Association Between Cardiovascular Risk Profiles and the Presence and Extent of Different Types of Coronary Atherosclerotic Plaque as Detected by Multidetector Computed Tomography
Objective— To assess the association between cardiovascular risk factors and extent of noncalcified- (NCAP), mixed- (MCAP), and calcified coronary atherosclerotic plaque (CAP).
Methods and Results— In this cross-sectional study, we included consecutive subjects who presented with chest pain but had no history of coronary artery disease (CAD) and did not develop acute coronary syndrome. Contrast-enhanced 64-slice coronary MDCT was performed to determine the presence of NCAP, MCAP, and CAP for each coronary segment. Among 195 patients (91 women, mean age: 54.6±12.0) exclusively NCAP was detected in 11 patients (5.6%). The extent of NCAP decreased and the extent of MCAP and CAP increased with age (P=0.06, P=0.02, and P=0.13, respectively). Hyperlipidemia and family history of CAD were associated with the extent of NCAP after adjusting for other risk factors (P=0.02 and P=0.04, respectively) or for the extent of MCAP and CAP (P=0.02 and P=0.05, respectively).
Conclusions— Our data suggest that only a small proportion of individuals have exclusively NCAP and indicate that the relation of NCAP and CAP changes with age. Among individual risk factors, hyperlipidemia and family history of CAD may be associated with the extent of NCAP. Larger observational trials are necessary to confirm our findings.
Traditional cardiovascular risk prediction relies on the presence of individual risk factors such as hypertension, hyperlipidemia, diabetes mellitus, and smoking. Risk assessment tools widely used in clinical practice such as the Framingham Risk Score (FRS) use these traditional risk factors (TRF) to categorize subjects according to their risk for cardiovascular events and to establish treatment guidelines.1,2 Although useful, the predictive value of these tools is limited as approximately 20% of cardiovascular events occur in the absence of any major risk factor,3 and at least one risk factor can be found in the majority of individuals who do not experience cardiovascular events.4
There is strong evidence that information on the presence and extent of calcified coronary atherosclerotic plaque (CAP) is independent and incremental to traditional risk assessment for the prediction of cardiovascular events.5 Whereas assessment of CAP is performed using noncontrast coronary CT imaging, recent data indicate that contrast-enhanced data acquisition using advanced multidetector (MDCT) technology permits the detection of noncalcified coronary atherosclerotic plaque (NCAP) in addition to CAP in good agreement with intravascular ultrasound (IVUS).6,7
Although previous studies indicate that the amount of CAP is highly related to the overall plaque burden, it represents only approximately 20% of the total atherosclerotic plaque burden8 and is thought to be present in the advanced stages of atherosclerosis within an individual plaque whereas NCAP is considered to be a feature of early atherosclerosis.9 Furthermore, there is growing evidence suggesting that NCAP might be associated with acute coronary syndrome.10,11 However, whether the relation of CAP to NCAP is dependent of age, and whether the presence and extent of NCAP, mixed coronary atherosclerotic plaque (MCAP), and CAP are similarly associated with cardiovascular risk factors remains unclear.
Thus, we performed a cross-sectional study to systematically assess the association between cardiovascular risk profiles and the presence and extent of different coronary atherosclerotic plaque components in a cohort of subjects without known coronary artery disease (CAD) who presented with acute chest pain and in whom acute coronary syndrome (ACS) was excluded.
This analysis was performed in a consecutive subset of subjects from the “Rule Out Myocardial Infarction by Computed Tomography” (ROMICAT) trial enrolled between May 2005 and April 2006. This trial was designed to assess the clinical utility of coronary MDCT to triage subjects with acute chest pain in the emergency department (ED).12 For this analysis, subjects with known CAD (history of MI, prior stent placement, or bypass grafting), or subjects who developed acute coronary syndrome during index hospitalization, were excluded. Briefly, consecutive adult subjects presenting to the ED with acute chest pain but without diagnostic electrocardiograpic (ECG) changes or positive initial cardiac biomarkers underwent coronary MDCT. Hemodynamically or clinically unstable subjects (systolic blood pressure <80 mm Hg, clinically significant supraventricular or ventricular arrhythmias, persistent chest pain despite therapy), subjects with known allergy to iodinated contrast agent, or serum creatinine greater than 1.3 mg/dL (per the recommendation of the institutional review board), on metformin treatment, with history of hyperthyroidism, and inability to provide informed consent were excluded.
