EXPEDITED REVIEW
Diagnostic Performance of Multislice Spiral Computed Tomography of Coronary Arteries as Compared With Conventional Invasive Coronary Angiography
A Meta-Analysis
Michèle Hamon, MD*,
Giuseppe G.L. Biondi-Zoccai, MD ,
Patrizia Malagutti, MD||,
Pierfrancesco Agostoni, MD¶,
Rémy Morello, MD ,
Marco Valgimigli, MD and
Martial Hamon, MD ,*
* Radiology
Cardiology
Statistics of the University Hospital of Caen, Caen, Normandy, France
Hemodynamics and Cardiovascular Radiology Service, Policlinico San Donato, San Donato Milanese, Milan, Italy
|| Radiology Department, Thoraxcenter, Erasmus MC, Rotterdam, the Netherlands
¶ Cardiovascular Institute Middelheim, AZ Middelheim, Antwerp, Belgium
Manuscript received July 5, 2006;
revised manuscript received August 16, 2006,
accepted August 26, 2006.
* Reprint requests and correspondence: Prof. Martial Hamon, Service des Maladies du Coeur et des Vaisseaux, Centre Hospitalier Universitaire de Caen, Avenue Côte de Nacre 14033 Caen, Normandy, France
(Email: hamon-m{at}chu-caen.fr).
OBJECTIVES: This study was designed to define the current role of multislice spiral computed tomography (MSCT) for the diagnosis of coronary artery disease (CAD) using a meta-analytic process.
BACKGROUND: Multislice spiral computed tomography has recently been proposed as an alternative to conventional coronary angiography (CA) for the diagnosis of CAD.
METHODS: Using Medline, we identified 29 studies (2,024 patients) evaluating CAD by means of both MSCT ( 16 slices) and conventional CA before July 2006. After data extraction the analysis was performed according to a random-effects model.
RESULTS: The per-segment analysis pooled the results from 27 studies corresponding to a cumulative number of 22,798 segments. Among unassessable segments, 4.2% were excluded from the analysis and 6.4% were classified at the discretion of the investigators, underscoring the shortcomings of MSCT. With this major limitation, the per-segment sensitivity and specificity were 81% (95% confidence interval [CI] 72% to 89%) and 93% (95% CI 90% to 97%), respectively, with positive and negative likelihood ratios of 21.5 (95% CI 13.1 to 35.5) and 0.11 (95% CI 0.06 to 0.21), respectively, and positive and negative predictive values of 67.8% (95% CI 57.6% to 78.0%) and 96.5% (95% CI 94.7% to 98.3%), respectively. As expected, the per-patient analysis has shown an increased sensitivity of 96% (95% CI 94% to 98%) but a decreased specificity of 74% (95% CI 65% to 84%).
CONCLUSIONS: Multislice spiral computed tomography has shortcomings difficult to overcome in daily practice and, at the more clinically relevant per-patient analysis, continues to have moderate specificity in patients with high prevalence of CAD. Studies evaluating the diagnostic performance of the newest generation of MSCT, including patients with low to moderate CAD prevalence, will be critical in establishing the clinical role of this emerging technology as an alternative to CA.
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Abbreviations and Acronyms
| | AUC = area under the curve | | CA = coronary angiography | | FN = false negative | | FP = false positive | | MSCT = multislice computed tomography | | NPV = negative predictive value | | PPV = positive predictive value | | TN = true negative | | TP = true positive |
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H. J Schunemann, A. D Oxman, J. Brozek, P. Glasziou, R. Jaeschke, G. E Vist, J. W Williams Jr, R. Kunz, J. Craig, V. M Montori, et al.
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I. Gottlieb and J. A. C. Lima
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S. Achenbach, U. Ropers, A. Kuettner, K. Anders, T. Pflederer, S. Komatsu, W. Bautz, W. G. Daniel, and D. Ropers
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J. A.C. Lima
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L. F. Tops, S. C. Krishnan, J. D. Schuijf, M. J. Schalij, and J. J. Bax
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M. Dewey, B. Hamm, H-P. Dubel, and W. Rutsch
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I. Decramer, P. K. Vanhoenacker, G. Sarno, L. Van Hoe, O. Bladt, W. Wijns, and P. M. Parizel
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G Pundziute, J D Schuijf, J W Jukema, J M van Werkhoven, E Boersma, A de Roos, E E van der Wall, and J J Bax
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J. Abdulla, S. Z. Abildstrom, O. Gotzsche, E. Christensen, L. Kober, and C. Torp-Pedersen
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S. K Seneviratne, F. Bamberg, and U. Hoffmann
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J. K. Min, L. J. Shaw, R. B. Devereux, P. M. Okin, J. W. Weinsaft, D. J. Russo, N. J. Lippolis, D. S. Berman, and T. Q. Callister
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R. J. Gibbons, P. A. Araoz, and E. E. Williamson
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A. C. Weustink, W. B. Meijboom, N. R. Mollet, M. Otsuka, F. Pugliese, C. van Mieghem, R. Malago, N. van Pelt, M. L. Dijkshoorn, F. Cademartiri, et al.
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C. A.G. Van Mieghem, A. Thury, W. B. Meijboom, F. Cademartiri, N. R. Mollet, A. C. Weustink, G. Sianos, P. P.T. de Jaegere, P. W. Serruys, and P. de Feyter
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T. C. Gerber, B. Kantor, and P. Chareonthaitawee
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A. J. Einstein, M. J. Henzlova, and S. Rajagopalan
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J. A.C. Lima and J. Hare
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M. Hamon, J. Riddell, and M. Hamon
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J. Sanz, P. R. Moreno, and V. Fuster
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A. N. DeMaria, O. Ben-Yehuda, G. K. Feld, G. S. Ginsburg, B. H. Greenberg, W. Y.W. Lew, J. A.C. Lima, A. S. Maisel, J. Narula, D. J. Sahn, et al.
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