Objective To explore the predictive value of visual coronary artery calcium score(VCACS) for coronary heart disease(CHD). Methods A retrospective collection of data was conducted on patients who underwent chest CT(with a slice thickness of 1.25 mm) and coronary CT angiography(CCTA) in Southwest Hospital of Army Medical University from July 2018 to July 2023. The imaging data were analyzed by experienced radiologists, and the patients with coronary artery stenosis ≥50% on CCTA were included in the CHD group. Agatston scores were derived from the analysis of ECG-gated cardiac CT using specialized software. Spearman's correlation analysis was employed to explore the relationship between Agatston scores and VCACS in all participants, different Agatston score ranges (0-10, 11-100, 101-400, >400) and age groups(≤65 years, >65 years). Univariate logistic regression analysis was used to assess the association between variables with CHD. After selecting statistically significant variables, multivariate logistic regression was employed to investigate the association between the total VCACS(tVCACS) subgroups and CHD. At the same time, the prediction model of XGBoost machine learning algorithm was constructed, and the significance analysis of SHAP features was combined to quantify the predictive value of each feature variable on CHD. Results A total of 287 participants, aged from 30 to 90 years, were included in the study, with 177 males. A total of 153 participants(53.3%) were classified into the CHD group. In the overall cohort, different Agatston score ranges and age groups, VCACS demonstrated good consistency with Agatston score in assessing the degree of coronary artery calcification. The tVCACS in CHD group was significantly higher than that in non-CHD group. Multivariate logistic regression analysis showed that the OR of tVCACS 2-5 group was 2.23(95% CI: 1.22-4.08), and the OR of the tVCACS≥6 group was 20.26(95% CI: 5.76-71.26), indicating a significantly increased risk of CHD with higher tVCACS. SHAP feature importance analysis also demonstrated that tVCACS had a higher predictive value for CHD risk than other factors. Conclusions VCACS obtained through 1.25 mm chest CT demonstrates a good correlation with Agatston score and has potential clinical application value in predicting CHD.
Key words
visual coronary artery calcification score /
Agatston score /
computed tomography /
coronary artery computed tomography angiography
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
References
[1] Hecht H S, Blaha M J, Kazerooni E A, et al. CAC-DRS: Coronary artery calcium data and reporting system. An expert consensus document of the society of cardiovascular computed tomography(SCCT) [J].J Cardiovasc Comput Tomogr, 2018, 12(3):185-191.
[2] Agatston A S, Janowitz W R, Hildner F J, et al. Quantification of coronary artery calcium using ultrafast computed tomography [J].J Am Coll Cardiol, 1990, 15(4):827-832.
[3] Huang Y L, Wu F Z, Wang Y C, et al. Reliable categorisation of visual scoring of coronary artery calcification on low-dose CT for lung cancer screening: validation with the standard agatston score [J].Eur Radiol, 2013, 23(5):1226-1233.
[4] Lorenzatti D, Piña P, Huang D, et al. Interaction between risk factors, coronary calcium, and CCTA plaque characteristics in patients aged 18-45 years [J].Eur Heart J Cardiovasc Imaging, 2024, 25(8):1071-1082.
[5] Kwan A C, Gransar H, Tzolos E, et al. The accuracy of coronary CT angiography in patients with coronary calcium score above 1000 agatston units: Comparison with quantitative coronary angiography [J]. J Cardiovasc Comput Tomogr, 2021, 15(5):412-418.
[6] Zhao Q, Liu L, Xian H, et al. Prognostic value of computed tomography-derived fractional flow reserve in patients with diabetes mellitus and unstable angina [J]. Cardiovasc Diabetol, 2024, 23(1):404.
[7] Hecht H S, Cronin P, Blaha M J, et al. 2016 SCCT/STR guidelines for coronary artery calcium scoring of noncontrast noncardiac chest CT scans: a report of the society of cardiovascular computed tomography and society of thoracic radiology [J]. J Cardiovasc Comput Tomogr, 2017, 11(1):74-84.
[8] Fresno C U, Tijmes F S, Thavendiranathan P, et al. Visual ordinal scoring of coronary artery calcium on contrast-enhanced and noncontrast chest CT: a retrospective study of diagnostic performance and prognostic utility[J].Am J Roentgenol, 2022, 219(4):569-578.
[9] Ference B A, Yoo W, Alesh I, et al. Effect of long-term exposure to lower low-density lipoprotein cholesterol beginning early in life on the risk of coronary heart disease: a mendelian randomization analysis [J].J Am Coll Cardiol, 2012, 60(25):2631-2639.
[10] Mach F, Baigent C, Catapano A L, et al. 2019 ESC/EAS guidelines for the management of dyslipidaemias: Lipid modification to reduce cardiovascular risk [J].Eur Heart J, 2020, 41(1):111-188.
[11] Neels J G, Leftheriotis G, Chinetti G. Atherosclerosis calcification: focus on lipoproteins [J]. Metabolites, 2023, 13(3):457.
[12] Klein L W. Cigarette smoking, atherosclerosis and the coronary hemodynamic response: a unifying hypothesis [J].J Am Coll Cardiol, 1984, 4(5):972-974.
[13] Rasmussen T, Frestad D, Køber L, et al. Development and progression of coronary artery calcification in long-term smokers: adverse effects of continued smoking [J].J Am Coll Cardiol, 2013, 62(3): 255-257.
[14] Petsophonsakul P, Burgmaier M, Willems B, et al. Nicotine promotes vascular calcification via intracellular Ca2+-mediated, Nox5-induced oxidative stress, and extracellular vesicle release in vascular smooth muscle cells [J].Cardiovasc Res, 2022, 118(9):2196-2210.
[15] Sharrett A R, Sorlie P D, Chambless L E, et al. Relative importance of various risk factors for asymptomatic carotid atherosclerosis versus coronary heart disease incidence: The atherosclerosis risk in communities study [J].Am J Epidemiol, 1999, 149(9):843-852.
[16] Mehta A, Rigdon J, Tattersall M C, et al. Association of carotid artery plaque with cardiovascular events and incident coronary artery calcium in individuals with absent coronary calcification: The MESA [J]. Circ Cardiovasc Imaging, 2021, 14(4): e011701.
PDF(1360 KB)
