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

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 year...

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Autores principales:	Elisa Cainelli, Patrizia S. Bisiacchi, Paola Cogo, Massimo Padalino, Manuela Simonato, Michela Vergine, Corrado Lanera, Luca Vedovelli
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ea23eb02811e472ea079296f112a9258
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spelling	oai:doaj.org-article:ea23eb02811e472ea079296f112a92582021-12-02T13:57:59ZDetecting neurodevelopmental trajectories in congenital heart diseases with a machine-learning approach10.1038/s41598-021-82328-82045-2322https://doaj.org/article/ea23eb02811e472ea079296f112a92582021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82328-8https://doaj.org/toc/2045-2322Abstract 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.Elisa CainelliPatrizia S. BisiacchiPaola CogoMassimo PadalinoManuela SimonatoMichela VergineCorrado LaneraLuca VedovelliNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Elisa Cainelli Patrizia S. Bisiacchi Paola Cogo Massimo Padalino Manuela Simonato Michela Vergine Corrado Lanera Luca Vedovelli Detecting neurodevelopmental trajectories in congenital heart diseases with a machine-learning approach
description	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.
format	article
author	Elisa Cainelli Patrizia S. Bisiacchi Paola Cogo Massimo Padalino Manuela Simonato Michela Vergine Corrado Lanera Luca Vedovelli
author_facet	Elisa Cainelli Patrizia S. Bisiacchi Paola Cogo Massimo Padalino Manuela Simonato Michela Vergine Corrado Lanera Luca Vedovelli
author_sort	Elisa Cainelli
title	Detecting neurodevelopmental trajectories in congenital heart diseases with a machine-learning approach
title_short	Detecting neurodevelopmental trajectories in congenital heart diseases with a machine-learning approach
title_full	Detecting neurodevelopmental trajectories in congenital heart diseases with a machine-learning approach
title_fullStr	Detecting neurodevelopmental trajectories in congenital heart diseases with a machine-learning approach
title_full_unstemmed	Detecting neurodevelopmental trajectories in congenital heart diseases with a machine-learning approach
title_sort	detecting neurodevelopmental trajectories in congenital heart diseases with a machine-learning approach
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ea23eb02811e472ea079296f112a9258
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Detecting neurodevelopmental trajectories in congenital heart diseases with a machine-learning approach

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