Assessment of coronary artery abnormalities in children with congenital heart disease by using multi-slice CT: a single-center experience
摘要
Congenital coronary artery anomalies (CAA) represent clinically important abnormalities due to their association with myocardial ischemia, arrhythmias, heart failure, and sudden cardiac death in children. They may be isolated findings or associated with congenital heart disease (CHD). The prevalence of CAAs is known to be higher in patients with CHD compared with the normal pediatric population. This study aims to determine the prevalence, types, and clinical implications of coronary artery anomalies in children with CHD by using multislice computed tomography (MSCT). It is a retrospective single-center study that included 102 pediatric patients (62 males and 40 females; mean age 60.3 ± 72.3 months) with echocardiographically confirmed congenital heart disease who were referred for cardiac MSCT. Coronary anatomy was assessed using ECG-gated MSCT with advanced image reconstruction techniques.
ResultsCoronary artery anomalies were identified in 10 patients (9.8%). The majority of anomalies involved the right coronary artery (80%). The most frequent anomaly was the anomalous origin of the right coronary artery. Other anomalies included single coronary artery variants, a high-takeoff right coronary artery, an origin of the right coronary artery from the non-coronary sinus, a dual left anterior descending artery (type IV), and an anomalous origin of the left coronary artery from the pulmonary artery (ALCAPA). Two patients required coronary-related intervention. Electrocardiographic evidence of ischemia was documented in one patient with ALCAPA.
ConclusionCoronary artery anomalies were relatively common among children with congenital heart disease in this cohort, with right coronary artery abnormalities representing the most frequent type. Multislice computed tomography enabled accurate assessment of coronary artery anatomy and provided valuable information for detecting and characterizing anomalies that may influence clinical management.