The Institutional Review Board of the Massachusetts General Hospital approved the study. All participants provided written informed consent.
Subjects underwent MDCT imaging using a 64-slice MDCT scanner (Sensation 64, Siemens Medical Solutions, Forchheim, Germany). In preparation for the scan, all subjects with a heart rate >60 beats per minute received a β-blocker (intravenous metoprolol, 5 to 20 mg), unless contraindications were present.
Contrast-enhanced MDCT data were acquired with the use of a spiral scan with a 32×0.6 mm slice collimation, a gantry rotation time of 330 ms, tube voltage of 120 kV, and an effective tube current of 850 to 950 mAs depending on subject body mass index. A total of 64 overlapping 0.6 slices per rotation were acquired with the use of a focal spot periodically moving in the longitudinal direction. ECG correlated tube current modulation was used to restrict full tube current to the diastolic phase of the cardiac cycle. Contrast agent (Iodhexodol 320 g/cm3, Visipaque, General Electrics Healthcare) was injected intravenously at a rate of 5 mL per second (mean volume 78±11 mL) after a delay, which was calculated during a dedicated timing bolus acquisition. The contrast injection was immediately followed by a 40 mL saline flush injected at a rate of 5 mL per second.
Overlapping transaxial images were reconstructed using a medium sharp convolution kernel (B25f) with an image matrix of 512×512 pixels, slice thickness, and increment of 0.75/0.4 mm using an ECG gated half-scan algorithm with a resulting temporal resolution 165 ms in the center of rotation. Image reconstruction was retrospectively gated to the ECG. The position of the reconstruction window within the cardiac cycle was individually optimized to minimize motion artifacts. In the presence of remaining motion artifacts (ie, attributable to heart rate irregularities during the acquisition), the reconstruction window was manually edited to achieve highest image quality. For all analysis, the reconstruction with the highest image quality was selected.
On average, 3 data sets per subject were reconstructed. Reconstructed MDCT data sets of all subjects were transferred to an offline workstation (Leonardo, Siemens Medical Solutions). Two observers (U.H., F.B.) with more than 3 years of experience (more than 800 cases) in cardiac CT performed the analysis HGH cardiovascular CT. First, image quality was graded on a 5-point scale (excellent, good, fair, limited, nondiagnostic). Exams in which ≥1 coronary segments were classified as nondiagnostic (presence of severe artifacts or low contrast-to-noise ratio rendering any coronary segment nondiagnostic) were excluded from analysis. Second, both observers blinded to the subject’s clinical presentation and history determined the presence and extent of NCAP, MCAP, and CAP. Initially both observers independently performed an independent reading in a subset of 100 subjects (Cohen κ=0.92 for any plaque).12 Consensus reading was performed for any subsequent analysis.
The presence and extent of NCAP, MCAP, and CAP was evaluated according to the modified American Heart Association classification (with 17-coronary segments)13 using original axial images, thin slice (5 mm) maximal intensity projections, and cross-sectional reconstructions orthogonal to the long axis of each coronary segment (0.75 mm thickness).
