Direct Comparison of Cardiovascular Magnetic Resonance and Single-Photon Emission Computed Tomography for Detection of Coronary Artery Disease: A Meta-Analysis
Objective To use direct comparative studies or randomised controlled trials to compare the accuracy of cardiac magnetic resonance (CMR) and single-photon emission computed tomography (SPECT) for the detection of obstructive coronary artery disease (CAD). Materials and Methods Various databases were searched for original articles published prior to June 2013. Studies were selected that performed both CMR and SPECT in the same or randomised patients to detect CAD and that presented sufficient data to allow construction of contingency tables. For each study, the true-positive, false-positive, true-negative, and false-negative values were extracted or derived, and 2×2 contingency tables were constructed. To reduce heterogeneity, the meta-analysis was carried out in two parts: (1) coronary territory-based analysis and (2) patient-based analysis. Results 10 studies (5 studies based on patient, 4 studies based on coronary territory, and 1 study based on both) were included in the meta-analysis with a total of 1727 patients. The methodological quality was moderate. For part (1), the summary estimates were as follows: for CMR based on patient–a sensitivity of 0.79 (95% confidence interval: 0.72–0.84) and a specificity of 0.75 (0.65–0.83); for SPECT based on patient–a sensitivity of 0.70 (0.59–0.79) and a specificity of 0.76 (0.66–0.83). For part (2), the summary estimates for CMR based on coronary territory were a sensitivity of 0.80 (0.73–0.85) and a specificity of 0.87 (0.81–0.91), and the summary estimates for SPECT based on coronary territory were a sensitivity of 0.67 (0.60–0.72) and a specificity of 0.80 (0.75–0.84). Conclusions Compared with SPECT, CMR is more sensitive to detect CAD on a per-patient basis. Nonetheless, large scale, well-designed trials are necessary to assess its clinical value on a per-coronary territory basis.
References
[1]
Lloyd-Jones D, Adams RJ, Brown TM, Carnethon M, Dai S, et al. (2010) Heart disease and stroke statistics–2010 update: a report from the American Heart Association. Circulation 121: e46–e215. doi: 10.1161/circulationaha.109.192667
[2]
Smith SC Jr, Feldman TE, Hirshfeld JW Jr, Jacobs AK, Kern MJ, et al. (2006) ACC/AHA/SCAI 2005 Guideline Update for Percutaneous Coronary Intervention-Summary Article: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (ACC/AHA/SCAI Writing Committee to Update the 2001 Guidelines for Percutaneous Coronary Intervention). J Am Coll Cardiol 47: 216–235. doi: 10.1161/circulationaha.105.170815
[3]
Wijns W, Kolh P, Danchin N, Di Mario C, Falk V, et al. (2010) Guidelines on myocardial revascularization. Eur Heart J 31: 2501–2555. doi: 10.1093/eurheartj/ehq277
[4]
Lerakis S, McLean DS, Anadiotis AV, Janik M, Oshinski JN, et al. (2009) Prognostic value of adenosine stress cardiovascular magnetic resonance in patients with low-risk chest pain. J Cardiovasc Magn Reson 11: 37. doi: 10.1186/1532-429x-11-37
[5]
Jaarsma C, Leiner T, Bekkers SC, Crijns HJ, Wildberger JE, et al. (2012) Diagnostic performance of noninvasive myocardial perfusion imaging using single-photon emission computed tomography, cardiac magnetic resonance, and positron emission tomography imaging for the detection of obstructive coronary artery disease: A meta-analysis. J Am Coll Cardiol 59: 1719–1728. doi: 10.1016/j.jacc.2011.12.040
[6]
Mowatt G, Vale L, Brazzelli M, Hernandez R, Murray A, et al.. (2004) Systematic review of the effectiveness and cost-effectiveness, and economic evaluation, of myocardial perfusion scintigraphy for the diagnosis and management of angina and myocardial infarction. Health Technol Assess 8: iii–iv, 1–207.
[7]
Hamon M, Fau G, Nee G, Ehtisham J, Morello R (2010) Meta-analysis of the diagnostic performance of stress perfusion cardiovascular magnetic resonance for detection of coronary artery disease. J Cardiovasc Magn Reson 12: 29. doi: 10.1186/1532-429x-12-29
[8]
Schwitter J, Wacker CM, Wilke N, Al-Saadi N, Sauer E, et al. (2013) MR-IMPACT II: Magnetic resonance imaging for myocardial perfusion assessment in coronary artery disease trial: Perfusion-cardiac magnetic resonance vs. single-photon emission computed tomography for the detection of coronary artery disease: A comparative multicentre, multivendor trial. Eur Heart J 34: 775–781. doi: 10.1093/eurheartj/ehs022
[9]
Greenwood JP, Maredia N, Younger JF, Brown JM, Nixon J, et al. (2012) Cardiovascular magnetic resonance and single-photon emission computed tomography for diagnosis of coronary heart disease (CE-MARC): a prospective trial. Lancet 379: 453–460. doi: 10.1016/s0140-6736(11)61335-4
[10]
Schwitter J, Wacker CM, van Rossum AC, Lombardi M, Al-Saadi N, et al. (2008) MR-IMPACT: comparison of perfusion-cardiac magnetic resonance with single-photon emission computed tomography for the detection of coronary artery disease in a multicentre, multivendor, randomized trial. Eur Heart J 29: 480–489. doi: 10.1093/eurheartj/ehm617
[11]
Sakuma H, Suzawa N, Ichikawa Y, Makino K, Hirano T, et al. (2005) Diagnostic accuracy of stress first-pass contrast-enhanced myocardial perfusion MRI compared with stress myocardial perfusion scintigraphy. American Journal of Roentgenology 185: 95–102. doi: 10.2214/ajr.185.1.01850095
[12]
Okuda S, Tanimoto A, Satoh T, Hashimoto J, Shinmoto H, et al. (2005) Evaluation of ischemic heart disease on a 1.5 tesla scanner: Combined first-pass perfusion and viability study. Radiation Medicine - Medical Imaging and Radiation Oncology 23: 230–235.
