Conventional coronary angiography (CAG) has limitations in the evaluation of the hemodynamic significance of detected coronary stenosis, even with a negative stress test. This suggests a need for further evaluation of the coronary vasculature using additional imaging modalities. The increasing employment of multi-slice spiral computed tomography (MSCT) in the diagnosis of coronary artery diseases (CAD) has made it possible to detect coronary atherosclerosis plaques by noninvasive methods. Thrombus embolization caused by the rupture of soft plaque accounts for acute coronary syndrome (ACS). Histopathological research has confirmed the existence of inflammatory factors in soft plaques. The evaluation of plaque composition and stability requires further study and the relationship between inflammatory markers and the characteristics of coronary atherosclerosis plaques needs to be probed.
We studied 256 patients with suspected ACS between July 2006 and July 2007 in the Cardiopathy Department of Shanxi Provincial Peoples’ Hospital. All of them underwent 64-slice CT coronary angiography (64-SCTA) and CAG. Based on the results of CAG and 64-SCTA, we selected 30 patients with normal CAG and 64-SCTA for the control group; 22 males and 8 females, mean age (58.0±9.7) years. According to WHO diagnostic criteria, the standard CAD group included at least one coronary artery cavity narrowing ≥50% in the left main, left anterior descending (LAD), left circumflex (LCX), or right coronary artery (RCA) as determined by CAG. From a group of patients with CAD, we evaluated their CT scales to select 30 patients with soft plaque (including 24 males and 6 females, mean age (62.8±10.1) years), 30 patients with mixed plaque (including 22 males and 8 females, mean age (61.3±10.3) years), and 30 patients with hard plaque (including 22 males and 8 females, mean age (63.5±6.9) years). Exclusion criteria were as follows: acute myocardial infarction (AMI) ≤2 weeks, patients with diseases which influenced the level of Hs-CRP, such as acute or chronic infection, a tumor, an operation in the near future, a wound, chronic connective tissue disease or valvular heart disease.
The patients were requested to hold their breath for 10 to 12 seconds while scanning. Scanning was performed at a 330 ms gantry rotation time, Pitch 0.25, slice thickness 0.6 mm or 0.75 mm, and re-establish compartment 0.4 mm or 0.5 mm acquisition. A contrast-enhanced scan was then performed with a bolus of 100 ml of contrast medium (Ultravist 300 mg I/ml; Shenzhen, China). Heart rate was lower than 70 beats/min and rhythm was normal while scanning. A β-blocker was administered when the heart rate was more than 70 beats/min one hour before scanning and scanning was not performed until the heart rate was lower than 70 beats/min. Patients who had severe irregularity, heart/renal failure, hypersensitiveness or the inability to hold their breath could not receive the scanning.
Image reconstruction and post-treatment
Images were reconstructed with an acquisition time of 210 ms in diastole 350-450 ms before the R wave using retrospective electrocardiogram gating. The original data were transmitted to the workstation for volume rendering (VR), multiplanar reformation (MPR) and maximum intensity projection (MIP). The CT datasets were analyzed by two independent, experienced readers who were blinded to the patients’ conditions and consequence of CAG, who reached a consensus on the findings. According to the grade of the stenosis (maximum diameter reduction) of the coronary artery, normals were classified as ≤25% diameter stenosis, light stenoses were classified as 25%-50% diameter stenosis, midrange stenoses were classified as 51%-75% diameter stenosis, sever stenoses were classified as ≥76% diameter stenosis.2 The plaques detected in CT image were fractionated into four sections. Plaque density measurements were performed by an independent observer. Four sites on each plaque area (1 mm) of MIP were randomly chosen for measurement. CT values were recorded and mean value was calculated to detect the quality of the plaque. The following thresholds for the noninvasive differentiation of plaque morphology according to MSCT criteria seem to be meaningful: soft plaques ≤60 Hounsfeild units (HU), mixed plaques 61-129 HU, hard plaques ≥130 HU. The plaques were classified according to Schroeder's classification by plaque density.3 The plaque classification was simplified as follows: soft plaques ≤60 HU, mixed plaques 61 to 129 HU, hard plaques ≥130 HU. A patient having one soft plaque was in the soft plaque group. A patient having only hard plaques belonged in the hard plaque group and others belonged in the mixed plaque group. The calculation of the coronary plaque burden and volume was performed on axial cross sectional images by two independent observers in a joint reading. The observers were blinded to the results of the first scan. Plaque areas were manually traced per slice in all affected coronary segments. The plaque area of each coronary plaque was visualized in at least two adjacent slices (reconstructed slice thickness 1 mm) and was determined on all affected slices and plaque volume was assessed by multiplying the area by the slice thickness. Total plaque burden was defined as the sum of all assessable plaque volumes in one patient.4
In all patients, CAG was obtained using 5F catheters in two weeks with Judkin's method. The angiograms were evaluated through eight typical projection positions. Left coronary nervure was performed in six typical projection positions such as left anterior oblique (LAO) 45°+head, foot 20°; right anterior oblique (RAO) 30°+head, foot 20°, anterior posterior (AP) + head, foot 20°. RCA was performed by two typical projection position such as LAO 45°+head 20°, RAO 30°+head 20°. Lesions with a stenosis diameter ≥50% in left main, LAD, LCX, RCA and arch-branch were considered to be severe lesions.
