In the multivariate logistic regression analysis after adjusting for age, gender, and history of diabetes mellitus and presence of CAS ≥50%, increased AAP thickness per millimeter (adjusted OR: 1.56, 95% CI: 1.18–2.05), severe-extent of AAP (adjusted OR: 13.66, 95% CI: 2.33–80.15), and presence of complex AAP (adjusted OR: 7.27, 95% CI: 2.30–23.03) remained associated with concomitant ACAS ≥50% [Table 3].
To examine the combined predictive power of the AAP thickness, extent, and complexity, a multivariate logistic regression model for ACAS ≥50% was built using the three AAP characteristics and their interactions as covariates. The logistic regression model's probabilities for predicting ACAS ≥50% showed an area under the ROC curve of 0.78 (95% CI: 0.70–0.85, P < 0.001), with a sensitivity of 69.0% and a specificity of 78.7% [Figure 4].
Based on the ROC curve, the optimal cut-off value of the AAP thickness as a predictor for ACAS ≥50% was projected to be 2.95 mm, with the area under the curve at 0.75 (95% CI: 0.65–0.86; P < 0.001). In 15 patients with AICVD whose AAP thickness was ≥2.95 mm and AAP extent was severe, with accompanying complex AAP, the percentage of the likelihood of developing ACAS ≥50% was 73.3% (11/15). In 32 AICVD patients without AAP, the percentage of the possibility of developing ACAS ≥50% was only 12.5% (4/32).
To determine whether AAP could offer further information beyond CAS and IAS in predicting ACAS, we established a basic logistic regression model including CAS ≥50% and IAS ≥50% as covariates to predict the coexistence of ACAS ≥50% (predicted probabilities’ area under the ROC curve: 0.66, 95% CI: 0.57–0.75, P = 0.008). Additionally, entering the AAP characteristics to the basic logistic regression model significantly increased these models’ predictive power (changes in the predicted probabilities’ area under the ROC curve: 0.11 for AAP thickness, P = 0.017; 0.11 for AAP extent, P = 0.012; 0.10 for presence of complex AAP, P = 0.040) [Figure 5, Table 4]. In multivariable logistic regression analysis, AAP characteristics were predictors for ACAS ≥50% regardless of whether CAS ≥50% or IAS ≥50% existed, whereas presence of CAS ≥50% and IAS ≥50% had no predictive value independent of AAP characteristics [Table 5].
The prevalence of ACAS ≥50% (24.6%) in our study was comparable to existing data (18%–33%).[1–3] However, the importance of AAP for ACAS ≥50% in patients with AICVD had been rarely explored. In contrast with previous studies,[3,19] we profiled the AAP characteristics in a more comprehensive manner, and examined the predictive value of AAP for ACAS ≥50% in the patients with AICVD from various aspects.
In our study, the presence of complex AAP and maximal thickness of AAPs were proven to be independently associated with ACAS ≥50% in the patients with AICVD; further, the AAP extent was also indicated as a parameter for predicting ACAS ≥50%. It echoed our previous findings that AICVD patients with symptomatic coronary artery stenosis of ≥50% or ACAS ≥50% had more diffused cervicocephalic atherosclerosis and lent more weight to the notion that atherosclerosis was a generalized disease. This has been rarely examined before perhaps because previous studies on AAP mainly used transesophageal echocardiography, which failed to visualize the entire aortic arch and reliably determine the AAP extent. Although CTA could image the full length of the aortic arch and detect smaller plaques,[20,26] the relationship between the AAP extent on CTA and ACAS has not been explored in patients with AICVD yet. A CTA study on 48 patients with ischemic stroke of undetermined etiology, without excluding those with coronary artery disease history, did not show significant associations between the thoracic aortic plaque extent and coronary stenosis of ≥50%. Aside from different study populations, distinct observing scope of thoracic aorta and relatively small sample size, they got negative results perhaps also because that they measured the aortic plaque extent with a semi-quantitative scale, where “rare, small plaques” and “frequent, large plaques” might be difficult to be clearly distinguished. Conversely, we applied a more quantitative approach that the mild- and severe-extent AAP were separated by counting the number of the perpendicular slices and quadrants on each perpendicular slice occupied by AAPs. In our study, severe-extent AAP exhibited independent relationship with ACAS ≥50%.