Noncalcified plaque (NCAP) was defined as any clearly discernible structure that could be assigned to the coronary artery wall in at least 2 independent image planes and had a CT density less than 130 HU but greater than the surrounding connective tissue. Calcified atherosclerotic plaque (CAP) was defined as any structure with a density of 130 HU or more that could be visualized separately from the contrast-enhanced coronary lumen (because its density was above the contrast-enhanced lumen), that could be assigned to the coronary artery wall, and that could be identified in at least 2 independent planes. Mixed coronary atherosclerotic plaque (MCAP) was defined as the presence of NCAP and CAP, either because it was “embedded” within noncalcified plaque or adjacent to each other within a coronary segment. All plaque components were assessed on a per segment basis. This approach has previously been validated.6,7
Covariates and Risk Factor Assessment
Covariates were assessed at the time of subject’s enrollment. Presence of cardiovascular risk factors was established from actual measurements obtained during index hospitalization. Hypertension was defined as systolic blood pressure of at least 140 mm Hg or diastolic blood pressure of at least 90 mm Hg or current antihypertensive treatment. Diabetes was defined as a fasting plasma glucose ≥126 mg/dL or treatment with a hypoglycemic agent. Hyperlipidemia was defined as total cholesterol of ≥200 mg/dL or treatment with a lipid lowering medication. Subjects were classified as smokers if they had smoked at least one cigarette per day in the year before the study. Body mass index (BMI) was defined as weight (kilograms) divided by the height squared (meters). Family history of CAD was defined as having a first-degree female (<65 years) or male (<55 years) relative with a documented history of myocardial infarction (MI) or sudden cardiac death. Subjects were classified as high-risk (>20%), intermediate (10% to 20%), or low-risk (<10%) according to their FRS.2
Descriptive characteristics for all variables were expressed as mean±SD for continuous and percentages for categorical variables. Differences between the analytic population and excluded subjects were assessed using the 2-tailed student t test (age) and chi-square test (gender).
All univariate analyses (2 group comparisons) to determine significant associations between the presence and extent of plaque composition and risk profiles (TRF, covariates and FRS category) were performed using Pearson or Spearman correlation (for normally and nonnormally distributed variables, respectively) for 2 continuous variables. Student t test or Wilcoxon Sum Rank test (for normally and nonnormally distributed variables, respectively) and Fishers exact test were used to compare continuous variables between binary predictors and to compare 2 categorical variables, respectively.
To assess whether the risk factor profiles were different across the 4 groups of subjects with no plaque, exclusively NCAP, MCAP, or CAP we performed ANOVA for normally distributed variables, Kruskal-Wallis test for nonnormally distributed continuous variables, and Chi-Sq trend test for categorical variables.
We further assessed whether risk profiles were associated with the extent of CAD detected by MDCT (defined as the number of coronary segments containing plaque) for different plaque components (NCAP, MCAP, or CAP). Age quartiles (<45, 45 to 52, 52 to 62, and >62 years of age) were selected to serve as cutoff values to assess differences in the extent of NCAP, MAP, and CAP according to age and ANOVA or Kruskal-Wallis Tests were performed to derive significance levels for normally and nonnormally distributed variables, respectively.
Further, we performed multivariable logistic regression analysis to determine the association between NCAP, MCAP, or CAP and age, gender, and individual risk factors (Model 1) or age, BMI, or FRS (Model 2). In addition, we performed multivariable linear regression analysis to determine the association between the extent of NCAP, MCAP, or CAP and age, gender, and individual risk factors (Model 1) or age, BMI, or FRS (Model 2). Model fit was assessed using c-statistics for logistic regression models and model R-Sq for linear regression. Residual diagnostics were performed for all linear regression models to verify normality of the residuals.
Finally, we determined whether NCAP is associated with individual cardiovascular risk factors or high-risk status according to FRS, independent of MCAP and CAP. We constructed separate models for individual risk factors and for high FRS as the dependent variable and the extent of NCAP as the independent variable. In each model NCAP was then additionally adjusted for the extent of MCAP and CAP.
All analyses were performed using SAS (Version 9.1, SAS Institute Inc). A 2-sided probability value <0.05 was considered to indicate statistical significance.
All authors had full access to the data and take responsibility for its integrity. All authors have read and agree with the manuscript as written.
Our study population consisted of 255 subjects of whom 251 successfully underwent 64-slice coronary MDCT (due to nausea, claustrophobia, and extravasation). Of these, we excluded 56 subjects who had either a validated history of CAD (n=41), or developed acute coronary syndrome during index hospitalization (n=24), or had nondiagnostic image quality (n=10). Thus, the analytic study population consisted of 195 subjects (91 women). Subject characteristics are detailed in Table 1. Excluded subjects were older (60±13 versus 55±12 years, P=0.007) and more likely to be male (74% versus 54%, P=0.002).
