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Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
Published on: August 28, 2018
Relationship between abdominal aortic and coronary artery calcification as detected by computed tomography in chronic
Yohei Takayama1, Yoshinari Yasuda2,3, Susumu Suzuki4,5
1Department of Cardiology, Nagoya University Graduate School of Medicine, 65, Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan.
Insights
Abdominal aortic calcification index (ACI) can predict severe coronary artery calcification (CAC) in chronic kidney disease (CKD) patients. An ACI of 16.5% is a key indicator, improving risk prediction when combined with traditional factors.
Area of Science:
- Cardiovascular Medicine
- Nephrology
- Radiology
Background:
- Chronic kidney disease (CKD) is associated with increased cardiovascular risk.
- Vascular calcification, including abdominal aortic calcification (AAC) and coronary artery calcification (CAC), is common in CKD patients.
- The relationship between AAC and CAC in CKD requires further investigation for improved risk stratification.
Purpose of the Study:
- To investigate the association between abdominal aortic calcification (AAC) and coronary artery calcification (CAC) in asymptomatic patients with chronic kidney disease (CKD).
- To evaluate the predictive value of the abdominal aortic calcification index (ACI) for severe CAC in this population.
- To determine if ACI improves the prediction of severe CAC when added to traditional risk factors.
Main Methods:
- A cohort of 126 asymptomatic CKD patients was evaluated.
- Non-contrast computed tomography (CT) was used to quantify AAC (using ACI) and CAC.
- Statistical analyses, including multivariate adjustment and receiver-operating curve (ROC) analysis, were performed to assess the relationship and predictive value.
Main Results:
- Abdominal aortic calcification (AAC) was prevalent (86.5%) in the CKD cohort.
- The abdominal aortic calcification index (ACI) significantly increased with higher coronary artery calcification (CAC) score grades (p < 0.001).
- ACI was independently associated with severe CAC (CAC score >400) (OR 1.08, p < 0.001), with an optimal cut-off of 16.5% (AUC = 0.79, p < 0.001).
- Adding ACI to traditional risk factors significantly improved the prediction of severe CAC (C-statistic 0.853 vs 0.737, p = 0.023).
Conclusions:
- The abdominal aortic calcification index (ACI) of 16.5% can predict severe coronary artery calcification (CAC) in patients with chronic kidney disease (CKD).
- Incorporating ACI into risk prediction models significantly enhances the ability to identify severe CAC in CKD patients.
- ACI serves as a valuable screening tool for assessing cardiovascular risk in clinical practice for CKD patients.
Abstract:
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.
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