ABSTRACT
BACKGROUND: The identification and assessment of myocardial infarction (MI) are important for therapeutic and prognostic purposes, yet current recommended diagnostic strategies have significant limitations. We prospectively tested the performance of delayed-enhancement magnetic resonance imaging (MRI) with gadolinium-based contrast for the detection of MI in an international, multicenter trial. METHODS AND RESULTS: Patients with their first MI were enrolled in an acute (< or = 16 days after MI; n=282) or chronic (17 days to 6 months; n=284) arm and then randomized to 1 of 4 doses of gadoversetamide: 0.05, 0.1, 0.2, or 0.3 mmol/kg. Standard delayed-enhancement MRI was performed before contrast (control) and 10 and 30 minutes after gadoversetamide. For blinded analysis, precontrast and postcontrast MRIs were randomized and then scored for enhanced regions by 3 independent readers not associated with the study. The infarct-related artery perfusion territory was scored from x-ray angiograms separately. In total, 566 scans were performed in 26 centers using commercially available scanners from all major US/European vendors. All scans were included in the analysis. The sensitivity of MRI for detecting MI increased with rising dose of gadoversetamide (P<0.0001), reaching 99% (acute) and 94% (chronic) after contrast compared with 11% before contrast. Likewise, the accuracy of MRI for identifying MI location (compared with infarct-related artery perfusion territory) increased with rising dose of gadoversetamide (P<0.0001), reaching 99% (acute) and 91% (chronic) after contrast compared with 9% before contrast. For gadoversetamide doses > or = 0.2 mmol/kg, 10- and 30-minute images provided equal performance, and peak creatine kinase-MB levels correlated with MRI infarct size (P<0.0001). CONCLUSIONS: Gadoversetamide-enhanced MRI using doses of > or = 0.2 mmol/kg is effective in the detection and assessment of both acute and chronic MI. This study represents the first multicenter trial designed to evaluate an imaging approach for detecting MI.