Neuropsychological features in children and adults with congenital heart disease: an exploratory data analysis

ABSTRACT

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.

GRAPHICAL ABSTRACT

KEYWORDS:

• Congenital heart disease
• classification
• neurodevelopmental
• neuropsychological
• clustering

Highlights

• Beyond the age of 1 year, two clusters of patients could be identified in patients with CHD.

• The clusters are different in maternal diabetes and neuropsychological features.

• The neuropsychological features included seizure, depression, ADHD, anxiety, developmental delay, learning disability and speech problem.

• Neuropsychological features could be incorporated into the current systems for the classification of CHD.

Acknowledgments

We would like to thank Hoang et al. for sharing their data.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

Both authors carried out the literature review and contributed to the study idea, concept, design, analyses and computer programming, interpretation of the results, drafting, and final approval of the manuscript.

Data availability statement

The data can be downloaded from http://www.plosone.org/article/fetchSingleRepresentation.action?uri=info:doi/10.1371/journal.pone.0191319.s006 (access date: 27 July 2021)

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.