Value of early cardiovascular magnetic resonance for the prediction of adverse arrhythmic cardiac events after a first noncomplicated ST-segment-elevation myocardial infarction.

Background: Infarct size (IS) determined by cardiac magnetic resonance (CMR) has proven an additional value, on top of left ventricular ejection fraction (LVEF), in prediction of adverse arrhythmic cardiac events (AACEs) in chronic ischemic heart disease. Its value soon after an acute ST-segment-elevation myocardial infarction remains unknown. Our aim was to determine whether early CMR can improve AACE risk prediction after acute ST-segment-elevation myocardial infarction.

Methods And Results: Patients admitted for a first noncomplicated ST-segment-elevation myocardial infarction were prospectively followed up. A total of 440 patients were included. All of them underwent CMR 1 week after admission. CMR-derived LVEF and IS (grams per meter squared) were quantified. AACEs included postdischarge sudden death, sustained ventricular tachycardia, and ventricular fibrillation either documented on ECG or recorded via an implantable cardioverter-defibrillator. Within a median follow-up of 2 years, 11 AACEs (2.5%) were detected: 5 sudden deaths (1.1%) and 6 spontaneous ventricular tachycardia/ventricular fibrillation. In the whole group, AACEs associated with more depressed LVEF (adjusted hazard ratio [95% confidence interval], 0.90 [0.83-0.97]; P<0.01) and larger IS (adjusted hazard ratio [95% confidence interval], 1.06 [1.01-1.12]; P=0.01). According to the corresponding area under the receiver operating characteristic curve, LVEF ≤36% and IS ≥23.5 g/m(2) best predicted AACEs. The vast majority of AACEs (10/11) occurred in patients with simultaneous depressed LVEF ≤36% and IS ≥23.5 g/m(2) (n=39).

Conclusions: In the era of reperfusion therapies, occurrence of AACEs in patients with an in-hospital noncomplicated first ST-segment-elevation myocardial infarction is low. In this setting, assessment of an early CMR-derived IS could be useful for further optimization of AACE risk prediction.