Analyzing Coronary Artery Disease in Patients with Low CAC Scores by 64-Slice MDCT
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....
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1100/2012/907062 |
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| _version_ | 1832564645144035328 |
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| author | Nan-Han Lu Lee-Ren Yeh Tai-Been Chen Yung-Hui Huang Chung-Ming Kuo Hueisch-Jy Ding |
| author_facet | Nan-Han Lu Lee-Ren Yeh Tai-Been Chen Yung-Hui Huang Chung-Ming Kuo Hueisch-Jy Ding |
| author_sort | Nan-Han Lu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e8967801ba334e11b7aec6387bedfe58 |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-e8967801ba334e11b7aec6387bedfe582025-02-03T01:10:38ZengWileyThe Scientific World Journal1537-744X2012-01-01201210.1100/2012/907062907062Analyzing Coronary Artery Disease in Patients with Low CAC Scores by 64-Slice MDCTNan-Han Lu0Lee-Ren Yeh1Tai-Been Chen2Yung-Hui Huang3Chung-Ming Kuo4Hueisch-Jy Ding5Department of Information Engineering, I-Shou University, Kaohsiung City 84001, TaiwanDepartment of Radiology, E-DA Hospital, I-Shou University, Kaohsiung City 82445, TaiwanDepartment of Medical Imaging and Radiological Sciences, I-Shou University, Kaohsiung City 82445, TaiwanDepartment of Medical Imaging and Radiological Sciences, I-Shou University, Kaohsiung City 82445, TaiwanDepartment of Information Engineering, I-Shou University, Kaohsiung City 84001, TaiwanDepartment of Medical Imaging and Radiological Sciences, I-Shou University, Kaohsiung City 82445, TaiwanPurpose. 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.http://dx.doi.org/10.1100/2012/907062 |
| spellingShingle | Nan-Han Lu Lee-Ren Yeh Tai-Been Chen Yung-Hui Huang Chung-Ming Kuo Hueisch-Jy Ding Analyzing Coronary Artery Disease in Patients with Low CAC Scores by 64-Slice MDCT The Scientific World Journal |
| title | Analyzing Coronary Artery Disease in Patients with Low CAC Scores by 64-Slice MDCT |
| title_full | Analyzing Coronary Artery Disease in Patients with Low CAC Scores by 64-Slice MDCT |
| title_fullStr | Analyzing Coronary Artery Disease in Patients with Low CAC Scores by 64-Slice MDCT |
| title_full_unstemmed | Analyzing Coronary Artery Disease in Patients with Low CAC Scores by 64-Slice MDCT |
| title_short | Analyzing Coronary Artery Disease in Patients with Low CAC Scores by 64-Slice MDCT |
| title_sort | analyzing coronary artery disease in patients with low cac scores by 64 slice mdct |
| url | http://dx.doi.org/10.1100/2012/907062 |
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