Quantitative coronary arterial stenosis assessment by multidetector CT and invasive coronary angiography for identifying patients with myocardial perfusion abnormalities
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- Godoy, G.K., Vavere, A., Miller, J.M. et al. J. Nucl. Cardiol. (2012) 19: 922. doi:10.1007/s12350-012-9598-6
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Semi-quantitative stenosis assessment by coronary CT angiography only modestly predicts stress-induced myocardial perfusion abnormalities. The performance of quantitative CT angiography (QCTA) for identifying patients with myocardial perfusion defects remains unclear.
CorE-64 is a multicenter, international study to assess the accuracy of 64-slice QCTA for detecting ≥50% coronary arterial stenoses by quantitative coronary angiography (QCA). Patients referred for cardiac catheterization with suspected or known coronary artery disease were enrolled. Area under the receiver-operating-characteristic curve (AUC) was used to evaluate the diagnostic accuracy of the most severe coronary artery stenosis in a subset of 63 patients assessed by QCTA and QCA for detecting myocardial perfusion abnormalities on exercise or pharmacologic stress SPECT.
Diagnostic accuracy of QCTA for identifying patients with myocardial perfusion abnormalities by SPECT revealed an AUC of 0.71, compared to 0.72 by QCA (P = .75). AUC did not improve after excluding studies with fixed myocardial perfusion abnormalities and total coronary arterial occlusions. Optimal stenosis threshold for QCTA was 43% yielding a sensitivity of 0.81 and specificity of 0.50, respectively, compared to 0.75 and 0.69 by QCA at a threshold of 59%. Sensitivity and specificity of QCTA to identify patients with both obstructive lesions and myocardial perfusion defects were 0.94 and 0.77, respectively.
Coronary artery stenosis assessment by QCTA or QCA only modestly predicts the presence and the absence of myocardial perfusion abnormalities by SPECT. Confounding variables affecting the relationship between coronary anatomy and myocardial perfusion likely account for some of the observed discrepancies between coronary angiography and SPECT results.
KeywordsCT angiographySPECTmyocardial ischemiacardiac computed tomography
CT coronary angiography accurately identifies patients with obstructive coronary artery disease (CAD).1 Since percutaneous coronary intervention appears to be most beneficial in hemodynamically significant coronary arterial stenoses, identifying such lesions as opposed to merely obstructive stenoses may be more desirable.2 If anatomic assessment of atherosclerotic lesions is capable of predicting hemodynamic significance remains controversial. In support of this notion, intravascular ultrasound (IVUS) assessment of lumen obstruction correlates well with hemodynamic evaluation by coronary flow reserve.3 Conventional quantitative coronary angiography (QCA), on the other hand, correlates poorly with fractional flow reserve (FFR) possibly because of its known limitations in accurately assessing complex lumen geometry.4-7 CT angiography shares some of the favorable features of IVUS, i.e., allowing cross-sectional lumen analysis, and thus, may be similarly positioned to accurately assess luminal dimensions. Indeed when compared to IVUS, lumen area measurements by CT agreed better than those by QCA.8 We recently showed that CT angiography more accurately quantifies lumen diameter stenosis than QCA in phantom vessels with non-circular geometry.9 Yet in two studies, CT assessment of lumen stenoses was not more accurate than QCA in predicting hemodynamic significance by FFR.10,11 These investigations, however, were conducted using semi-quantitative CT stenosis assessment possibly limiting the identification of an anatomical threshold that best predicts blood flow restriction. Accordingly, the purpose of this study was to use quantitative CT angiography (QCTA) for coronary artery stenosis assessment in comparison to QCA for identifying patients with myocardial perfusion abnormalities.
