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Evidence-based Practice |
1 From the Department of Radiology and Medical Imaging (P.K.V., R.V.H., I.D., L.R.V.H.) and Cardiovascular Center Aalst (I.D., W.W.), OLV Ziekenhuis Aalst, Moorselbaan 164, 9300 Aalst, Belgium; and Program for the Assessment of Radiological Technology, Department of Epidemiology & Biostatistics and Department of Radiology, Erasmus MC–University Medical Center Rotterdam, Rotterdam, the Netherlands (M.H.H., M.G.M.H.). Received July 14, 2006; revision requested September 18; revision received November 27; final version accepted January 2, 2007. Address correspondence to P.K.V. (e-mail: piet{at}vanhoenacker.be).
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONADVANCE IN KNOWLEDGEIMPLICATIONS FOR PATIENT CAREReferences |
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Materials and Methods: A PubMed and manual search of the literature published between January 1998 and May 2006 on use of multidetector CT angiography compared with coronary angiography in patients with symptomatic coronary artery disease was performed. Summary estimates of diagnostic odds ratio, sensitivity, and specificity were calculated. Random-effects models were used to compare the diagnostic performance of four-, 16-, and 64-detector CT angiographic units, and the proportion of nonassessable coronary arterial segments was evaluated.
Results: Fifty-four studies were included in the meta-analysis: 22 studies with four-detector CT angiography, 26 with 16-detector CT angiography, and six with 64-detector CT angiography. The pooled sensitivity and specificity for detecting a greater than 50% stenosis per segment were 0.93 (95% confidence interval [CI]: 0.88, 0.97) and 0.96 (95% CI: 0.96, 0.97) for 64-detector CT angiography, 0.83 (95% CI: 0.76, 0.90) and 0.96 (95% CI: 0.95, 0.97) for 16-detector CT angiography, and 0.84 (95% CI: 0.81, 0.88) and 0.93 (95% CI: 0.91, 0.95) for four-detector CT angiography, respectively. Results of regression analysis indicated that the diagnostic performance significantly improved with the newer generations of multidetector CT scanners (64- and 16-detector vs four-detector units), adjusted for exclusion of nonassessable segments, and contrast agent concentration used (P < .05). Simultaneously, the nonassessable proportion of segments significantly decreased with the newer generations of multidetector CT scanners, adjusted for heart rate, prevalence of significant disease, and mean age.
Conclusion: With the newer generations of multidetector CT scanners, the diagnostic performance for the assessment of coronary artery disease has significantly improved, and the proportion of nonassessable segments has decreased.
Supplemental material:
radiology.rsnajnls.org/cgi/content/full/244/2/419/DC1
radiology.rsnajnls.org/cgi/content/full/244/2/419/DC2
radiology.rsnajnls.org/cgi/content/full/244/2/419/DC3
© RSNA, 2007
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONADVANCE IN KNOWLEDGEIMPLICATIONS FOR PATIENT CAREReferences |
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Contrast material–enhanced multidetector computed tomographic (CT) angiography has emerged as a noninvasive method for imaging the coronary arterial tree. Since the advent of hardware with multiple detectors, more than one section can be acquired in a single gantry rotation, and this capability has improved the spatial resolution of the images. The diagnostic performance of multidetector CT angiography for the assessment of coronary artery disease has been evaluated in multiple studies from 1998. Investigators in these studies reported highly varying estimates of sensitivity, specificity, and related statistics and used different scanner hardware and scanning protocols in various settings.
Since the advent of scanners with 64 detectors, investigators claim that the diagnostic performance of the technique has improved significantly. To our knowledge, however, no study has statistically proved that this claim is correct. Because of the differences between study protocols and settings, it is hazardous to simply compare new results with previously published results if the goal is to evaluate the effect of technical advances on diagnostic performance. In the first studies of multidetector CT angiography, for example, many coronary arterial segments were excluded from the statistical analysis on the grounds that the stenosis in these segments could not be assessed, and such an exclusion may have resulted in overly optimistic estimates of diagnostic performance.
So far, two systematic reviews about multidetector CT angiography for coronary artery disease have been published in the literature (3,4). In one review (3), 64-detector CT was not included, and in the other review (4), only one study was included. Furthermore, no summary receiver operating characteristic (sROC) analysis was performed, although this is the preferred method for analysis of diagnostic data from multiple studies (5), and no investigation of the influence of study variables or adjustment for differences across studies was performed.