Moreover, our results suggested that the AAP characteristics might provide more information than the presence of CAS ≥50% and IAS ≥50% to indicate ACAS ≥50% in patients with AICVD. On one hand, neither CAS ≥50% nor IAS ≥50% showed a predictive value independent of the AAP characteristics in this study. On the other hand, we found that the AAP characteristics could add further power to CAS ≥50% and IAS ≥50% for ACAS prediction, with a significant increment in the area under ROC. Larger-scale studies are needed to verify these findings and the underlying mechanisms should be explored. A possible explanation may be the anatomical and physiological differences in various arterial beds, to some extent reflected by the developing order of atherosclerosis. Aortic and coronary arteries were usually affected earlier than the cervical and intracranial arteries; thus, AAP may serve as a better marker for ACAS ≥50% in AICVD than CAS ≥50% and IAS ≥50%.
Although without known history of coronary artery disease, AICVD patients with ACAS ≥50% had significantly worse outcomes, suffering not only more cardiac events but also more recurrent stroke. Early detection of ACAS ≥50% in patients with AICVD is of priority to make more targeted and integrated monitoring and intervening plans for these high-risk patients. However, routine screening of ACAS ≥50% in patients with AICVD might not be warranted. Our data supported a comprehensive evaluation of AAP characteristics to aid in selecting the very high risk AICVD patients who should proceed with further examination of coronary arteries, in addition to the established ACAS prediction paradigms based on traditional vascular risk factors, CAS and IAS. These AAP characteristics could be feasibly assessed with traditional CTA of brain blood-supplying arteries, thus it is highly applicable in current clinical settings of AICVD and should not be overlooked. Such a “one shot” prediction of ACAS ≥50% in the acute setting of AICVD without requiring additional screening imaging may be more clinically and economically efficient. In the future, larger-scale and prospective studies are needed to reevaluate the utility of AAP in the risk stratification and clinical management of AICVD patients, in light of the close association between AAP and ACAS ≥50%.
There were several limitations to this study. First, the sample size was not large, and all patients were Chinese. Given the ethnic differences in the atherosclerotic severity and distribution, correlations of various arterial beds may also be different. Second, CTA has a limited resolution in imaging AAPs. Small isodense AAPs might be overlooked. However, CTA is non-invasive and capable of evaluating AAPs along with routine examination of cervical and intracranial arteries in patients with AICVD. It potentially reduced the selection bias of our study subjects, and our data might be more easily translated into clinical practice. In addition, focusing on the relationship between AAP and ACAS ≥50%, we only evaluated atherosclerosis in the brain blood-supplying arteries without information on other arterial beds, which could have stronger inter-arterial correlations and more clinical impacts.
In conclusion, patients with both AICVD and ACAS ≥50% were more likely to have thicker, severe-extent, and complex AAP than those with AICVD only. These AAP characteristics were independent markers of ACAS ≥50% in AICVD, and their predictive power might be stronger than CAS ≥50% and IAS ≥50%. For AICVD patients with no history of coronary artery disease, evaluating AAP was important to prompt early identification of ACAS ≥50% and timely initiation of more integrated clinical management considering both coronary and brain blood-supplying arteries.
We acknowledged the great material support by the Clinical Center for Cardio-cerebrovascular Disease of Capital Medical University.
This study was supported by grants from Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support (No. ZYLX201706) and Beijing Municipal Natural Science Foundation (No. 7172093).
1. Calvet D, Touze E, Varenne O, Sablayrolles JL, Weber S, Mas JL. Prevalence of asymptomatic coronary artery disease in ischemic stroke patients: the PRECORIS study. Circulation
2010; 121:1623–1629. doi: 10.1161/CIRCULATIONAHA.109.906958.
2. Yoo J, Yang JH, Choi BW, Kim YD, Nam HS, Choi HY, et al. The frequency and risk of preclinical coronary artery disease detected using multichannel cardiac computed tomography in patients with ischemic stroke. Cerebrovasc Dis
2012; 33:286–294. doi: 10.1159/000334980.