Blood sample collection and detection
Venous blood samples (4 ml) were collected and then centrifuged with 3000 r/min in the next morning after hospitalization, and aliquots of blood serum were stored at -20°C until assay. IL-6 was measured in plasma samples by an ABC-ELISA assay. Hs-CRP was measured in plasma samples by an immunoturbidimetric assay.
All data were treated with the statistical program SPSS 11.5 for Windows (SPSS Inc., USA). Continuous variables were described as mean ± standard deviation (SD). Analysis of variance (ANOVA) was used to compare the variables of two groups among the three or four groups. SNK analysis was performed to evaluate differences in two groups. Pearson's correlation analysis was used to investigate associations between biomarker parameters (Hs-CRP and IL-6) and plaque densities. Values of P<0.05 were considered significantly different.
The characteristics of the four groups are shown in Table 1. As expected, low density lipoprotein (LDL) from the soft plaque group, mixed plaque group and hard plaque group showed values significantly higher than normal plaque. Total cholesterol (TC) from the soft plaque group and mixed plaque group showed values significantly higher than the hard plaque group and control group There were no significant differences in age, sex, smoking, family history of CAD, hypertension, abnormal blood fat occurrence, diabetes, body mass index (BMI), triglyceride (TG) or high density lipoprotein (HDL) among the four groups.
Values of 64-SCTA in detection of coronary artery plaque
There were 120 patients who had received both the 64-SCTA and the CAG examinations. The sensitivity of detecting coronary artery plaque in 64-SCTA was 87.4%, the specificity was 87.1%, the positive predictive value was 82.2%, and the negative predictive value was 91.0%.
The comparison of the levels of serum Hs-CRP, IL-6 among plaque groups
Mean levels of serum Hs-CRP and IL-6 in three plaque groups were significantly higher than those in the control group (P <0.01). Mean levels of serum Hs-CRP and IL-6 in the soft plaque group and mixed plaque group were significantly higher than in the hard plaque group (P <0.01), but there was no statistically significant difference in these factors between the soft plaque group and mixed plaque group (P=0.135, P=0.362, Table 2).
Comparison of plaque burden among plaque groups and the correlations between the CT scale, plaque burden and Hs-CRP and IL-6
Mean levels of the plaque burden in the soft plaque group and mixed plaque group were significantly higher than those in the hard plaque group (P <0.01), but there was no statistical difference between the soft plaque group and the mixed plaque group (P=0.246, Table 3).
There was a negative correlation between the CT scale and Hs-CRP and IL-6 in the soft plaque group (r=-0.621, r=-0.593, P <0.01). There was a positive correlation between the plaque burden and Hs-CRP and IL-6 in the soft plaque group (r=0.579, r=0.429, P <0.05) (Table 4 and Figures 1 and 2).