Cardiovascular Risk Profiles in Subjects Without Plaque, With Exclusively NCAP, MCAP, or CAP
Overall, coronary atherosclerotic plaque was detected in 112/195 (57%) subjects. Among them, 11 (10%) subjects had exclusively NCAP (5.6% of overall cohort), 22 subjects (20%) had exclusively CAP, and 79 subjects (70%) had both and thus were classified as having MCAP (Figure 1). Subject characteristics within the four groups are shown in Table 1.
Compared with subjects without any plaque, subjects with exclusively NCAP had a significantly higher prevalence of family history of CAD (46% versus 18%, P=0.04), significantly higher BMI (35±6 versus 29±5 kg/m2, P=0.002), and higher prevalence of hypertension and hyperlipidemia (55% versus 36%, P=0.31 and 64% versus 41%, P=0.20, for hypertension and hyperlipidemia, respectively). However, age was similar between these 2 groups (50±6 versus 49±9 years of age, P=0.83).
Compared with subjects with MCAP, subjects with exclusively NCAP were significantly younger (50±6 versus 60±12 years of age, P=0.0002) and had a significantly higher BMI (35±6 versus 28±5 kg/m2, P=0.0002). Similar differences were found for age and BMI among subject with exclusively NCAP versus exclusively CAP (P=0.04 for both).
Subjects with MCAP were significantly older (60±12 versus 49±9 years of age, P<0.0001) and had a significantly higher prevalence of hypertension (65% versus 36%, P=0.0004) and hyperlipidemia (74% versus 41%, P<0.0001) as compared with subjects without any plaque. Similar differences were found between subjects with exclusively CAP as compared with subjects without any plaque, although the comparison for hypertension and hyperlipidemia did not reach significance. The majority of subjects without any plaque were classified as low risk according to FRS (84%), whereas up to 45% of subjects who had some degree of plaque were deemed at intermediate or high FRS risk. Lipid levels and smoking were similar between the groups (P=0.90, P=0.61, P=0.64 for LDL, HDL, and smoking, respectively).
Plaque Composition Across Quartiles of Age
We observed a systematic change in plaque composition with increasing age. While the extent of NCAP decreased with age (<45: 1.08 versus 45 to 52: 0.94 versus 52 to 62: 0.83 versus >62: 0.41 segments of NCAP; P=0.06), the extent of MCAP (<45: 0.75 versus 45 to 52: 1.19 versus 52 to 62: 2.03 versus >62: 2.67 segments of MCAP; P=0.02), and CAP (<45: 1.50 versus 45 to 52: 1.83 versus 52 to 62: 1.87 versus >62: 2.95 segments of CAP; P=0.13) increased (Figure 2).
Association Between the Extent of NCAP, MCAP, or CAP With Cardiovascular Risk Profiles
When subjects were stratified into having primarily NCAP, MCAP, or CAP (defined as >70% of all segments containing plaque), subjects with primarily NCAP were younger than subjects with primarily MCAP or CAP (mean age: 49.9 versus 65.2 versus 60.7 years for subjects with primarily NCAP, MCAP, and CAP; P=0.0005), had the highest BMI (33.2 versus 28.1 versus 29.1 kg/m2, P=0.02), and the highest prevalence of family history of CAD (36% versus 21% versus 11%, P=0.05). Subjects with primarily MCAP had the highest prevalence of hypertension (84% versus 49% versus 59% for >70% MCAP, NCAP, and CAP; P=0.03) (Figure 3).
The extent of NCAP was higher in subjects with hyperlipidemia (0.3 versus 0.6 segments, P=0.02), family history of CAD (1.0 versus 0.6 segments; P=0.001), and positively correlated with BMI (r=0.17, P=0.01) but did not differ according to age, gender, hypertension, diabetes, and smoking. In contrast, the extent of MCAP and CAP was positively correlated with age (r=0.38, P<0.0001 and r=0.35, P<0.0001, respectively) and higher in subjects with hyperlipidemia (1.4 versus 0.6 segments, P=0.003; and 1.7 versus 0.7 segments, P=0.004) but not different among subjects with and without family history of CAD.
