Takayama Yohei, Yasuda Yoshinari, Suzuki Susumu, Shibata Yohei, Tatami Yosuke, Shibata Kanako, Niwa Misao, Sawai Akihiro, Morimoto Ryota, Kato Sawako, Ishii Hideki, Maruyama Shoichi, Murohara Toyoaki
Department of Cardiology, Nagoya University Graduate School of Medicine, 65, Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan.
Department of CKD Initiatives Internal Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Heart Vessels. 2016 Jul;31(7):1030-7. doi: 10.1007/s00380-015-0712-y. Epub 2015 Jul 12.
The purpose of this study was to investigate the relationship between abdominal aortic calcification (AAC) and coronary artery calcification (CAC) in chronic kidney disease (CKD) patients. We evaluated 126 asymptomatic CKD patients (mean estimated glomerular filtration rate: 36.1 ± 14.1 mL/min/1.73 m(2), mean age 70.3 ± 10.1 years). A non-contrast computed tomography scan was used to determine the abdominal aortic calcification index (ACI) and CAC score, and this relationship was investigated. Among the subjects, AAC was present in 109 patients (86.5 %) as defined by ACI >0 and median ACI was 11.7 %. ACI increased in accordance with advances in CAC score grades (3.0, 5.2, 17.2, and 32.8 % for CAC score 0, 1-100, 101-400, and 401 or more, respectively, p < 0.001). Even after multivariate adjustment, ACI was independently associated with severe CAC score as defined by CAC score >400 [odds ratio 1.08, 95 % confidence interval (CI) 1.04-1.12, p < 0.001]. Receiver-operating curve analysis showed that the ACI optimal cut-off value predicting severe CAC score was 16.5 % (area under the curve = 0.79, 95 % CI 0.69-0.90, p < 0.001). The C statics for predicting CAC score was significantly increased by adding ACI values to the model including other risk factors (0.853 versus 0.737, p = 0.023). In conclusion, the ACI value of 16.5 % allows us to predict the presence of severe CAC in CKD patients, and that the addition of ACI to the model with traditional risk factors significantly improves the predictive ability of severe CAC score. These data reinforce the utility of ACI as a screening tool in clinical practice.
本研究旨在探讨慢性肾脏病(CKD)患者腹主动脉钙化(AAC)与冠状动脉钙化(CAC)之间的关系。我们评估了126例无症状CKD患者(平均估算肾小球滤过率:36.1±14.1 mL/min/1.73 m²,平均年龄70.3±10.1岁)。采用非增强计算机断层扫描来确定腹主动脉钙化指数(ACI)和CAC评分,并对这种关系进行研究。在研究对象中,根据ACI>0定义,109例患者(86.5%)存在AAC,中位ACI为11.7%。ACI随着CAC评分等级的升高而增加(CAC评分为0、1 - 100、101 - 400以及401及以上时,ACI分别为3.0%、5.2%、17.2%和32.8%,p<0.001)。即使经过多变量调整,ACI仍与CAC评分>400所定义的重度CAC独立相关[优势比1.08,95%置信区间(CI)1.04 - 1.12,p<0.001]。受试者工作特征曲线分析显示,预测重度CAC评分的ACI最佳截断值为16.5%(曲线下面积 = 0.79,95%CI 0.69 - 0.90,p<0.001)。将ACI值添加到包含其他危险因素的模型中,预测CAC评分的C统计量显著增加(分别为0.853和0.737,p = 0.023)。总之,16.5%的ACI值能够使我们预测CKD患者中重度CAC的存在,并且在传统危险因素模型中加入ACI可显著提高重度CAC评分的预测能力。这些数据强化了ACI作为临床实践中筛查工具的实用性。