Some studies have found the diagnostic accuracy of MSCT to be excellent compared with catheter angiography, but there has not been an abundance of clinical outcomes studies. For any new risk marker to be considered useful for risk prediction, it must, at the very least, have an independent statistical association with risk after accounting for established readily available and inexpensive risk markers.5,6 CAD is one of the most common causes of death in humans. Recently, its incidence and mortality are increasing. ACS, accounting for 30% to 40% of CAD, has been the main cause of CAD patients'poor prognosis and sudden death. Coronary atherosclerosis, plaque formation, plaque instability and disruption, a thrombus in the artery, obliteration of the artery, heart muscle ischemia and even necrosis are the basic pathological mechanisms of CAD. The basic pathology of ACS and sudden coronary death involve vulnerable plaque, vulnerable cardiac muscle, and vulnerable blood.7
Research shows that almost all the cases of plaque disruption are based on the instability of plaque with little relation to the degree of coronary artery stenosis. A correlation has been shown between elevated systemic inflammation markers, accumulation of inflammatory cells within atherosclerotic soft plaque, and lower fibrous cap thickness.8 Early identification and early intervention with soft plaque is definitely important to improving the prognosis in ACS. Intravascular unltrasound (IVUS) studies show that the patients with ACS have infarction-coherent criminal blood vessels and also have plaque disruption (vulnerable plaque) in uncriminal blood vessels. At present it is a technique for early detection of and anticipation of vulnerable plaque. We need a non-invasive means to replace early invasive inspection to identify and predict unstable plaque.9,10 Selected biomarkers may be used to predict future cardiovascular events, but the gains over considering conventional risk factors are minimal. Risk classification has improved in intermediate-risk individuals, mainly through the identification of those unlikely to develop events.11,12 Many risk factors have been proposed as predictors of CHD. New risk factors or markers are frequently identified and evaluated as potential additions to standard risk assessment strategies.13,14
MSCT features high scanning speed, no injury to the patient and high resolution and is equipped with abundant and advanced postprocessing software. Currently, the clinical employment of 64-slice spiral CT for diagnosing CAD has made it possible to detect plaque quality by noninvasive methods.15 This study discovered that MSCT coronary angiography has high sensitivity and specificity to show the main coronary artery and the second and third branches; minimum blood vessel diameter 1.5 mm. According to the CT scale, the plaque property can be evaluated and offers a good correlation with pathohistological examination.16,17
Histopathology shows that instable plaques are characterized by their connective tissue, a fairly high content of fat and inflammatory masses in soft plaques with the frequent occurrence of hemorrhage, calcification, necrosis and occasional thrombus on the surface. Studies have discovered that CRP is a typical phase synthetic protein in human liver regulated by IL-6. It is not only a symbol of inflammation but also a direct factor of arterial thrombosis. Serum Hs-CRP levels have a close correlation with inflammation and with the degree of tissue injury. CRP has been shown to participate in the whole atherosclerotic process, including damage of blood vessel endothelium and the formation, maturation, instability and final disruption of atheromatous plaque. Hard plaque contains mainly collagen, elastic fiber or calcified tissues and has an intimate relation with stable plaque. Therefore detection of surrogate inflammatory markers may reflect the degree of stability of plaque in a CAD patient. We may affirm the fat, water and fiber composition by the CT scale, which helps in the classification of the character of all coronary atherosclerosis plaques.18
We diagnosed all the patients based on their GAG results. All coronary atherosclerosis plaques were classified by 64-slice spiral CT coronary angiography. The results and the levels of serum Hs-CRP and IL-6 were statistically analyzed. The study showed that mean levels of serum Hs-CRP and IL-6 in the soft plaque group and the mixed plaque group were statistically higher than those in the hard plaque group (P <0.01). Mean levels of serum Hs-CRP and IL-6 in the three plaque groups were statistically higher than in the control group (P <0.01). This agrees with previous research results,1 which suggested that inflammation is involved in the occurrence and development of plaque and is the cause for ACS. The scale of plaque vulnerability was graded. Correlation analysis suggests that there was negative correlation between the CT scale, Hs-CRP and IL-6 in the soft plaque group (r=-0.621, r=-0.593, P <0.01). There is a positive correlation between the plaque burden and Hs-CRP and IL-6 in the soft plaque group (r=0.579, r=0.429, P <0.01). With plaque burden's accretion, levels of serum Hs-CRP and IL-6 were increased which suggests that they were involved in the occurrence and development of soft plaque.
The results suggest that 64-CT is an effective way to distinguish different qualities of coronary atherosclerotic plaque and suggest that serum Hs-CRP and IL-6 levels can be considered as one of the indexes to judge the degree of CHD and may reflect the activities of plaque in CHD patients. Thus it is important for clinical diagnosis and risk evaluation of ACS patients.
