Neuropsychological features in children and adults with congenital heart disease: an exploratory data analysis.
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...
| Publicado en: | Psychology, Health & Medicine Vol. 28; no. 3; pp. 693 - 707 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Mar2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=162144286&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162144286 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13548506 0UX jtl: Psychology, Health & Medicine issn: 13548506 maglogo: N pubinfo: dt: Mar2023 vid: 28 iid: 3 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 162144286 160203904 162144286 162144286 10.1080/13548506.2022.2147558 162144286 ppf: 693 ppct: 14 formats: tig: atl: Neuropsychological features in children and adults with congenital heart disease: an exploratory data analysis. aug: au: Mohammadi, Tanya Mohammadi, Babak affil: College of Science, School of Mathematics, Statistics, and Computer Science, The University of Tehran, Tehran, Iran sug: subj: Heart Defects, Congenital Complications Pregnancy Complications Risk Factors Heart Defects, Congenital Classification Mental Disorders Diagnosed in Childhood Human Secondary Analysis Cluster Analysis Algorithms ROC Curve Decision Making, Clinical Chi Square Test Fisher's Exact Test Pregnancy in Diabetes Pregnancy Female Child Adult Confidence Intervals Developmental Disabilities Learning Disorders Child: 6-12 years Adult: 19-44 years Female ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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