The Coronary Artery Evaluation Using 64-Row (CorE-64) Multi-Detector Computed Tomography Angiography study is a prospective, multicenter study performed at nine hospitals in seven countries to evaluate the diagnostic accuracy of QCTA for detecting coronary artery stenoses in patients with suspected obstructive CAD.12 All centers received study approval from their local institutional review boards, and all patients gave written informed consent. In a subset of 63 patients, clinically driven myocardial stress perfusion studies were performed prior to CTA and conventional coronary angiography, which represents the study population for this investigation.
The patient population of the CorE-64 international study has been described in detail elsewhere.12 In brief, 405 study participants were selected for the study according to the following criteria: patients who are at least 40 years of age, with symptoms of relevant CAD and indication for conventional coronary angiography. Patients were not eligible if they had history of cardiac surgery, allergy to iodinated contrast or contrast induced nephropathy, multiple myeloma, organ transplantation, renal insufficiency, atrial fibrillation, New York Heart Association class III or IV heart failure, aortic stenosis, percutaneous coronary intervention within the past 6 months, intolerance to beta-blockers, or a body-mass index of more than 40. Patients with Agatston calcium scores of 600 or greater were prespecified to be excluded from the primary analysis of the CorE-64 study but were included for secondary analyses performed identically to the main cohort. Thus, in contrast to the main study cohort, patients with calcium score of 600 and greater were included in this investigation. Of the entire CorE-64 cohort, the patient population for this investigation consists of 63 patients who underwent clinically driven nuclear stress perfusion imaging prior to CT imaging.
Image Acquisition and Data Analysis by 64-Row CTA
Image Acquisition and Data Analysis by Conventional Coronary Angiography
Conventional coronary angiography was performed no later than 30 days after CT angiography using conventional techniques of QCA. All coronary segments with 1.5 mm or more in diameter were analyzed visually and quantitatively using the classification of a 29-segment standard model15 which was condensed to 19 segments for the equivalence of the number of coronary segments used in evaluation by CT. Evaluation by QCA was performed by two experienced readers blinded to the results of CT and SPECT using the software (CAAS II version 2.0.1 Research QCA, Pie Medical Imaging) in all coronary segments revealing diameter stenoses of 30% or greater by visual inspection.
Image Acquisition and Data Analysis by Myocardial Perfusion Imaging (SPECT)
All SPECT studies were performed and interpreted at the CorE-64 study sites according to the standards recommended by the American Society of Nuclear Cardiology.16 Myocardial perfusion imaging studies were performed using 1- or 2-day protocols with either pharmacological agents (dipyridamole, adenosine, or dobutamine) or exercise stress. The radiotracers utilized were 99mTc-sestamibi, 99mTc-tetrofosmin, and thallium-201 at doses from 2 to 3 mCi for thallium-201, 7 to 10 mCi for 99mTc-sestamibi or 99mTc-tetrofosmin at rest and about three times more (21 to 30 mCi) of radiotracers in the last stress stage. Only one patient underwent myocardial perfusion using a dual-isotope protocol with the intravenous administration of thallium-201 during rest and 99mTc-setamibi during the stress stage. Standard perfusion stages of rest and stress were performed at baseline and with exercise or pharmacological stress. Myocardial perfusion was visually evaluated by the attending physician at the study sites. Assessment for myocardial perfusion abnormalities was performed based on the intensity of tracer uptake compared to a normal reference segment and based on the size of the affected myocardium area in relation to the entire myocardium. Perfusion abnormalities were graded for size and intensity as mild, moderate, and large and allocated to a myocardial region as recommended by the American Society of Cardiology.16 Validated myocardial perfusion quantitation software, e.g., QPS (Cedars-Sinai Medical Center, Los Angeles, CA) was used at the discretion of the attending physician at study sites. A perfusion defect was defined as reversible if the change in regional activity was not evident on rest images. Results were sent to the CorE-64 core laboratory for analysis and comparison with QCTA and QCA.