The purpose of our study was to review the literature about the diagnostic performance of multidetector CT angiography for assessment of symptomatic coronary artery disease, with conventional coronary angiography as the reference standard.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONADVANCE IN KNOWLEDGEIMPLICATIONS FOR PATIENT CAREReferences |
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Reference lists of review articles and cited articles were used to locate additional studies. The following journals were hand searched from January 1998 to May 2006: European Radiology, Radiology, RadioGraphics, American Journal of Roentgenology, Journal of Computer Assisted Tomography, Journal de Radiologie, Heart, Lancet, New England Journal of Medicine, Journal of the American Medical Association, Journal of the American College of Cardiology, American Journal of Cardiology, American Heart Journal, Circulation, Hypertension, Circulation Research, European Heart Journal, and British Medical Journal. English, Dutch, French, German, Italian, and Spanish articles were included because the authors were familiar with these languages. The exact search strategy can be found in Appendix E1 (radiology.rsnajnls.org/cgi/content/full/244/2/419/DC1).
Studies were included in the meta-analysis if they met the following inclusion criteria: The data were acquired with a multidetector CT scanner with at least four detectors; conventional angiography was used as the reference standard in all patients; clinical suspicion of ischemic coronary artery disease was the reason for referral; the criteria for a positive result of multidetector CT angiography and coronary angiography were explicitly defined as a stenosis of greater than 50% diameter; and the absolute numbers of true-positive, false-negative, false-positive, and true-negative test results were available or could be derived from the available data or from the authors. These absolute numbers were accepted if they were derived on a per-segment basis, on a per-vessel basis, or on a per-patient basis. For segmental analysis, we used an adapted form of the 15-segment scheme of the coronary arterial tree of the American Heart Association (6).
Studies were excluded on the basis of the following criteria: Not all patients were tested with the reference test (possible referral bias); there was an inability to obtain original numbers of false-positive, false-negative, true-positive, and true-negative results; the article was a review article or an editorial; the subject was exclusively the calcium score; the modality was exclusively electron-beam CT; there was no comparison with coronary angiography as the reference standard; there was possible overlapping of study samples; the modality was exclusively intravascular ultrasonography (US); the target population was explicitly different; and miscellaneous factors (Table 1).
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Data Extraction
The study parameters were extracted first independently and subsequently in consensus if a disagreement existed between the observers (P.K.V., R.V.H.) concerning the numeric value of a parameter. Data were extracted from the original articles by taking into account the Standards for Reporting of Diagnostic Accuracy (also known as STARD) checklist (7).
The absolute numbers of false-negative, false-positive, true-positive, and true-negative test results were retrieved, calculated, or requested from the authors. The numbers were calculated with Bayes theorem if only values for sensitivity, specificity, and predictive values were reported. These calculations were performed separately for per-patient, per-segment, and per-vessel data, if available.
Multidetector CT angiographic results were considered true-positive per patient, per vessel, or per segment if at least one significant stenosis (>50%-diameter stenosis) was found on a multidetector CT angiogram in, respectively, the investigated patient, vessel, or segment and was confirmed on a coronary angiogram. Multidetector CT angiographic results were considered true-negative if significant stenoses were correctly ruled out. Multidetector CT angiographic results were considered false-negative if no significant stenoses were found on the multidetector CT angiogram and at least one significant stenosis was found on the coronary angiogram. Multidetector CT angiographic results were considered false-positive if at least one significant stenosis was found and the coronary angiogram showed no significant stenoses.
Data Synthesis and Statistical Analysis
Interobserver agreement for study selection was evaluated with the Cohen 
test according to which a value greater than 0.80 is considered to imply very good to excellent agreement.
The main analysis was performed at the level of coronary arterial segments, as the focus in most studies was on this level of information. Secondary analyses were performed on available per-patient and per-vessel data.
We evaluated potential heterogeneity and inconsistency between publications (8,9) expressed with the Higgins and Thompson index, which is used for calculation of the I2 statistic and is a derivative of the Cochran Q statistic (9–12). The Cochran Q statistic displays a low power for detection of inconsistency when the number of studies is small and a high power for detection when the number of studies is large.
Publication bias was assessed according to the method introduced by Egger et al (13) and Sterne et al (14). The existence of publication bias is expressed as an intercept value and is zero if no publication bias is found. Funnel plots for graphic analysis of publication bias were constructed. A funnel plot is a plot of some measure of sample size of each study, such as the standard error, as a function of its effect size. A distribution of the data points as an inverted funnel indicates that publication bias is highly unlikely. Calculations were performed with statistical software (StatsDirect; StatsDirect, Altrincham, Cheshire, England).