In logistic regression, younger age remained a significant predictor of primarily NCAP (OR: 0.90, P=0.01; Table 2) after adjustment for gender and TRF. In contrast, the association of family history of CAD with primarily NCAP became nonsignificant (OR: 1.70, P=0.41). In a second model, the association between BMI and primarily NCAP became nonsignificant (P=0.13) after adjustment for age and FRS. In this model, FRS was not associated with primarily NCAP (OR: 0.99, P=0.89) whereas younger age remained significantly associated (OR: 0.92, P=0.06).
In linear regression, hyperlipidemia and family history of CAD remained significantly associated with the extent of NCAP (β: 0.31, P=0.02 and β: 0.63, P=0.04, respectively; Table 3) after adjustment for age, gender, and TRF. Also, both age and gender were independently associated with the extent of MCAP and CAP (both β: 0.06, P<0.001 for age; β: 1.09, P<0.0001 and β: 0.61, P=0.04 for gender, respectively; Table 2). In a second model, the association between BMI and extent of NCAP became nonsignificant (β: 0.009, P=0.45) after adjustment for age and FRS. Also, FRS was not associated with the extent of NCAP (β: 0.02, P=0.17) but with the extent of MCAP (β: 0.07, P=0.007) and CAP (β: 0.07, P=0.008) after adjustment for age and BMI.
Association Between NCAP and Cardiovascular Risk Factors Adjusted for MCAP and CAP
Using logistic regression, hyperlipidemia (OR: 1.76 [95%-CI: 1.09 to 2.84] per segment of NCAP, P=0.02) and family history of CAD (OR: 1.42 [95%-CI: 1.0 to 2.03] per segment of NCAP, P=0.05) were significantly associated with NCAP independent of the extent of MCAP and CAP.
Using linear regression, the extent of NCAP was significantly associated with a decrease of HDL (β: −0.05 [95%- CI −0.10 to 0.01] log HDL per segment of NCAP, P=0.03) but not with age (β: −0.74 [95%-CI: −2.41 to 0.92] per segment of NCAP, P=0.38) or LDL (β: 0.04 [95%-CI−0.02 to 0.11] log LDL per segment of NCAP, P=0.20) after adjustment for MCAP and CAP.
In a logistic regression model containing high-risk FRS status (>20%) as the binary outcome, and the extent of NCAP, MCAP, and CAP as predictors, only the extent of NCAP was independently associated with high-risk FRS (OR: 1.49 [95%-CI: 1.03 to 2.17], P=0.03), while MCAP and CAP were not (1.13 [95%-CI: 0.94 to 1.35], P=0.14 and OR: 0.98 [95%-CI: 0.78 to 1.24], P=0.89 for the extent of MCAP and CAP, respectively).
In this cross-sectional study, we assessed the prevalence and extent of different coronary atherosclerotic plaque components as detected in cardiac MDCT and their association with cardiovascular risk profiles in a cohort of consecutive subjects who presented with acute chest pain in the absence of ACS or known CAD.
Our study provides a first insight into the relation of NCAP, MCAP, and CAP as our data suggest that only a small proportion of subjects have exclusively NCAP (11/195, 5.6%) as detected by coronary 64-slice MDCT. Subjects who only had NCAP were of similar age as subjects without any plaque but were approximately 10 years younger than subjects with MCAP and CAP.
