Improved detection of coronary artery disease by stress perfusion cardiovascular magnetic resonance with the use of delayed enhancement infarction imaging.

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.