Summary estimates for sensitivity, specificity, and the overall diagnostic performance expressed in the log of the diagnostic odds ratio (D) were calculated for the three levels of analysis (per segment, per patient, and per vessel). This calculation was performed with a random-effects model, which takes into account the variability between studies (15). The three levels were further analyzed in subgroups defined by the number of detectors (four, 16, or 64) of the CT scanner. Software (Meta-DiSc, version 1.2; Clinical Biostatistics Unit–Hospital Ramon y Cajal, Madrid, Spain) was used for these analyses.
Subsequently, we performed a random-effects sROC analysis to estimate the relationship between sensitivity and specificity, and we took into account potential differences in the positivity criterion (that is, the threshold used to mark a test as positive) and other factors of heterogeneity between settings. In an sROC analysis, the logits (log odds) of sensitivity and of the remainder of specificity subtracted from one are summed to calculate D, the log of the diagnostic odds ratio, and the logits are subtracted to calculate S, a proxy for the positivity criterion of the diagnostic test (16–18). Then, a linear regression model, D = a + bS, is estimated, and the estimate is weighted by the inverse of the variance of D.
Dummy variables were added to the regression model in a meta-regression sROC analysis to compare the generations of CT scanners (64-, 16-, and four-detector CT units). An eight-detector scanner was used in only two studies, which we included in the group of studies in which 16-detector scanners were used. Additional covariates were added to the meta–regression sROC model to adjust for clinical factors and study characteristics. The evaluated covariates included study characteristics (year of publication; journal type; number of segments, patients, and vessels; proportion of nonassessable segments, patients, and vessels; exclusion vs inclusion of nonassessable segments, patients, and vessels in the analysis of diagnostic performance), patient characteristics (mean age, proportion of men, mean heart rate, beta-blocker use expressed as a dichotomous [yes or no] variable, mean calcium score, proportion of patients who had undergone previous revascularization, prevalence of disease), and CT scanning parameters (type of CT scanner, number of detectors, section thickness, technical specifications [eg, tube voltage, tube current–time product], data about reconstruction methods, and data about contrast agent administration such as rate of injection, iodine concentration, total volume of injection, and total number of grams of iodine injected).
Variables with a significance level of P 
.1 were added to the multivariable meta–regression sROC model in a stepwise forward manner. A variable was kept in the model if the P value was less than .05. A P value of .1 or less was used to add variables to the multivariable model, whereas a P value of .05 or less was used to retain variables in the model. For adding variables to the model, a larger P value was chosen so as to increase the power of finding important effects. The residual between-study variance, 
2, was used as a measure of the model fit. A lower value for 2 indicates less residual between-study variance and therefore a better model fit and a better explanatory power by the model of the heterogeneity across studies. The ß coefficients and corresponding relative diagnostic odds ratios from the meta–regression sROC analysis indicated the effect of each variable on the overall diagnostic performance.
We also evaluated the proportion of nonassessable segments as a dependent variable in a random-effects multiple regression analysis to evaluate which variables influenced this proportion. We used statistical software (Stata 8.0; Stata, College Station, Tex) for all regression analyses.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONADVANCE IN KNOWLEDGEIMPLICATIONS FOR PATIENT CAREReferences |
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The PubMed search and the manual search for original articles resulted in 928 articles. Seven hundred forty articles were excluded on the basis of their title, with 188 remaining for further evaluation. From these 188 articles, 54 were finally included in the meta-analysis (Table E1 [radiology.rsnajnls.org/cgi/content/full/244/2/419/DC3]) (19–72). Twenty-two studies were on four-detector CT, 26 were on 16-detector CT, and six were on 64-detector CT (Fig 1). Forty-nine (91%) studies supplied data on a segment level, 24 (44%) studies supplied data on a patient level, and 11 (20%) studies supplied data on a vessel level. One hundred thirty-four studies were excluded (Table 1). In Appendix E2 (radiology.rsnajnls.org/cgi/content/full/244/2/419/DC2), the studies that were excluded were cited and classified according to the reasons listed in Table 1.
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). Thirteen of 188 studies required a consensus agreement. For one study (69), additional information was required from the authors. The authors responded, and the study was included.