Previous studies have emphasized the correlation of CAP to overall coronary atherosclerotic plaque burden. Our data may suggest that there is no constant association between CAP and NCAP. Our findings indicate that the extent of NCAP relative to CAP decreases whereas both the extent of MCAP and CAP increase with age. These data may suggest that NCAP is a feature of early atherosclerosis whereas CAP develops during the advanced stages of atherosclerotic plaque development.9
Although various studies have demonstrated a strong association between the extent of CAP and TRF,14,15 we studied the association between the different components of atherosclerotic plaque and cardiovascular risk factors. We observed that hyperlipidemia and family history of CAD were associated with the extent of NCAP, a finding which persisted after adjustment for other cardiovascular risk factors (P=0.02 and P=0.04, respectively). Interestingly, a similar association was found in models that examined whether the extent of NCAP is related to individual risk factors after adjustment for MCAP and CAP (hyperlipidemia: OR: 1.76 per segment of NCAP, P=0.02 and family history of CAD: OR: 1.42 per segment of NCAP, P=0.05). These observations are consistent with studies on endothelial dysfunction and carotid intimal-media thickness.16,17 We also found that the extent of NCAP was associated with decreased HDL (P=0.03) but not with increased LDL (P=0.20). This observation is in line with previous observations that suggest an inverse relationship between HDL-C levels and atherosclerosis.18
We observed good agreement between the FRS and the absence of plaque, as the majority of subjects without any plaque (84%) were classified at low FRS risk. However, more than half of the subjects with NCAP, MCAP, and CAP were also deemed to be at low FRS risk. Also, we found that only the extent of NCAP was associated with the high-risk category according to FRS (>20%) after adjustment for the extent of MCAP and CAP. Thus, coronary atherosclerotic plaque can be detected in a large group of patients deemed to be at low FRS risk, indicating that plaque assessment may provide useful additional information for risk assessment in this population. In contrast, in subjects deemed as high risk according to FRS the incremental value of information on NCAP may be limited. However, these findings need to be confirmed in larger trials and the appropriateness of MDCT needs to be evaluated in the context of risks arising from administration of iodinated contrast agents and radiation exposure.
The primary utility of cardiac MDCT in patients with acute chest pain is to noninvasively assess the presence of significant coronary artery disease and coronary atherosclerotic plaque in the acute setting. However, patients without previously known CAD in whom ACS was excluded during index hospitalization remain candidates for primary risk assessment using traditional risk scores such as the FRS. It is clear that ideally such associations would be studied in larger population based cohorts (ie, MESA or FHS). However, these studies are currently limited to the assessment of coronary calcification as a contrast enhanced CT study is not feasible because of the amount of radiation exposure associated with these exams.
Although our observations may provide a first insight on the association between NCAP and individual risk factors, our study is limited by its sample size, especially by the low number of subjects who had exclusively NCAP. The observational nature of our study and the cross sectional and explorative analysis limits us to generate hypotheses. Larger observational trials are necessary to confirm our findings whereas prospective longitudinal and outcome studies are necessary to determine whether noninvasive assessment of NCAP provides incremental value over CAP and MCAP for prediction of future cardiovascular events.
Although we report on CAP and NCAP, we know from studies using histology or IVUS that the overwhelming majority of CAP does have a noncalcified component. The sensitivity for detection of NCAP is a function of spatial and contrast resolution which is still limited when using MDCT. As opposed to quantification of calcified plaque, there is no established automated plaque measurement tool available. Thus, in our study similar to all previously published articles, the presence of noncalcified plaque was based on visual assessment. Moreover, the goal of our study was to determine the associations of different types of plaque as seen with cardiac CT as it is assessed and reported clinically in every patient undergoing cardiac CT for the detection of coronary stenosis.
Finally, the assessment of NCAP was limited to a per segment analysis without validation through a reference method such as IVUS. There is the possibility that quantitative measurements of actual plaque volume may have ranked participants differently according to their extent of CAD. However, data suggest that plaque volume measurements may be currently unreliable because of significant interobserver variability (up to 37%).19 Also, reported associations between TRF and CAP are based on the Agatston Score, a continuous quantitative measure of CAP.
Our study provides a first insight into the association of cardiovascular risk factors and the presence and extent of different coronary atherosclerotic plaque components in a cohort of consecutive subjects with acute chest pain but without ACS or known CAD. Our data suggest that only a small proportion of individuals have exclusively NCAP (5.6%) and that they are of similar age as compared with patients without any plaque. Subjects with CAP and MCAP were found to be significantly older. In addition, our results indicate that the relation of NCAP and CAP changes with age. Hyperlipidemia and family history of CAD may be associated with the extent of NCAP.
Sources of Funding
This work was supported by NIH (RO1 HL080053), Siemens Medical Solutions, General Electric Healthcare, and the New York Cardiac Center. Dr Seneviratne received support from The National Heart Foundation of New Zealand grant 1152.
Original received July 10, 2007; final version accepted December 15, 2007.
Wilson PW, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998; 97: 1837–1847.
