0.894, P < 0.0001). Absence of AAC had a sensitivity, specificity and accuracy of 90 %, 84 % and 89 %, respectively, for a CAC score of < 100. An AAC grade of ≥ 2 predicted a CAC score of ≥ 400 with a sensitivity, specificity and accuracy of 68 %, 98 % and 95 %, respectively.
AAC is a strong and independent predictor of CAC. The discriminative performance of AAC is high in detecting patients with low- and high-risk CAC scores.
我们旨在研究主动脉弓钙化与冠状动脉钙化(CAC)之间的关联。我们推测,通过评估主动脉弓钙化(AAC)的标准胸部X线片,可以预测低风险和高风险的CAC评分。
前瞻性纳入连续接受多排螺旋计算机断层扫描(MDCT)检查的患者。所有患者均使用市售的64层MDCT扫描仪进行扫描,以评估CAC评分。采用四点分级量表(0、1、2和3)在标准后前位胸部X线片图像上评估AAC。
研究组由248例患者组成。研究组的中位年龄为52岁(四分位间距:10岁),男性165例(67%)。AAC分级(r = 0.676,p < 0.0001)和年龄(r = 0.518,p < 0.0001)与CAC评分显著正相关。AAC的存在与CAC的存在独立相关(比值比:11.20,95%置信区间4.25至29.52)。AAC分级≥2是高风险CAC评分的最强独立预测因素(比值比:27.42,95%置信区间6.09至123.52)。受试者工作特征曲线分析显示,AAC分级对CAC评分≥100(曲线下面积 = 0.892,P < 0.0001)和≥400(曲线下面积 = 0.894,P < 0.0001)具有较强的预测能力。对于CAC评分<100,无AAC的敏感度、特异度和准确度分别为90%、84%和89%。AAC分级≥2预测CAC评分≥400的敏感度、特异度和准确度分别为68%、98%和95%。
AAC是CAC的一个强有力的独立预测因素。AAC在检测低风险和高风险CAC评分患者方面具有较高的鉴别性能。