Data Synthesis and Statistical Analysis
A total of 30 775 segments, 2692 vessels, and 1474 patients were analyzed. Heterogeneity was present among the studies on all levels, and this presence justifies our choice of a random-effects sROC model. Results of the per-patient analysis showed the least heterogeneity (I2 = 65.95%), whereas results of the other two analyses showed considerably greater heterogeneity (per-vessel I2 = 82.09%, per-segment I2 = 94.04%).
Publication bias was considerable in the per-segment analysis (intercept, 5.19; P < .05) and lower in the per-patient analysis (intercept, 2.82; P < .05). No publication bias could be detected in the per-vessel analysis (intercept, 3.27; P > .05), but in only a limited number of articles were results presented on a per-vessel basis. Funnel plots of the studies included in the three levels of analysis are given in Figures 2–4. They show a considerable departure from the ideal funnel-shaped distribution.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONADVANCE IN KNOWLEDGEIMPLICATIONS FOR PATIENT CAREReferences |
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The result that the number of detectors significantly improved the diagnostic performance is an important observation. It should be noted that we corrected the data for exclusion of nonassessable segments, because researchers in some studies present data after exclusion of nonassessable segments and others present data without exclusion. Because image quality from examinations with scanners that had four detectors was not very good in distal segments, the number of segments that were excluded from the calculations of sensitivity and specificity was considerable, and this exclusion masked the shortcomings of the technology in early publications. Furthermore, we showed that the proportion of nonassessable segments was significantly reduced with an increase in the number of detectors. This is of major importance, because noninvasive imaging of the coronary arteries requires that the complete coronary arterial tree can be visualized with excellent sensitivity and specificity in order to become an alternative to invasive coronary angiography.
These results imply that four-detector CT should not be used for the diagnosis of coronary artery disease anymore, as the newer generations of CT scanners are significantly better. In two previously published meta-analyses (3,4), the technological advances of multidetector CT angiography were not evaluated. Only one study about 64-detector CT was previously included in a meta-analysis and the total number of studies analyzed for 16- and four-detector CT was smaller than that in the present study. Furthermore, analyses were performed only on the level of the coronary arterial segments and not on a per-patient or per-vessel level.
The results of the multivariable regression analysis for the prediction of the proportion of nonassessable segments showed that significant predictors were not only the type of CT scanner (number of detectors) but also heart rate, the prevalence of significant disease, and mean age. An increased heart rate leads to more nonassessable segments. This result for heart rate is in accordance with data in the literature (73–75) and is related to motion artifacts. A higher prevalence of diseased segments and a higher mean age also lead to a reduction in the proportion of nonassessable segments, perhaps because the presence of disease will entice the observer to make the diagnosis even if the images are suboptimal, whereas the observer may be reluctant to exclude disease if the images are suboptimal. A higher prevalence on the per-segment level also may be related to more proximal disease, and proximal segments are more often assessable than distal segments because of their larger diameter.
By using the 15-segment model approved by the American Heart Association, 15 segments per patient were included in the analysis of each source study, and such inclusion resulted in a low prevalence of disease in the total population of segments. Going from the analysis per vessel to the analysis per segment implies that there is an increased opportunity for false-negative test results, and this opportunity results in a lower sensitivity. If, for example, a vessel has three stenotic segments, then the observer who interprets the findings on a per-vessel basis will have three opportunities to make the diagnosis (true-positive results), whereas the observer who interprets the findings on a per-segment basis has three opportunities to miss the diagnosis (false-negative results). In contrast, if a vessel is normal and has been identified as such, then classifying the vessel into multiple segments leads to an increase in the number of true-negative results without an increase in false-positive results; this finding implies an increase in specificity. Similar arguments apply for the difference between the analysis on a per-patient basis versus the analysis on a per-vessel basis.
A limitation of this meta-analysis is that we were unable to demonstrate that the calcium score significantly influences diagnostic performance and the proportion of nonassessable segments when adjustments are made for other significant predictors. This finding was observed probably because calcium scores were reported in only a small number of studies (n = 10). Results of our meta-analysis indicated that, with an increase in the calcium score, the overall diagnostic performance decreased significantly, but this variable was omitted from the multivariable analysis. Our results are in agreement with those of other studies that suggest that patients with a high calcium score should be referred for coronary angiography instead of undergoing multidetector CT angiography (32,55,59,76–78).
