Interobserver variability in the classification of congenital coronary abnormalities: A substudy of the anomalous connections of the coronary arteries registry.

ObjectiveThe diagnosis of anomalous connections of the coronary arteries (ANOCOR) requires an appropriate identification for the management of the patients involved. We studied the observer variability in the description and classification of ANOCOR between a nonexpert group of physicians and a group of expert physicians, using the ANOCOR cohort. Patients and designConsecutive patients identified by 71 referring cardiologists were included in the ANOCOR cohort. Anomalous connection was diagnosed by invasive and/or computed tomography coronary angiography. Angiographic images were reviewed by an angiographic committee with experience in this field. Both investigators and angiographic committee filled out a questionnaire to classify each anomaly with the type of coronary artery involved, the site of anomalous connection, and the initial course. Observer variability between investigators and angiographic committee was assessed by statistics. Anomalous connection with a preaortic course was defined as at-risk. ResultsAmong 472 patients of the ANOCOR cohort, 496 abnormalities were identified with a preaortic course present in 31%. The agreement for the type of artery was excellent (=0.92, 95% CI=0.86-0.98, P<.05), while the agreement for the site of anomalous connection was moderate (=0.50, 95% CI=0.42-0.58, P<.05), and the agreement for the initial course was only fair (=0.32, 95% CI=0.28-0.37, P<.05). Observer agreement for the identification of at-risk forms was moderate (=0.497, 95% CI=0.40-0.59, P<.05). ConclusionsObserver variability in the assessment of anomalous connection of the coronary arteries between nonexperienced and experienced physicians can be significant. We found that expert physicians provide a more robust classification in comparison with nonexpert physicians. Therefore, referral to physicians with a relevant experience should be considered, especially if an anomaly at-risk is suspected.