3. Amarenco P, Lavallee PC, Labreuche J, Ducrocq G, Juliard JM, Feldman L, et al. Prevalence of coronary atherosclerosis
in patients with cerebral infarction. Stroke
2011; 42:22–29. doi: 10.1161/STROKEAHA.110.584086.
4. Gunnoo T, Hasan N, Khan MS, Slark J, Bentley P, Sharma P. Quantifying the risk of heart disease following acute ischaemic stroke: a meta-analysis of over 50,000 participants. BMJ Open
2016; 6:e009535doi: 10.1136/bmjopen-2015-009535.
5. Amarenco P, Lavallee PC, Labreuche J, Ducrocq G, Juliard JM, Feldman L, et al. Coronary artery disease and risk of major vascular events after cerebral infarction. Stroke
2013; 44:1505–1511. doi: 10.1161/STROKEAHA.111.000142.
6. Hur J, Lee KH, Hong SR, Suh YJ, Hong YJ, Lee HJ, et al. Prognostic value of coronary computed tomography angiography in stroke patients. Atherosclerosis
2015; 238:271–277. doi: 10.1016/j.atherosclerosis
7. Yoo J, Song D, Baek JH, Kim K, Kim J, Song TJ, et al. Poor long-term outcomes in stroke patients with asymptomatic coronary artery disease in heart CT. Atherosclerosis
2017; 265:7–13. doi: 10.1016/j.atherosclerosis
8. Pepine CJ, Cohn PF, Deedwania PC, Gibson RS, Handberg E, Hill JA, et al. Effects of treatment on outcome in mildly symptomatic patients with ischemia during daily life. The Atenolol Silent Ischemia Study (ASIST). Circulation
1994; 90:762–768. doi: 10.1161/01.cir.90.2.762.
9. Choi KH, Lee JM, Park I, Kim J, Rhee TM, Hwang D, et al. Comparison of long-term clinical outcomes between revascularization versus medical treatment in patients with silent myocardial ischemia. Int J Cardiol
2019; 277:47–53. doi: 10.1016/j.ijcard.2018.08.006.
10. Adams RJ, Chimowitz MI, Alpert JS, Awad IA, Cerqueria MD, Fayad P, et al. Coronary risk evaluation in patients with transient ischemic attack and ischemic stroke: a scientific statement for healthcare professionals from the Stroke Council and the Council on Clinical Cardiology of the American Heart Association/American Stroke Association. Stroke
2003; 34:2310–2322. doi: 10.1161/01.STR.0000090125.28466.E2.
11. Hoshino A, Nakamura T, Enomoto S, Kawahito H, Kurata H, Nakahara Y, et al. Prevalence of coronary artery disease in Japanese patients with cerebral infarction: impact of metabolic syndrome and intracranial large artery atherosclerosis
. Circ J
12. Seo WK, Yong HS, Koh SB, Suh SI, Kim JH, Yu SW, et al. Correlation of coronary artery atherosclerosis
of the intracranial cerebral artery and the extracranial carotid artery. Eur Neurol
2008; 59:292–298. doi: 10.1159/000121418.
13. Conforto AB, Leite CC, Nomura CH, Bor-Seng-Shu E, Santos RD. Is there a consistent association between coronary heart disease and ischemic stroke caused by intracranial atherosclerosis
? Arq Neuropsiquiatr
2013; 71:320–326. doi: 10.1590/0004-282x20130028.
14. Adraktas DD, Brasic N, Furtado AD, Cheng SC, Ordovas K, Chun K, et al. Carotid atherosclerosis
does not predict coronary, vertebral, or aortic atherosclerosis
in patients with acute stroke symptoms. Stroke
2010; 41:1604–1609. doi: 10.1161/STROKEAHA.109.577437.
15. Kong Q, Ma X, Wang C, Feng W, Ovbiagele B, Zhang Y, et al. Patients with acute ischemic cerebrovascular disease
with coronary artery stenosis have more diffused cervicocephalic atherosclerosis
. J Atheroscler Thromb
2019; Epub ahead of print. doi: 10.5551/jat.47464.