[13]
Thiele H, Plein S, Breeuwer M, Ridgway JP, Higgins D, et al. (2004) Color-encoded semiautomatic analysis of multi-slice first-pass magnetic resonance perfusion: Comparison to tetrofosmin single photon emission computed tomography perfusion and X-ray angiography. International Journal of Cardiovascular Imaging 20: 371–384. doi: 10.1023/b:caim.0000041938.45383.a4
[14]
Ishida N, Sakuma H, Motoyasu M, Okinaka T, Isaka N, et al. (2003) Noninfarcted myocardium: Correlation between dynamic first-pass contrast-enhanced myocardial MR imaging and quantitative coronary angiography. Radiology 229: 209–216. doi: 10.1148/radiol.2291021118
[15]
Doyle M, Fuisz A, Kortright E, Biederman RWW, Walsh EG, et al. (2003) The impact of myocardial flow reserve on the detection of coronary artery disease by perfusion imaging methods: An NHLBI WISE study. Journal of Cardiovascular Magnetic Resonance 5: 475–485. doi: 10.1081/jcmr-120022263
[16]
Panting JR, Gatehouse PD, Yang GZ, Jerosch-Herold M, Wilke N, et al. (2001) Echo-planar magnetic resonance myocardial perfusion imaging: Parametric map analysis and comparison with thallium SPECT. Journal of Magnetic Resonance Imaging 13: 192–200. doi: 10.1002/1522-2586(200102)13:2<192::aid-jmri1029>3.0.co;2-n
[17]
Sharples L, Hughes V, Crean A, Dyer M, Buxton M, et al.. (2007) Cost-effectiveness of functional cardiac testing in the diagnosis and management of coronary artery disease: a randomised controlled trial. The CECaT trial. Health Technol Assess 11: iii–iv, ix-115.
[18]
Whiting P, Rutjes AW, Reitsma JB, Bossuyt PM, Kleijnen J (2003) The development of QUADAS: a tool for the quality assessment of studies of diagnostic accuracy included in systematic reviews. BMC Med Res Methodol 3: 25.
[19]
Whiting PF, Weswood ME, Rutjes AW, Reitsma JB, Bossuyt PN, et al. (2006) Evaluation of QUADAS, a tool for the quality assessment of diagnostic accuracy studies. BMC Med Res Methodol 6: 9. doi: 10.1186/1471-2288-6-9
[20]
Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, et al. (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155: 529–536. doi: 10.7326/0003-4819-155-8-201110180-00009
[21]
Menke J (2010) Bivariate random-effects meta-analysis of sensitivity and specificity with SAS PROC GLIMMIX. Methods Inf Med 49: 54–62, 62–54.
Leeflang MM, Deeks JJ, Gatsonis C, Bossuyt PM (2008) Systematic reviews of diagnostic test accuracy. Ann Intern Med 149: 889–897. doi: 10.7326/0003-4819-149-12-200812160-00008
[24]
StataCorp (2009) Stata Statistical Software: Release 11. College Station, TX: StataCorp LP.
[25]
Deeks JJ, Macaskill P, Irwig L (2005) The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. Journal of Clinical Epidemiology 58: 882–893. doi: 10.1016/j.jclinepi.2005.01.016
[26]
Moher D, Liberati A, Tetzlaff J, Altman DG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6: e1000097. doi: 10.1371/journal.pmed.1000097
[27]
Iwata K, Kubota M, Ogasawara K (2008) [Comparsion with myocardial perfusion MRI and myocardial perfusion SPECT in the diagnostic performance of coronary artery disease: a meta-analysis]. Nihon Hoshasen Gijutsu Gakkai Zasshi 64: 251–258. doi: 10.6009/jjrt.64.251
[28]
de Jong MC, Genders TS, van Geuns RJ, Moelker A, Hunink MG (2012) Diagnostic performance of stress myocardial perfusion imaging for coronary artery disease: a systematic review and meta-analysis. Eur Radiol 22: 1881–1895. doi: 10.1007/s00330-012-2434-1
[29]
Brazzelli M, Sandercock PA, Chappell FM, Celani MG, Righetti E, et al.. (2009) Magnetic resonance imaging versus computed tomography for detection of acute vascular lesions in patients presenting with stroke symptoms. Cochrane Database Syst Rev: CD007424.
[30]
Jogiya R, Kozerke S, Morton G, De Silva K, Redwood S, et al. (2012) Validation of dynamic 3-dimensional whole heart magnetic resonance myocardial perfusion imaging against fractional flow reserve for the detection of significant coronary artery disease. J Am Coll Cardiol 60: 756–765. doi: 10.1016/j.jacc.2012.02.075
[31]
Rutjes AW, Reitsma JB, Di Nisio M, Smidt N, van Rijn JC, et al. (2006) Evidence of bias and variation in diagnostic accuracy studies. CMAJ 174: 469–476.
[32]
Davey J, Turner RM, Clarke MJ, Higgins JP (2011) Characteristics of meta-analyses and their component studies in the Cochrane Database of Systematic Reviews: a cross-sectional, descriptive analysis. BMC Med Res Methodol 11: 160. doi: 10.1186/1471-2288-11-160
[33]
McAuley L, Ba'Pham, Tugwell P, Moher D (2000) Does the inclusion of grey literature influence estimates of intervention effectiveness reported in meta-analyses? Lancet 356: 1228–1231. doi: 10.1016/s0140-6736(00)02786-0
