Meta-analysis: Noninvasive Coronary Angiography Using Computed Tomography Versus Magnetic Resonance Imaging

Department of Radiology, Charité Medical School, Humboldt-Universität zu Berlin, Freie Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany.
Annals of internal medicine (Impact Factor: 17.81). 02/2010; 152(3):167-77. DOI: 10.1059/0003-4819-152-3-201002020-00008
Source: PubMed


Two imaging techniques, multislice computed tomography (CT) and magnetic resonance imaging (MRI), have evolved for noninvasive coronary angiography.
To compare CT and MRI for ruling out clinically significant coronary artery disease (CAD) in adults with suspected or known CAD.
MEDLINE, EMBASE, and ISI Web of Science searches from inception through 2 June 2009 and bibliographies of reviews.
Prospective English- or German-language studies that compared CT or MRI with conventional coronary angiography in all patients and included sufficient data for compilation of 2 x 2 tables.
2 investigators independently extracted patient and study characteristics; differences were resolved by consensus.
89 and 20 studies (comprising 7516 and 989 patients) assessed CT and MRI, respectively. Bivariate analysis of data yielded a mean sensitivity and specificity of 97.2% (95% CI, 96.2% to 98.0%) and 87.4% (CI, 84.5% to 89.8%) for CT and 87.1% (CI, 83.0% to 90.3%) and 70.3% (CI, 58.8% to 79.7%) for MRI. In studies that included only patients with suspected CAD, sensitivity and specificity of CT were 97.6% (CI, 96.1% to 98.5%) and 89.2% (CI, 86.0% to 91.8%). Covariate analysis yielded a significantly higher sensitivity for CT scanners with more than 16 rows (98.1% [CI, 97.0% to 99.0%]; P < 0.050) than for older-generation scanners (95.6% [CI, 94.0% to 97.0%]). Heart rates less than 60 beats/min during CT yielded significantly better values for sensitivity than did higher heart rates (P < 0.001).
Few studies investigated coronary angiography with MRI. Only 5 studies were direct head-to-head comparisons of CT and MRI. Covariate analyses explained only part of the observed heterogeneity.
For ruling out CAD, CT is more accurate than MRI. Scanners with more than 16 rows improve sensitivity, as do slowed heart rates. Primary Funding Source: None.

