Data-driven cognitive phenotypes in subjects with bipolar disorder and their clinical markers of severity.
Background: Subjects with bipolar disorder (BD) show heterogeneous cognitive profile and that not necessarily the disease will lead to unfavorable clinical outcomes. We aimed to identify clinical markers of severity among cognitive clusters in individuals with BD through data-driven methods. Methods...
| Published in: | Psychological Medicine Vol. 52; no. 9; pp. 1728 - 1736 |
|---|---|
| Main Authors: | , , , , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Cambridge University Press
Jul2022
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157954172&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157954172 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00332917 6Q3 jtl: Psychological Medicine issn: 00332917 maglogo: N pubinfo: dt: Jul2022 vid: 52 iid: 9 pid: 15979 pub: Cambridge University Press artinfo: ui: 157954172 157954172 157954172 10.1017/S0033291720003499 157954172 ppf: 1728 ppct: 8 formats: tig: atl: Data-driven cognitive phenotypes in subjects with bipolar disorder and their clinical markers of severity. aug: au: Rabelo-da-Ponte, Francisco Diego Lima, Flavia Moreira Martinez-Aran, Anabel Kapczinski, Flávio Vieta, Eduard Rosa, Adriane Ribeiro Kunz, Maurício Czepielewski, Leticia Sanguinetti affil: Molecular Psychiatry Laboratory, Hospital de Clinicas de Porto Alegre, Programa de Pós-Graduação em Psiquiatria e Ciências do Comportamento, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil sug: subj: Bipolar Disorder Severity of Illness Biological Markers Phenotype Cognition Human Descriptive Statistics Data Analysis Software Algorithms Scales Sociodemographic Factors Male Female Adult Middle Age Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Background: Subjects with bipolar disorder (BD) show heterogeneous cognitive profile and that not necessarily the disease will lead to unfavorable clinical outcomes. We aimed to identify clinical markers of severity among cognitive clusters in individuals with BD through data-driven methods. Methods: We recruited 167 outpatients with BD and 100 unaffected volunteers from Brazil and Spain that underwent a neuropsychological assessment. Cognitive functions assessed were inhibitory control, processing speed, cognitive flexibility, verbal fluency, working memory, short- and long-term verbal memory. We performed hierarchical cluster analysis and discriminant function analysis to determine and confirm cognitive clusters, respectively. Then, we used classification and regression tree (CART) algorithm to determine clinical and sociodemographic variables of the previously defined cognitive clusters. Results: We identified three neuropsychological subgroups in individuals with BD: intact (35.3%), selectively impaired (34.7%), and severely impaired individuals (29.9%). The most important predictors of cognitive subgroups were years of education, the number of hospitalizations, and age, respectively. The model with CART algorithm showed sensitivity 45.8%, specificity 78.4%, balanced accuracy 62.1%, and the area under the ROC curve was 0.61. Of 10 attributes included in the model, only three variables were able to separate cognitive clusters in BD individuals: years of education, number of hospitalizations, and age. Conclusion: These results corroborate with recent findings of neuropsychological heterogeneity in BD, and suggest an overlapping between premorbid and morbid aspects that influence distinct cognitive courses of the disease. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|