Coronary Artery Calcification Score As the Determinant of Coronary Artery Disease in Chronic Kidney Disease Patients: A Preliminary Study Publisher



Moradi M¹ ; Talebi A¹ ; Shavakhi S² ; Tarrahi MJ³ ; Meraji Far F⁴
Source: ARYA Atherosclerosis Published:2024

Abstract

BACKGROUND: Coronary computed tomography angiography (CCTA) is a noninvasive cardiovascular imaging procedure that visualizes coronary artery calcifications (CAC), a marker of subclinical atherosclerosis. Due to different calcification patterns in patients with chronic kidney disease (CKD) compared to the general population, this study aims to present diagnostic cut-off values for CAC to detect early coronary artery disease (CAD) in CKD patients. METHODS: This cross-sectional study included 807 patients: 407 with CKD and 400 controls with normal kidney function who underwent CCTA during 2019-2021. CAC score measurements were performed for all left main coronary arteries to investigate CAD. The Coronary Artery Disease Reporting and Data System (CAD-RADS) was used as the gold standard to determine the value of CAC, and diagnostic values were measured. RESULTS: The number of female patients was 443 (54.9%), and 364 (45.1%) were male. The mean age in the case group was 63.95 ± 10.26 years, and in the control group, it was 53.80 ± 11.84 years. At the cut-off point of 85, the CAC score had a sensitivity and specificity of 84.7% and 83%, respectively, among patients with CKD to detect CAD (Area Under the Curve (AUC): 0.919, 95% CI: 0.89-0.94; P-value < 0.001). Considering a cut-point of 85 for CAC, the frequency of healthy subjects with CAD-RADS less than two was significantly higher than the cases (P-value = 0.012), while the two groups were similar regarding CAD-RADS 3-5 (P-value = 0.83). CONCLUSION: According to this study, the CAC score is a valuable means to detect CAD among CKD subjects. There is no significant difference in CAC between patients with substantial CAD-RADS in CKD and non-CKD patients. The cut-point of 85 for the CAC score was found valuable to diagnose CAD with over 80% sensitivity and specificity. © 2024, Isfahan University of Medical Sciences(IUMS). All rights reserved.