Using Two-Step Cluster Analysis and Latent Class Cluster Analysis to Classify the Cognitive Heterogeneity of Cross-Diagnostic Psychiatric Inpatients

The heterogeneity of cognitive profiles among psychiatric patients has been reported to carry significant clinical information. However, how to best characterize such cognitive heterogeneity is still a matter of debate. Despite being well suited for clinical data, cluster analysis techniques, like t...

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Detalles Bibliográficos
Autores principales: Benassi, Mariagrazia, Garofalo, Sara, Ambrosini, Federica, Sant’Angelo, Rosa Patrizia, Raggini, Roberta, De Paoli, Giovanni, Ravani, Claudio, Giovagnoli, Sara, Orsoni, Matteo, Piraccini, Giovanni
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Psychology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7299079/
https://www.ncbi.nlm.nih.gov/pubmed/32587546
http://dx.doi.org/10.3389/fpsyg.2020.01085
Descripción
Sumario:The heterogeneity of cognitive profiles among psychiatric patients has been reported to carry significant clinical information. However, how to best characterize such cognitive heterogeneity is still a matter of debate. Despite being well suited for clinical data, cluster analysis techniques, like the Two-Step and the Latent Class, received little to no attention in the literature. The present study aimed to test the validity of the cluster solutions obtained with Two-Step and Latent Class cluster analysis on the cognitive profile of a cross-diagnostic sample of 387 psychiatric inpatients. Two-Step and Latent Class cluster analysis produced similar and reliable solutions. The overall results reported that it is possible to group all psychiatric inpatients into Low and High Cognitive Profiles, with a higher degree of cognitive heterogeneity in schizophrenia and bipolar disorder patients than in depressive disorders and personality disorder patients.

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