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Available from: Niki-Maria Zacharopoulou, Oct 06, 2015
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    • "In a simulation study, the LCBM is compared to the standard BM in terms of bias, power, and confidence. Moreover, it is applied to a well-known dataset on the diagnostic performance of multislice computed tomography and magnetic resonance imaging for the diagnosis of coronary artery disease [16,29]. "
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    ABSTRACT: Background Several types of statistical methods are currently available for the meta-analysis of studies on diagnostic test accuracy. One of these methods is the Bivariate Model which involves a simultaneous analysis of the sensitivity and specificity from a set of studies. In this paper, we review the characteristics of the Bivariate Model and demonstrate how it can be extended with a discrete latent variable. The resulting clustering of studies yields additional insight into the accuracy of the test of interest. Methods A Latent Class Bivariate Model is proposed. This model captures the between-study variability in sensitivity and specificity by assuming that studies belong to one of a small number of latent classes. This yields both an easier to interpret and a more precise description of the heterogeneity between studies. Latent classes may not only differ with respect to the average sensitivity and specificity, but also with respect to the correlation between sensitivity and specificity. Results The Latent Class Bivariate Model identifies clusters of studies with their own estimates of sensitivity and specificity. Our simulation study demonstrated excellent parameter recovery and good performance of the model selection statistics typically used in latent class analysis. Application in a real data example on coronary artery disease showed that the inclusion of latent classes yields interesting additional information. Conclusions Our proposed new meta-analysis method can lead to a better fit of the data set of interest, less biased estimates and more reliable confidence intervals for sensitivities and specificities. But even more important, it may serve as an exploratory tool for subsequent sub-group meta-analyses.
    BMC Medical Research Methodology 07/2014; 14(1):88. DOI:10.1186/1471-2288-14-88 · 2.27 Impact Factor
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    • "Clinical evaluation of CAD patients is based on the assessment of CAD likelihood using traditional risk factors. Patients with intermediate probability are typically evaluated by functional noninvasive testing to detect ischemia, while those with highlikelihood are referred for invasive coronary angiography to confirm luminal stenosis [13], or more recently, by contrastenhanced coronary computed tomography angiography (CTA) [14]. Calcified plaque can be quantified by non-contrast-enhanced coronary artery calcium (CAC) scanning and overall coronary plaque can be directly visualized and quantified by CTA [15]. "
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    ABSTRACT: Objective We previously validated a gene expression score (GES) based on age, sex and peripheral blood cell expression levels of 23 genes measured by quantitative real-time PCR (qRT-PCR) for diagnosis of obstructive coronary artery disease (CAD) (≥50% luminal diameter stenosis). In this study we sought to determine the association between the GES and coronary arterial Plaque Burden and Stenosis by CT-angiography. Methods A total of 610 patients (mean age: 57 ± 11; 50% male) from the PREDICT and COMPASS studies from 59 centers were analyzed. Coronary artery calcium (CAC) scoring, CT angiography (CTA)-based plaque and stenosis and GES measurements were performed. CAC was expressed as Agatston score and CTA evaluated for stenosis severity: 0. None; 1. Minimal, 2. Mild, 3. Moderate, 4. Severe and 5. Occluded. Correlation analysis, one-way analysis of variance (ANOVA) and receiver operating characteristics (ROC) analyses were performed. Results GES was significantly associated with plaque burden by CAC (r = 0.50; p < 0.001) and CTA (segment involvement score index: r = 0.37, p < 0.001); a low score (≤15) had sensitivity of 0.71 and a high score (≥28) a specificity of 0.97 for the prediction of zero vs. non-zero CAC. Increasing GES was associated with a greater degree of categorical stenosis by ANOVA (p < 0.001); GES significantly correlated with maximum luminal stenosis (r = 0.41; p < 0.01) and segment stenosis score index (r = 0.38; p < 0.01). A low score had sensitivity of 0.90 and a high score a specificity of 0.87 for ≥70% stenosis. Conclusions A previously validated GES is significantly associated with Plaque Burden and Stenosis by CT. Clinical trial registration. (PREDICT [NCT00500617] and COMPASS [NCT01117506]),
    Atherosclerosis 03/2014; 233(1):284–290. DOI:10.1016/j.atherosclerosis.2013.12.045 · 3.99 Impact Factor
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    • "In particular, the CAD diagnosis is not only dependent upon the accuracy of CTA, but also upon pre-test probability, which is estimated according to the symptoms and examinations [3], [6]–[8]. The pre-test probability categorization is important because of its significant impact on the post-test probability of disease and the selection of a diagnostic test [8]. "
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    ABSTRACT: To comprehensively investigate the diagnostic performance of coronary artery angiography with 64-MDCT and post 64-MDCT. PubMed was searched for all published studies that evaluated coronary arteries with 64-MDCT and post 64-MDCT. The clinical diagnostic role was evaluated by applying the likelihood ratios (LRs) to calculate the post-test probability based on Bayes' theorem. 91 studies that met our inclusion criteria were ultimately included in the analysis. The pooled positive and negative LRs at patient level were 8.91 (95% CI, 7.53, 10.54) and 0.02 (CI, 0.01, 0.03), respectively. For studies that did not claim that non-evaluable segments were included, the pooled positive and negative LRs were 11.16 (CI, 8.90, 14.00) and 0.01 (CI, 0.01, 0.03), respectively. For studies including uninterruptable results, the diagnostic performance decreased, with the pooled positive LR 7.40 (CI, 6.00, 9.13) and negative LR 0.02 (CI, 0.01, 0.03). The areas under the summary ROC curve were 0.98 (CI, 0.97 to 0.99) for 64-MDCT and 0.96 (CI, 0.94 to 0.98) for post 64-MDCT, respectively. For references explicitly stating that the non-assessable segments were included during analysis, a post-test probability of negative results >95% and a positive post-test probability <95% could be obtained for patients with a pre-test probability of <73% for coronary artery disease (CAD). On the other hand, when the pre-test probability of CAD was >73%, the diagnostic role was reversed, with a positive post-test probability of CAD >95% and a negative post-test probability of CAD <95%. The diagnostic performance of post 64-MDCT does not increase as compared with 64-MDCT. CTA, overall, is a test of exclusion for patients with a pre-test probability of CAD<73%, while for patients with a pre-test probability of CAD>73%, CTA is a test used to confirm the presence of CAD.
    PLoS ONE 01/2014; 9(1):e84937. DOI:10.1371/journal.pone.0084937 · 3.23 Impact Factor
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