Introduction

Coronary atherosclerotic disease burden strongly correlates with patient outcome [1]. Risk of adverse events from coronary artery disease (CAD) starts to rise with the presence of mild atherosclerotic disease and gradually increases with the extent of plaque burden [1]. Our current paradigm of grading the secerity of CAD by the number and location of stenoses is an approximate assessment of CAD burden as both correlate reasonably well [2]. With the availability of noninvasive, semi-automated coronary atherosclerotic plaque burden assessment by CT coronary angiography (CTA), there is growing interest in using total disease burden as an integrating metric for CAD risk assessment [3, 4]. Coronary calcium scanning approximates total atherosclerotic disease burden but – by design – does not account for non-calcified disease. Several studies have reported good diagnostic accuracy for coronary atherosclerotic burden to identify patients with hemodynamically significant CAD, as assessed by fractional flow reserve (FFR) or myocardial stress perfusion imaging [5, 6]. However, there are scarce data on how total coronary atherosclerotic disease burden assessment by CT compares to traditional CAD evaluation for predicting patient outcome. The purpose of this study was to directly compare the short-term and intermediate-term prognostic information of total coronary atherosclerotic plaque volume by CT angiography to established metrics of CAD risk assessment.

Methods

Study design and study population

The study design of the CORE320 multicenter study has been previously detailed [7]. The CORE320 study (coronary artery evaluation using 320-row Multidetector Computed Tomography Angiography and Myocardial Perfusion) is a prospective, multicenter, multinational, diagnostic study designed to compare the accuracy of combined CTA and myocardial computed tomography perfusion imaging (CTP) against the combination of invasive coronary angiography (ICA) and single-photon emission computed tomography myocardial perfusion imaging [8]. Patients 45 to 85 years of age who were referred for clinically indicated ICA for suspected or known CAD were enrolled. Pretest probability/risk was established by the method of Morise et al. [9].

CT acquisition, image reconstruction, transfer, and analysis

A detailed description of CORE320 image acquisition and interpretation methods has been published [10]. In brief, all CT images were acquired before cardiac catheterization using a single protocol developed for a 320 × 0.5 mm-detector row CT system (Aquilion ONE, Toshiba Medical Systems, Otawara, Japan). Patient preparation included oral (75–150 mg) or IV (up to 15 mg) metoprolol and sublingual, fast-acting nitrates. Coronary calcium scanning was performed using prospective ECG triggering over a single heartbeat with a gantry rotation and x-ray exposure time of 0.35 s, 0.5-mm slice collimation, tube voltage of 120 kV, and tube current adjusted to patient body mass index. For CT angiography, 50 to 70 mL of iodinated contrast (Iopamidol 370 mg iodine/mL) was injected intravenously at 4.0–5.0 mL/s for each of the separate, axial, prospectively ECG-triggered acquisitions. For all CTA acquisitions, de-identified sinograms were reconstructed, processed, and interpreted by independent core laboratories. CT data were reconstructed to generate 0.5-mm slice thickness images with a 0.25-mm increment using both a standard (FC43) and a sharp (FC05) convolution kernel. Two level III certified investigators evaluated each CTA study for the presence and severity of CAD; disagreements were resolved by consensus. Readers examined all coronary artery segments of 1.0 mm in diameter or more for the presence of CAD using a 19-segment coronary artery model. All coronary lesions with a subjective diameter stenosis of ≥ 30% underwent quantitative evaluation on a continuous scale (0–100%) using software tools (Vitrea™ FX version 3.0 workstation, Vital Images, Minnetonka, MN, USA) at the discretion of the reader. Segment stenosis score was determined as previously described [11]. Briefly, coronary segments were scored based on the presence and severity of atherosclerotic stenoses (0–3) and scores were summed for all 19 segments (total score ranging from 0 to 57) [12]. Coronary calcium was analyzed by the Agatston method [13].

Coronary atheroma volume analysis

All reconstructed datasets were transferred to an offline workstation for quantitative coronary atheroma volume analysis using dedicated software with a semi-automated 3-dimensional (3D) contour detection algorithm (QAngio CT Research Edition version 2.0 RC4, Medis Medical Imaging Systems (MEDIS), Leiden, the Netherlands) [14,15,16]. The quantitative atheroma analysis was performed by two independent, experienced observers who were blinded to initial CTA, quantitative coronary angiography (QCA), and clinical data. On the basis of longitudinal contours, cross-sectional images at 0.5-mm intervals were obtained to create transversal lumen and vessel wall contours, using automated contour detection techniques applied to the intensity gradients in the cross sections and guided by the longitudinal contours. These cross-sectional contours were examined and, if necessary, corrected by the observer. All coronary vessels were assessed using a 19-coronary-segment model, including each epicardial vessel and side branches with at least 1.5 mm in diameter. Segments containing stents and those with poor image quality were excluded from analysis. The plaque volume was calculated by subtracting the lumen volume from the vessel volume. For each patient, the vessel, lumen, atheroma, and length values were calculated by adding all the analyzed segments. Based on prior investigations of plaque assessment, we identified percent atheroma volume as the representative metric for total coronary atherosclerotic plaque burden [6]. Percent atheroma volume (PAV) was defined as: (total atheroma volume/total vessel volume) x 100.

