Detecting neurodevelopmental trajectories in congenital heart diseases with a machine-learning approach

• Main Authors: Elisa Cainelli, Patrizia S. Bisiacchi, Paola Cogo, Massimo Padalino, Manuela Simonato, Michela Vergine, Corrado Lanera, Luca Vedovelli
• Format: Article
• Language: English
• Published: Nature Publishing Group 2021-01-01
• Series: Scientific Reports
• Online Access: https://doi.org/10.1038/s41598-021-82328-8

Abstract We aimed to delineate the neuropsychological and psychopathological profiles of children with congenital heart disease (CHD) and look for associations with clinical parameters. We conducted a prospective observational study in children with CHD who underwent cardiac surgery within five years of age. At least 18 months after cardiac surgery, we performed an extensive neuropsychological (intelligence, language, attention, executive function, memory, social skills) and psychopathological assessment, implementing a machine-learning approach for clustering and influencing variable classification. We examined 74 children (37 with CHD and 37 age-matched controls). Group comparisons have shown differences in many domains: intelligence, language, executive skills, and memory. From CHD questionnaires, we identified two clinical subtypes of psychopathological profiles: a small subgroup with high symptoms of psychopathology and a wider subgroup of patients with ADHD-like profiles. No associations with the considered clinical parameters were found. CHD patients are prone to high interindividual variability in neuropsychological and psychological outcomes, depending on many factors that are difficult to control and study. Unfortunately, these dysfunctions are under-recognized by clinicians. Given that brain maturation continues through childhood, providing a significant window for recovery, there is a need for a lifespan approach to optimize the outcome trajectory for patients with CHD.