Furthermore, heterogeneity among individual study results was observed, and such an observation is common in meta-analyses. Heterogeneity can be due to random variation between studies, variation of study characteristics, and variation in the diagnostic threshold level required for a positive result of the test (78). We were able to explain part of the heterogeneity by adding covariates to the multivariable meta–regression sROC analysis and we used a random-effects analysis to take residual heterogeneity into account.
In addition, publication bias may have affected our results. Publication bias may occur when positive studies are preferentially published and negative studies are not. A positive study in this context generally means a study with fairly high diagnostic performance. This implies that our results probably lead to an overestimation of diagnostic performance, to some degree. The magnitude of this effect is practically impossible to quantify. The effect will, however, play a similar role for all three types of CT scanners so that the comparison among the different generations of CT scanners remains valid. Furthermore, the effect of the covariates on diagnostic performance will also remain valid. We did attempt to correct for publication bias by adding the year of publication as covariate to the regression analyses. We observed that the diagnostic performance did not depend on the year of publication, but we did observe a significantly smaller proportion of nonassessable segments in later publications. In the multivariable analysis, the year of publication did not remain significant because the decrease in the proportion of nonassessable segments could be explained by other variables, such as the use of beta-blockers and 64-detector CT scanners mentioned in later publications.
Another limitation of this meta-analysis is the relatively high prevalence of coronary artery disease in the source populations, as mentioned before. The results of this study may therefore not be generalizable to low-prevalence populations, since the technique has not really been tested in these groups.
A further limitation of this meta-analysis and the comparison of the pooled estimates of sensitivity and specificity and the overall diagnostic performance is that relevant data on each analytic level often could not be retrieved from each study, and this difficulty in retrieval led to missing data in the meta-analysis. This could have resulted in a slightly distorted view of the diagnostic performance of multidetector CT angiography when the three levels of analysis were compared with each other. Nevertheless, a consistent and plausible pattern was observed: As one increases the size of the unit analyzed from coronary arterial segments, to vessels, and to patients, the sensitivity increases, the specificity decreases, and the overall diagnostic performance decreases.
Another potential flaw is that we were unable to obtain detailed data that could link segmental stenoses to their exact anatomic location, so that an index of clinical importance (79,80) could be generated and taken into account for pooled analysis. A pooled analysis that includes this information could be useful to generate exact data on the detection capability for one-, two-, and three-vessel disease and would probably be more informative than the rather crude analysis per segment, per vessel, or per patient.
In this meta-analysis, we considered only the head-to-head comparison of invasive angiography with multidetector CT angiography for the detection of significant coronary arterial stenosis. The future value of multidetector CT angiography, however, probably lies not only in the detection and classification of significant disease but rather in the determination of nonobstructive plaque burden and characterization and quantification of plaque (81).
Finally, in this study, we did not use a formal tool such as Quality Assessment of Studies of Diagnostic Accuracy included in Systematic Reviews (known as QUADAS) (82) to assess the quality of the individual studies. We did, however, analyze the specific effect of study characteristics, which we believe is a more explicit method for assessment of the effect of quality of the study design.
From the results of this meta-analysis, we conclude that, with the newer generations of multidetector CT scanners, the diagnostic performance for the assessment of significant coronary arterial stenoses (>50%-diameter stenosis) has significantly improved (P < .05) and the proportion of nonassessable segments has decreased. How this clinically relevant imaging technology will fit in the diagnostic strategy for patients with known coronary artery disease and those who are suspected of having it remains to be determined.
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ADVANCE IN KNOWLEDGE |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONADVANCE IN KNOWLEDGEIMPLICATIONS FOR PATIENT CAREReferences |
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IMPLICATIONS FOR PATIENT CARE |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONADVANCE IN KNOWLEDGEIMPLICATIONS FOR PATIENT CAREReferences |
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FOOTNOTES |
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Abbreviations: CI = confidence interval • sROC = summary receiver operating characteristic
Authors stated no financial relationship to disclose.
Author contributions: Guarantor of integrity of entire study, P.K.V.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; manuscript final version approval, all authors; literature research, P.K.V., R.V.H., I.D., L.R.V.H., W.W.; clinical studies, L.R.V.H., W.W.; statistical analysis, P.K.V., M.H.H., L.R.V.H., M.G.M.H.; and manuscript editing, P.K.V., M.H.H., R.V.H., I.D., W.W., M.G.M.H.
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONADVANCE IN KNOWLEDGEIMPLICATIONS FOR PATIENT CAREReferences |
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