Budoff MJ, Achenbach S, Blumenthal RS, Carr JJ, Goldin JG, Greenland P, Guerci AD, Lima JA, Rader DJ, Rubin GD, Shaw LJ, Wiegers SE. Assessment of coronary artery disease by cardiac computed tomography: a scientific statement from the Am Heart Association Committee on Cardiovascular Imaging and Intervention, Council on Cardiovascular Radiology and Intervention, and Committee on Cardiac Imaging, Council on Clinical Cardiology. Circulation. 2006; 114: 1761–1791.
Leber AW, Knez A, von Ziegler F, Becker A, Nikolaou K, Paul S, Wintersperger B, Reiser M, Becker CR, Steinbeck G, Boekstegers P. 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: 154.
Achenbach S, Moselewski F, Ropers D, Ferencik M, Hoffmann U, MacNeill B, Pohle K, Baum U, Anders K, Jang IK, Daniel WG, Brady TJ. Detection of calcified and noncalcified coronary atherosclerotic plaque by contrast-enhanced, submillimeter multidetector spiral computed tomography: a segment-based comparison with intravascular ultrasound. Circulation. 2004; 109: 14–17.
Rumberger JA, Simons DB, Fitzpatrick LA, Sheedy PF, Schwartz RS. Coronary artery calcium area by electron-beam computed tomography and coronary atherosclerotic plaque area. A histopathologic correlative study. Circulation. 1995; 92: 2157–2162.
Stary HC, Chandler AB, Dinsmore RE, Fuster V, Glagov S, Insull W Jr, Rosenfeld ME, Schwartz CJ, Wagner WD, Wissler RW. 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. Circulation. 1995; 92: 1355–1374.
Hoffmann U, Moselewski F, Nieman K, Jang IK, Ferencik M, Rahman AM, Cury RC, Abbara S, Joneidi-Jafari H, Achenbach S, Brady TJ. Noninvasive assessment of plaque morphology and composition in culprit and stable lesions in acute coronary syndrome and stable lesions in stable angina by multidetector computed tomography. J Am Coll Cardiol. 2006; 47: 1655–1662.
Fujii K, Kobayashi Y, Mintz GS, Takebayashi H, Dangas G, Moussa I, Mehran R, Lansky AJ, Kreps E, Collins M, Colombo A, Stone GW, Leon MB, Moses JW. Intravascular ultrasound assessment of ulcerated ruptured plaques: a comparison of culprit and nonculprit lesions of patients with acute coronary syndromes and lesions in patients without acute coronary syndromes. Circulation. 2003; 108: 2473–2478.
Hoffmann U, Nagurney JT, Moselewski F, Pena A, Ferencik M, Chae CU, Cury RC, Butler J, Abbara S, Brown DF, Manini A, Nichols JH, Achenbach S, Brady TJ. Coronary multidetector computed tomography in the assessment of patients with acute chest pain. Circulation. 2006; 114: 2251–2260.
Austen WG, Edwards JE, Frye RL, Gensini GG, Gott VL, Griffith LS, McGoon DC, Murphy ML, Roe BB. A reporting system on patients evaluated for coronary artery disease. Report of the Ad Hoc Committee for Grading of Coronary Artery Disease, Council on Cardiovascular Surgery, American Heart Association. Circulation. 1975; 51: 5–40.
Newman AB, Naydeck BL, Sutton-Tyrrell K, Edmundowicz D, O’Leary D, Kronmal R, Burke GL, Kuller LH. Relationship between coronary artery calcification and other measures of subclinical cardiovascular disease in older adults. Arterioscler Thromb Vasc Biol. 2002; 22: 1674–1679.
Clarkson P, Celermajer DS, Powe AJ, Donald AE, Henry RM, Deanfield JE. Endothelium-dependent dilatation is impaired in young healthy subjects with a family history of premature coronary disease. Circulation. 1997; 96: 3378–3383.
Leber AW, Becker A, Knez A, von Ziegler F, Sirol M, Nikolaou K, Ohnesorge B, Fayad ZA, Becker CR, Reiser M, Steinbeck G, Boekstegers P. 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.