Details

Autor(en) / Beteiligte

• 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

Titel

Relationship between abdominal aortic and coronary artery calcification as detected by computed tomography in chronic kidney disease patients

Ist Teil von

• Heart and vessels, 2016-07, Vol.31 (7), p.1030-1037

Ort / Verlag

Tokyo: Springer Japan

Erscheinungsjahr

2016

Link zum Volltext

Quelle

SpringerLink

Beschreibungen/Notizen

• 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.