Invasive coronary angiography acquisition and analysis

Invasive coronary angiography was performed using standard techniques within 60 days following CTA acquisition. Quantitative coronary angiography (QCA) was performed using standard, validated analysis software (CAAS II QCA Research version 2.0.1, PIE Medical Imaging, Maastricht, The Netherlands). Coronary segments were defined using a 19-coronary-segment model, and all coronary segments 1.5 mm or more in diameter were analyzed quantitatively. Significant coronary artery stenosis (obstructive CAD) was defined as ≥ 50% diameter stenosis by QCA.

Outcome variables

Outcome variables were (1) 30-day revascularization and (2) major adverse cardiac events after 2 years follow up. Major adverse cardiac events (MACE) included cardiac death, myocardial infarction, hospitalization for acute chest pain or heart failure, arrhythmia, and late revascularization (beyond 6 months of index cardiac catheterization). Assessment of follow-up data was performed at 30 days, 6 months, 12 months, and 24 months after conventional coronary angiography. Data was obtained on office visits, telephone interviews, or mailing of a standardized questionnaire. The follow-up questionnaire assessed death, myocardial infarction, hospitalization, new or unstable angina, congestive heart failure, percutaneous intervention, and coronary artery bypass surgery. All events were adjudicated by a committee of nine cardiologists and radiologists.

Statistical analysis

Descriptive statistics of metrics were compared by Wilcoxon rank-sum test. Area under the receiver operating characteristic (ROC) curve (AUC) was used as a measure of diagnostic power. AUCs were calculated for the full group as well as the subset of patients without known CAD. Each graph includes a calibration curve (dotted line); to identify the corresponding cut point, extend a vertical line from a point on the ROC curve to the calibration curve, then a horizontal line to the right-hand ordinate, which gives the cut point. Kaplan-Meier survival curves were computed using standard methodology. All analyses were carried out in SAS 9.4 (SAS Institute, Cary, NC) and graphics were created using S-Plus 8.0 (TIBCO Spotfire, Palo Alto, ca.).

Results

Clinical characteristics

Of 391 patients included in the final CORE320 study sample, nineteen patients were excluded from this analysis because of loss to follow up or technical problems, resulting in a final study population of 372 patients. Of the 372, 86 (23%) underwent coronary artery revascularization within 30 days of invasive coronary angiography and 32 (8.6%) experienced MACE after 30 days: 20 revascularization procedures, 6 hospitalizations for chest pain, 4 myocardial infarctions, one hospitalization for heart failure and one occurrence of a new arrhythmia. Table 1 displays the clinical data and baseline characteristics for the entire study population and according to occurrence of MACE. The median age of all patients was 62 years (range 56 to 68 years): 67% were male, 33% were Asian, 11% were African-American, and 56% were Caucasian. Patients had a high prevalence of risk factors (hypertension 78%, diabetes 34%, dyslipidemia 68%, current smoker 17%, and previous percutaneous coronary intervention 30%). Patients who experienced MACE were more likely to experience angina and have high grade stenoses on coronary angiography.

Table 1 Baseline patients characteristics by event status
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Coronary artery disease evaluation

Results for the tested metrics of CAD evaluation are presented in Table 2, including a break down for subjects who experienced MACE and who did not. Notably, values for percent atheroma volume (p = 0.05), coronary artery stenosis by both CTA (p = 0.0006) and QCA (p < 0.0001), and calcium score (p = 0.051) were greater among patients with MACE compared to those without. Table 3 presents the same data after excluding patients with known CAD. The same metrics (percent atheroma volume, percent stenosis, and calcium score) showed a strong association with MACE in this context (Table 3).

Table 2 Quantified coronary artery characteristics (entire cohort)
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Table 3 Quantified coronary artery characteristics (patients without known CAD)
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Accuracy for identifying patients who required 30-day revascularization

Table 4 presents the performances of the tested metrics for identifying patients at baseline who underwent coronary artery revascularization within 30 days, including results after excluding patients with known CAD. Coronary stenosis assessment by CTA and QCA achieved greatest AUC wheras other metrics performed only modestly. Performances for all metrics improved after excluding patients with known CAD (Table 4). ROC curves are presented in Fig. 1.

Fig. 1
figure 1

Accuracy of Predicting 30-Day Revascularization. Shown are the receiver operating characteristic (ROC) curves along with their respective calibration curves for identifying patients who required coronary artery revascularization within 30 days of coronary angiography for percent atheroma volume (PAV), CT angiography (CTA), quantitative coronary angiography (QCA), coronary calcium scanning (CACS), segment stenosis score (SSS), and Clinical Risk Score by Morise. Each graph includes a calibration curve (dotted line); to identify the corresponding cut point, extend a vertical line from a point on the ROC curve to the calibration curve, then a horizontal line to the right-hand ordinate, which gives the cut point

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Table 4 Accuracy for identifying patients who required 30-day revascularization
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Accuracy for identifying patients who experienced MACE after 30 days

Table 5 presents the performances of the tested metrics for identifying patients at baseline who experienced MACE, including results after excluding patients with known CAD. Overall, metrics performed only modestly, particularly in patients with history of CAD. Coronary stenosis assessment by CTA and QCA achieved the greatest AUC. Performances for all metrics improved after excluding patients with known CAD, though statistically significantly only for PAV (p = 0.02). ROC curves are presented in Fig. 2.