In conclusion, the partial and systemic inflammation has a major effect on arteriosclerosis and its complications. The 64-slice spiral CT coronary angiography can effectually detect coronary atherosclerosis plaque and provide evidence of vulnerable patients. Inflammation symbols, such as Hs-CRP and IL-6, indirectly reflect the stabilization degree of plaque and reveal their involvement in the occurrence and development of soft plaque.
1. Sun Z, Lin C, Davidson R, Donq C, Liao Y. Diagnostic value of 64-slice CT angiography in coronary artery disease: a systematic review. Eur J Radiol 2008; 67: 78-84.
2. Leber AW, Becker A, Knez A, von Ziegler F, Sirol M, Nikolaou K, et al. Accuracy of 64-slice computed tomography to classify and quantify plaque volumes in the proximal coronary system: a comparative study using intravascular ultrasound. J Am Coll Cardiol 2006; 47: 672-677.
3. Schroeder S, Kopp AF, Baumbach A, Meisner C, Kuettner A, Georg C, et al. Noninvasive detection and evaluation of atherosclerotic coronary plaques with multislice computed tomography. J Am Coll Cardiol 2001; 37: 1430-1435.
4. Leber AW, Knez A, von Ziegler F, Becker A, Nikolaou K, Paul S, et al. Quantification of obstructive and nonobstructive coronary lesions by 64-slice computed tomography: a comparative study with quantitative coronary angiography and intravascular ultrasound. J Am Coll Cardiol 2005; 46: 147-154.
5. D'Agostino RB, Sr. Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 2008; 117: 743-753.
6. Greenland P, Alpert JS, Beller GA, Benjamin EJ, Budoff MJ, Fayad ZA, et al. 2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults. J Am Coll Cardiol 2010; 56: e50-e103.
7. Naghavi M, Libby P, Falk E, Casscells SW, Litovsky S, Rumberger J, et al. From vulnerable plaque to vulnerable patient: a call for new definitions and risk assessment strategies. Part I. Circulation 2003; 108: 1664-1672.
8. Raffel OC, Tearney GJ, Gauthier DD, Halpern EF, Bouma BE, Jang IK. Relationship between a systemic inflammatory marker, plaque inflammation, and plaque characteristics determined by intravascular optical coherence tomography. Arterioscler Thromb Vasc Biol 2007; 27: 1820-1827.
9. Liu YL, Liu XJ. Modern image diagnosis on CAD. Beijing: People Surgeon Book Concern; 2002: 21-29.
10. Hong MK, Mintz GS, Lee CW, Kim YH, Lee SW, Song JM, et al. Comparison of coronary plaque rupture between stable angina and acute myocardial infarction. Circulation 2004; 110: 928-933.
11. Motoyama S, Kondo T, Sarai M, Sugiura A, Harigaya H, Sato T, et al. Multislice computed tomographic characteristics of coronary lesions in acute coronary syndromes. J Am Coll Cardiol 2007; 50: 319-326.
12. Tanaka A, Shimada K, Sano T, Namba M, Sakamoto T, Nishida Y, et al. Multiple plaque rupture and C reactive protein in acute myocardial infarction. J Am Coll Cardiol 2005; 45: 1594-1599.
13. Zethelius B, Berglund L, Sundstrom J, Ingelsson E, Basu S, Larsson A, et al. Use of multiple biomarkers to improve the prediction of death from cardiovascular causes. N Engl J Med 2008;358:2107-2116.
14. Ridker PM, Paynter NP, Rifai N, Gaziano JM, Cook NR. C-reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds Risk Score for men. Circulation 2008; 118: 2243-2251.
15. Melander O, Newton-Cheh C, Almgren P, Hedblad B, Berglund G, Engström G, et al. Novel and conventional biomarkers for prediction of incident cardiovascular events in the community. JAMA 2009; 302: 49-57.
16. Brindle P, Beswick A, Fahey T, Ebrahim S. Accuracy and impact of risk assessment in the primary prevention of cardiovascular disease: asystematic review. Heart 2006; 92: 1752-1759.
17. Ridker PM, Paynter NP, Rifai N, Gaziano JM, Cook NR. C-reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds Risk Score for men. Circulation 2008; 118: 2243-2251.
18. Schroeder S, Flohr T, Kopp AF, Meisner C, Kuettner A, Herdeg C. Accuracy of density measurements within plaques located in artificial coronary arteries by X-ray multislice CT: results of a phantom study. J Comput Assist Tomogr 2001; 25: 900-906.