Statistical analyses were performed with Stata Statistical Software (Release 10.0, Stata Corporation, College Station, TX, 2007). To evaluate the diagnostic performance of coronary artery stenosis assessment by QCTA for identifying patients with myocardial perfusion defects (reference standard), we performed a patient-based receiver-operating-characteristic (ROC) curve as the measure of diagnostic accuracy. ROC analysis was applied to compare the diagnostic performance of QCTA and conventional angiography (using the threshold for significant coronary stenoses as the variable parameter) for identifying patients with perfusion defects by comparing the respective areas under the ROC curve (AUCs). Optimal performance was defined as diagnostic accuracy that yielded a balance of high sensitivity and specificity for a given threshold. Univariable logistic regression analysis was used to compare the findings from QCTA and QCA with the myocardial perfusion imaging results. The regression results are presented as odds ratios and their respective 95% CIs. All tests were two-tailed, the significance threshold was P < .05, and confidence intervals were 95%.
62.3 ± 9.2
Body Mass Index (%)
Family history of premature CAD (%)
SPECT exam parameters
Exercise stress (%)
Pharmacological stress (%)
Diagnostic Accuracy of QCTA for Detecting Myocardial Perfusion Defects Using Predefined Stenosis Thresholds
Diagnostic accuracy of QCTA for identifying patients with any perfusion defects by SPECT
Diagnostic accuracy of QCA and QCTA for identifying patients with only reversible perfusion defects by SPECT
Diagnostic Accuracy of QCA for Detecting Myocardial Perfusion Defects Using Predefined Stenosis Thresholds
Diagnostic accuracy of QCA for identifying patients with any perfusion defects by SPECT
Quantitative Stenosis Assessment by QCA and QCTA for Identifying Patients with Myocardial Perfusion Abnormalities
Diagnostic accuracy of QCA and QCTA for identifying patients with perfusion defects by SPECT after exclusion of total coronary arterial occlusions
Reversible defects only
Diagnostic Accuracy of QCTA to Identify Patients with a Combined Myocardial Perfusion Defect and ≥50% Stenosis by QCA
Sensitivity, specificity, PPV, and NPV for QCTA (50% stenosis threshold) to identify patients with both myocardial perfusion abnormality by SPECT and ≥50% coronary arterial stenosis by QCA were 94, 77, 81, 91%, respectively. Using a lower stenosis threshold (40%)—as commonly done in clinical practice as gatekeeper for invasive angiography—sensitivity, specificity, PPV, and NPV were 97, 54, 75, and 93%, respectively.
We found similar modest accuracy for quantitative coronary arterial stenosis assessment by QCTA and QCA for identifying patients with myocardial perfusion defects by SPECT. Using coronary arterial stenosis thresholds ranging from 30 to 100% by either QCTA or QCA did not yield high accuracy for identifying patients with myocardial perfusion abnormalities by SPECT. Rather, in some instances, myocardial perfusion abnormalities were associated with lower grade arterial stenoses and in others they paired with higher grade lumen obstruction. The accuracy for either method did not increase when only reversible perfusion defects were considered or when patients with total coronary arterial occlusions were excluded from analysis. Importantly, however, the sensitivity of QCTA to identify patients with combined myocardial perfusion defects by SPECT and obstructive CAD by QCA was high.
