We aimed to recognize clinically meaningful patterns among patients with congenital heart disease to support clinical decision-making and better classification in practice. This research was a secondary analysis of data from the Congenital Heart Disease Genetic Network Study conducted from December 2010 to November 2014 in the United States. The analytic dataset included 6002 patients ≥1 year of age with non-syndromic congenital heart disease. For each patient, features included demographic, clinical, maternal and paternal characteristics. We clustered patients to identify subgroups that shared similarities in their clinical features. The performance of the clustering algorithm was evaluated with a random forest. Next, we used the apriori algorithm to generate clinical rules from patients' characteristics. The clustering algorithm identified two discernible groups of patients. The two classes of patients were different in maternal diabetes and in neuropsychological indicators [Accuracy (95% CI) = 97.1% (96.2, 97.8), area under the ROC curve = 96.8%]. Our rule extraction suggested the presence of clinical pictures with high lift values among patients with maternal diabetes or with seizure, depression, attention-deficit hyperactivity disorder, anxiety, developmental delay, learning disability and speech problem. Beyond the age of 1 year, maternal diabetes and neuropsychological characteristics identify two clusters of patients with congenital heart disease. These characteristics have the potential of being incorporated into the current systems for the classification of congenital heart disease.
Keywords: Congenital heart disease; classification; clustering; neurodevelopmental; neuropsychological.