Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2012, Article ID 907062, 6 pages
Clinical Study

Analyzing Coronary Artery Disease in Patients with Low CAC Scores by 64-Slice MDCT

1Department of Information Engineering, I-Shou University, Kaohsiung City 84001, Taiwan
2Department of Radiology, E-DA Hospital, I-Shou University, Kaohsiung City 82445, Taiwan
3Department of Medical Imaging and Radiological Sciences, I-Shou University, Kaohsiung City 82445, Taiwan

Received 16 February 2012; Accepted 15 March 2012

Academic Editors: F. A. Atik, A. Barile, and T. Fujita

Copyright © 2012 Nan-Han Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Purpose. Coronary artery calcification (CAC) scores are widely used to determine risk for Coronary Artery Disease (CAD). A CAC score does not have the diagnostic accuracy needed for CAD. This work uses a novel efficient approach to predict CAD in patients with low CAC scores. Materials and Methods. The study group comprised 86 subjects who underwent a screening health examination, including laboratory testing, CAC scanning, and cardiac angiography by 64-slice multidetector computed tomographic angiography. Eleven physiological variables and three personal parameters were investigated in proposed model. Logistic regression was applied to assess the sensitivity, specificity, and accuracy of when using individual variables and CAC score. Meta-analysis combined physiological and personal parameters by logistic regression. Results. The diagnostic sensitivity of the CAC score was 14.3% when the CAC score was ≤30. Sensitivity increased to 57.13% using the proposed model. The statistically significant variables, based on beta values and 𝑃 values, were family history, LDL-c, blood pressure, HDL-c, age, triglyceride, and cholesterol. Conclusions. The CAC score has low negative predictive value for CAD. This work applied a novel prediction method that uses patient information, including physiological and society parameters. The proposed method increases the accuracy of CAC score for predicting CAD.