Fig. 2
figure 2

Accuracy of Predicting MACE. Shown are the receiver operating characteristic (ROC) curves along with their respective calibration curves for identifying patients who experienced major adverse cardiovascular events (MACE) for percent atheroma volume (PAV), CT angiography (CTA), quantitative coronary angiography (QCA), coronary calcium scanning (CACS), segment stenosis score (SSS), and Clinical Risk Score by Morise. Each graph includes a calibration curve (dotted line); to identify the corresponding cut point, extend a vertical line from a point on the ROC curve to the calibration curve, then a horizontal line to the right-hand ordinate, which gives the cut point

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Table 5 Accuracy for identifying patients who experienced MACE
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Incremental value of metrics beyond CTA stenosis

Table 6 presents the performances of the tested metrics for identifying patients at baseline who experienced 30-day revascularization and MACE using a stepwise model to assess the incremental value of metrics beyond CTA stenosis assessment. Only QCA stenosis assement yielded statistically significant increase in diagnostic performance for both outcomes.

Table 6 Incremental value of metrics beyond CTA stenosis
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Discussion

We compared the effectiveness of frequently used cardiac CT metrics for predicting short and long term coronary artery revascularization and MACE. We found overall similar performance for PAV, coronary calcium scanning, and SSS while CTA and QCA stenosis assessment fared slightly better, particularly, for predicting short term revascularization. The favorable performance by both CTA and QCA stenosis assessment is not surprising given that most of the events were related to coronary artery revascularization procedures which were triggered by the degree of observed diameter stenosis. Indeed, it is remarkable that the metrics based on atherosclerotic disease burden assessment performed relatively well in this context.

The question of whether stenosis severity confers risk of adverse outcome through its association with disease burden or if there is independent risk associated with high grade stenoses has not been conclusively settled. While lesion stenosis severity at baseline is clearly associated with greater risk of subsequent revascularization, it appears less strongly linked with the risk of myocardial infarction and death [17]. The presence of coronary artery stenoses correlates with coronary atherosclerotic disease burden, which explains its strong association with patient outcome [18]. Given the strong correlation of coronary atherosclerotic disease burden and occurrence of myocardial infarction and death at follow up, it is conceivable that metrics capturing total disease volume yield an advantage over CAD evaluation—which only approximates such assessment.However, our data did not allow to confirm this hypothesis because of the few observed events of myocardial infarction or death.

All metrics performed better after excluding patients with known CAD which is of clinical significance since cardiac CT is predominantly used in patients without prior CAD history. The reason for the rather modest predictive value in patients with history of CAD remains speculative at this time. It is likely that the more homogeneous group of high-risk individuals allows less risk discrimination.

While PAV did not perform superiorly to standard stenosis evaluation by CTA or QCA, a semi-automated assessment of plaque burden provides conceptual advantages over categorical determination of disease presence, including the reduction of observer bias. While not tested in this study, historical data suggest greater reproducibility of plaque volume analysis compared to user depedent stenosis evaluation [19, 20].

We noted with interest that coronary calcium scanning performed less well than standard stenosis assessment and also trended inferiorly to total atherosclerotic disease burden evaluation in our study. In the CONFIRM registry, stenosis assessment by CTA provided incremental risk prediction over calcium scanning in symptomatic but not in most low risk patients [21, 22]. It is likely that the performance of calcium scanning was affected by the predominance of coronary revascularization among outcome measures. On the other hand, evaluation of total atherosclerotic disease burden by PAV still performed well, which may indicate that assessment of noncalcified coronary artery disease is relevant in the context of testing intermediate-high risk populations.

Limitations

We acknowledge several study limitations. First, the CORE320 study was not designed to address the present particular research question and thus we may not have had sufficient statistical power to conclusively demonstrate differences between groups. As power calculations would not be appropriate as a post hoc measure, we provide 95% confidence intervals for our analyses. Second, the CORE320 study population contains patients who are at higher risk than typically seen with the application of CTA. Thus, results may not be applicable to low risk populations. Third, prospectively total atheroma assessment was limited to non-stented segments. Fourth, the contour detection algorithm was upgraded and improved since our analysis and thus its current performance may exceed that of our study. It is conceivable that further software upgrades may allow including smaller segments and stented lesions and perform better with poor image quality or extensive calcification. Lastly, it should be noted that the contour detection software is for research purposes only at this time and not yet validated for clinical use.

Conclusions

Coronary atherosclerosis and stenosis assessment by CTA and QCA predict only modestly well major adverse cardiac events, consistent of predominantly revascularization procedures, in high risk patients presenting with stable symptoms. Predictive performance increases after excluding patients with prior coronary heart disease history. Stenosis assessment by CTA and QCA performed marginally better than atherosclerotic plaque burden evaluation for predicting revascularization procedures.