OBJECTIVES: We tested a pre-defined visual interpretation algorithm that combines cardiovascular magnetic resonance (CMR) data from perfusion and infarction imaging for the diagnosis of coronary artery disease (CAD). BACKGROUND: Cardiovascular magnetic resonance can assess both myocardial perfusion and infarction with independent techniques in a single session. METHODS: We prospectively enrolled 100 consecutive patients with suspected CAD scheduled for X-ray coronary angiography. Patients had comprehensive clinical evaluation, including Rose angina questionnaire, 12-lead electrocardiography, C-reactive protein, and calculation of Framingham risk. Cardiovascular magnetic resonance included cine, adenosine-stress and rest perfusion-CMR, and delayed enhancement-CMR (DE-CMR) for infarction imaging. Matched stress-rest perfusion defects in the absence of infarction by DE-CMR were considered artifactual. All patients underwent X-ray angiography within 24 h of CMR. RESULTS: Ninety-two patients had complete CMR examinations. Significant CAD (> or =70% stenosis) was found in 37 patients (40%). The combination of perfusion and DE-CMR had a sensitivity, specificity, and accuracy of 89%, 87%, and 88%, respectively, for CAD diagnosis, compared with 84%, 58%, and 68%, respectively, for perfusion-CMR alone. The combination had higher specificity and accuracy (p < 0.0001), owing to incorporating the exceptionally high specificity (98%) of DE-CMR. Receiver operating characteristic curve analysis demonstrated the combination provided better performance than cine, perfusion, or DE-CMR alone. The accuracy was high in single-vessel and multivessel disease and independent of CAD location. Multivariable analysis including standard clinical parameters demonstrated the combination was the strongest independent CAD predictor. CONCLUSIONS: A combined perfusion and infarction CMR examination with a visual interpretation algorithm can accurately diagnose CAD in the clinical setting. The combination is superior to perfusion-CMR alone.
OBJECTIVES: We tested a pre-defined visual interpretation algorithm that combines cardiovascular magnetic resonance (CMR) data from perfusion and infarction imaging for the diagnosis of coronary artery disease (CAD). BACKGROUND: Cardiovascular magnetic resonance can assess both myocardial perfusion and infarction with independent techniques in a single session. METHODS: We prospectively enrolled 100 consecutive patients with suspected CAD scheduled for X-ray coronary angiography. Patients had comprehensive clinical evaluation, including Rose angina questionnaire, 12-lead electrocardiography, C-reactive protein, and calculation of Framingham risk. Cardiovascular magnetic resonance included cine, adenosine-stress and rest perfusion-CMR, and delayed enhancement-CMR (DE-CMR) for infarction imaging. Matched stress-rest perfusion defects in the absence of infarction by DE-CMR were considered artifactual. All patients underwent X-ray angiography within 24 h of CMR. RESULTS: Ninety-two patients had complete CMR examinations. Significant CAD (> or =70% stenosis) was found in 37 patients (40%). The combination of perfusion and DE-CMR had a sensitivity, specificity, and accuracy of 89%, 87%, and 88%, respectively, for CAD diagnosis, compared with 84%, 58%, and 68%, respectively, for perfusion-CMR alone. The combination had higher specificity and accuracy (p < 0.0001), owing to incorporating the exceptionally high specificity (98%) of DE-CMR. Receiver operating characteristic curve analysis demonstrated the combination provided better performance than cine, perfusion, or DE-CMR alone. The accuracy was high in single-vessel and multivessel disease and independent of CAD location. Multivariable analysis including standard clinical parameters demonstrated the combination was the strongest independent CAD predictor. CONCLUSIONS: A combined perfusion and infarction CMR examination with a visual interpretation algorithm can accurately diagnose CAD in the clinical setting. The combination is superior to perfusion-CMR alone.
Authors: W Gregory Hundley; David A Bluemke; J Paul Finn; Scott D Flamm; Mark A Fogel; Matthias G Friedrich; Vincent B Ho; Michael Jerosch-Herold; Christopher M Kramer; Warren J Manning; Manesh Patel; Gerald M Pohost; Arthur E Stillman; Richard D White; Pamela K Woodard Journal: Circulation Date: 2010-05-17 Impact factor: 29.690
Authors: W Gregory Hundley; David A Bluemke; J Paul Finn; Scott D Flamm; Mark A Fogel; Matthias G Friedrich; Vincent B Ho; Michael Jerosch-Herold; Christopher M Kramer; Warren J Manning; Manesh Patel; Gerald M Pohost; Arthur E Stillman; Richard D White; Pamela K Woodard Journal: J Am Coll Cardiol Date: 2010-06-08 Impact factor: 24.094
Authors: Rolf Gebker; M Frick; C Jahnke; A Berger; C Schneeweis; R Manka; S Kelle; C Klein; B Schnackenburg; E Fleck; I Paetsch Journal: Int J Cardiovasc Imaging Date: 2010-12-14 Impact factor: 2.357
Authors: Federico E Mordini; Tariq Haddad; Li-Yueh Hsu; Peter Kellman; Tracy B Lowrey; Anthony H Aletras; W Patricia Bandettini; Andrew E Arai Journal: JACC Cardiovasc Imaging Date: 2014-01