The relationship between coronary arterial anatomy and blood flow restrictions causing myocardial ischemia is complex. Accordingly, a single stenosis threshold, e.g., 50 or 70% coronary arterial diameter stenosis, is unlikely to identify most patients with myocardial perfusion defects. In this investigation, we used quantitative coronary arterial stenosis measurements by QCTA to assess the relationship between coronary anatomy and myocardial ischemia over a wide range of stenoses (30-100%). However, compared to previous reports using semi-QCTA (e.g., using predefined visual stenosis thresholds of 25, 50% etc.) our results were similar, which may suggest that the application of QCTA does not confer an advantage over semi-quantitative, categorical assessment.17-21 Our reported sensitivity and specificity for QCTA to identify patients with inducible ischemia by SPECT are similar to those reported by Hacker et al19 On the other hand, Gaemperli et al18 reported higher sensitivity, whereas Bauer et al22 found lower sensitivity for identifying patients with myocardial perfusion defects by QCTA. Predictive values highly varied among studies as one would expect in patient populations with different disease prevalence.1
Possible explanations for the modest performance of QCTA to predict myocardial perfusion defects may include its poorer spatial resolution compared to QCA. However, at least in phantom studies, QCTA appeared quite capable of accurate lumen quantification and in fact had greater accuracy for stenosis assessment in lumen with non-circular geometry.9 Other explanations include the lack of prospective assessment, i.e., coronary artery lesions were assessed by QCTA with the intention to match QCA assessment and not to assess for hemodynamic significance. Finally, diameter stenosis was used in this study to determine lumen obstruction whereas luminal area assessment may be closer associated with hemodynamic significance.23
Besides technical factors, the modest association of anatomic assessment by either QCA or QCTA with functional assessment for the evaluation of CAD may also be explained by the complexity of factors leading to myocardial ischemia. In addition to the degree of luminal obstruction, the number of stenoses, extent of atherosclerotic plaque present, lesion length, extent of collateral flow, endothelial dysfunction, microvascular function, and possibly other factors and/or a combination of these influence the probability of myocardial ischemia.24 In CorE-64, disease prevalence and morbidity were very high which increases the chance of microvascular dysfunction and perfusion defects even in the absence of flow-limiting stenoses, possibly explaining the relative high probability of ischemia even with lower degree stenoses (Figure 4). One may therefore argue it is unrealistic to expect high accuracy for simple arterial diameter stenosis measurements to predict a fairly complex outcome. The complexity of the matter may be further illustrated by the relationship between FFR and myocardial ischemia detected by SPECT. While the coronary blood flow characteristics assessed by FFR in the epicardial coronary artery is expected to predict myocardial ischemia even in the setting of collateral flow, several studies revealed a modest correlation between FFR and myocardial perfusion abnormalities in patients with multivessel disease.25,26 Di Carli et al27 have shown that even when utilizing a combination of anatomic and physiological assessment in a single method by PET-CT, the correlation of both CTA and PET-CT for predicting myocardial ischemia is poor. In contrast to many studies reporting overestimation of stenoses compared to QCA, we found no such trend in our study. Reasons for this discrepancy includes the use of quantitative as opposed to visual assessment—which is known to result in lower stenosis estimates28—as well as a conscious effort by CorE-64 readers not to overcall stenoses.1
The primary objective of the CorE-64 study was to investigate the accuracy of 64-slice QCTA for detecting obstructive CAD compared to QCA. Since comparison with myocardial perfusion defects was not the primary goal, only a subset of patients underwent SPECT imaging, limiting this analysis. In contrast to QCTA and QCA protocols and analyses, no single protocol was followed for SPECT acquisition or was its analysis performed in an independent core laboratory. Indeed, SPECT results were likely to have influenced referral for cardiac catheterization and study enrollment, and likely increased the probability of positive SPECT results among this cohort. Although myocardial perfusion imaging by SPECT is an accepted standard for assessing myocardial perfusion abnormalities in patients with known or suspected CAD, the technique only allows the detection of relative myocardial perfusion differences which particularly affects patients with multivessel or left main CAD. Finally, all analyses were based on patients but not on a vessel level, precluding conclusions on associations of lesion location and myocardial perfusion abnormalities. An analysis on a vessel level was not attempted because of the variability of the coronary arterial anatomy associated with myocardial perfusion territories.29
QCTA is no more accurate than QCA for identifying patients with myocardial perfusion defects by SPECT. While the probability of myocardial ischemia increases with the degree of diameter stenosis, the accuracy for QCTA or QCA for identifying patients with myocardial perfusion abnormalities is similarly modest. Given the complexity of factors involved leading to myocardial ischemia, simple arterial diameter measurements appear inadequate for accurate prediction of blood flow restrictions.