16. Stary HC, Chandler AB, Dinsmore RE, Fuster V, Glagov S, Insull WJ, et al. A definition of advanced types of atherosclerotic lesions and a histological classification of atherosclerosis
. A report from the Committee on Vascular Lesions of the Council on Arteriosclerosis, American Heart Association. Arterioscler Thromb Vasc Biol
1995; 15:1512–1531. doi: 10.1161/01.ATV.15.9.1512.
17. Fujita S, Sugioka K, Matsumura Y, Ito A, Hozumi T, Hasegawa T, et al. Impact of concomitant coronary artery disease on atherosclerotic plaques in the aortic arch in patients with severe aortic stenosis. Clin Cardiol
2013; 36:352–357. doi: 10.1002/clc.22121.
18. Chatzikonstantinou A, Ebert AD, Schoenberg SO, Hennerici MG, Henzler T. Atherosclerosis
in intracranial, extracranial, and coronary arteries with aortic plaques in patients with ischemic stroke of undetermined etiology. Int J Neurosci
2015; 125:663–670. doi: 10.3109/00207454.2014.961188.
19. Cho HJ, Lee JH, Kim YJ, Moon Y, Ko SM, Kim HY. Comprehensive evaluation of coronary artery disease and aortic atherosclerosis
in acute ischemic stroke patients: usefulness based on Framingham risk score and stroke subtype. Cerebrovasc Dis
2011; 31:592–600. doi: 10.1159/000326075.
20. Chatzikonstantinou A, Krissak R, Fluchter S, Artemis D, Schaefer A, Schoenberg SO, et al. CT angiography of the aorta is superior to transesophageal echocardiography for determining stroke subtypes in patients with cryptogenic ischemic stroke. Cerebrovasc Dis
2012; 33:322–328. doi: 10.1159/000335828.
21. Lee K, Hur J, Hong SR, Suh YJ, Im DJ, Kim YJ, et al. Predictors of recurrent stroke in patients with ischemic stroke: comparison study between transesophageal echocardiography and cardiac CT. Radiology
2015; 276:381–389. doi: 10.1148/radiol.15142300.
22. Sun K, Han RJ, Wang LJ, Wang C, Chen N, Du XY, et al. Feasibility of high-pitch dual-scource CT combined with carotid and cerebrovascular angiograph (in Chinese). Chin J Med Imaging Technol
23. Amarenco P, Cohen A, Hommel M, Moulin T, Leys D, Bousser M. Atherosclerotic disease of the aortic arch as a risk factor for recurrent ischemic stroke. N Engl J Med
1996; 334:1216–1221. doi: 10.1056/NEJM199605093341902.
24. Hammadah M, Qintar M, Nissen SE, John JS, Alkharabsheh S, Mobolaji-Lawal M, et al. Non-invasive volumetric assessment of aortic atheroma: a core laboratory validation using computed tomography angiography. Int J Cardiovasc Imaging
2016; 32:121–129. doi: 10.1007/s10554-015-0674-2.
25. Tenenbaum A, Garniek A, Shemesh J, Fisman EZ, Stroh CI, Itzchak Y, et al. Dual-helical CT for detecting aortic atheromas as a source of stroke: comparison with transesophageal echocardiography. Radiology
1998; 208:153–158. doi: 10.1148/radiology.208.1.9646807.
26. Tenenbaum A, Garniek A, Shemesh J, Stroh CI, Itzchak Y, Vered Z, et al. Spiral computerized tomography (dual helical mode) as a detector of aortic atheromas in patients with stroke and systemic emboli: additional benefit of the contrast-enhanced technique. Isr Med Assoc J
27. Dong J, Ma X, Qie J, Ji X. Aortic complex plaque predicts the risk of cryptogenic ischemic cerebrovascular disease recurrence. Aging Dis
2016; 7:114–120. doi: 10.14336/AD.2015.0923.
28. North American Symptomatic Carotid Endarterectomy Trial. Methods, patient characteristics, and progress. Stroke
29. Samuels OB, Joseph GJ, Lynn MJ, Smith HA, Chimowitz MI. A standardized method for measuring intracranial arterial stenosis. AJNR Am J Neuroradiol