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Dealing with heterogeneity of cognitive dysfunction in acute depression: a clustering approach

BACKGROUND: Heterogeneity in cognitive functioning among major depressive disorder (MDD) patients could have been the reason for the small-to-moderate differences reported so far when it is compared to other psychiatric conditions or to healthy controls. Additionally, most of these studies did not t...

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Autores principales: Vicent-Gil, Muriel, Portella, Maria J., Serra-Blasco, Maria, Navarra-Ventura, Guillem, Crivillés, Sara, Aguilar, Eva, Palao, Diego, Cardoner, Narcís
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8640365/
https://www.ncbi.nlm.nih.gov/pubmed/32476636
http://dx.doi.org/10.1017/S0033291720001567
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author Vicent-Gil, Muriel
Portella, Maria J.
Serra-Blasco, Maria
Navarra-Ventura, Guillem
Crivillés, Sara
Aguilar, Eva
Palao, Diego
Cardoner, Narcís
author_facet Vicent-Gil, Muriel
Portella, Maria J.
Serra-Blasco, Maria
Navarra-Ventura, Guillem
Crivillés, Sara
Aguilar, Eva
Palao, Diego
Cardoner, Narcís
author_sort Vicent-Gil, Muriel
collection PubMed
description BACKGROUND: Heterogeneity in cognitive functioning among major depressive disorder (MDD) patients could have been the reason for the small-to-moderate differences reported so far when it is compared to other psychiatric conditions or to healthy controls. Additionally, most of these studies did not take into account clinical and sociodemographic characteristics that could have played a relevant role in cognitive variability. This study aims to identify empirical clusters based on cognitive, clinical and sociodemographic variables in a sample of acute MDD patients. METHODS: In a sample of 174 patients with an acute depressive episode, a two-step clustering analysis was applied considering potentially relevant cognitive, clinical and sociodemographic variables as indicators for grouping. RESULTS: Treatment resistance was the most important factor for clustering, closely followed by cognitive performance. Three empirical subgroups were obtained: cluster 1 was characterized by a sample of non-resistant patients with preserved cognitive functioning (n = 68, 39%); cluster 2 was formed by treatment-resistant patients with selective cognitive deficits (n = 66, 38%) and cluster 3 consisted of resistant (n = 23, 58%) and non-resistant (n = 17, 42%) acute patients with significant deficits in all neurocognitive domains (n = 40, 23%). CONCLUSIONS: The findings provide evidence upon the existence of cognitive heterogeneity across patients in an acute depressive episode. Therefore, assessing cognition becomes an evident necessity for all patients diagnosed with MDD, and although treatment resistant is associated with greater cognitive dysfunction, non-resistant patients can also show significant cognitive deficits. By targeting not only mood but also cognition, patients are more likely to achieve full recovery and prevent new relapses.
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spelling pubmed-86403652021-12-13 Dealing with heterogeneity of cognitive dysfunction in acute depression: a clustering approach Vicent-Gil, Muriel Portella, Maria J. Serra-Blasco, Maria Navarra-Ventura, Guillem Crivillés, Sara Aguilar, Eva Palao, Diego Cardoner, Narcís Psychol Med Original Article BACKGROUND: Heterogeneity in cognitive functioning among major depressive disorder (MDD) patients could have been the reason for the small-to-moderate differences reported so far when it is compared to other psychiatric conditions or to healthy controls. Additionally, most of these studies did not take into account clinical and sociodemographic characteristics that could have played a relevant role in cognitive variability. This study aims to identify empirical clusters based on cognitive, clinical and sociodemographic variables in a sample of acute MDD patients. METHODS: In a sample of 174 patients with an acute depressive episode, a two-step clustering analysis was applied considering potentially relevant cognitive, clinical and sociodemographic variables as indicators for grouping. RESULTS: Treatment resistance was the most important factor for clustering, closely followed by cognitive performance. Three empirical subgroups were obtained: cluster 1 was characterized by a sample of non-resistant patients with preserved cognitive functioning (n = 68, 39%); cluster 2 was formed by treatment-resistant patients with selective cognitive deficits (n = 66, 38%) and cluster 3 consisted of resistant (n = 23, 58%) and non-resistant (n = 17, 42%) acute patients with significant deficits in all neurocognitive domains (n = 40, 23%). CONCLUSIONS: The findings provide evidence upon the existence of cognitive heterogeneity across patients in an acute depressive episode. Therefore, assessing cognition becomes an evident necessity for all patients diagnosed with MDD, and although treatment resistant is associated with greater cognitive dysfunction, non-resistant patients can also show significant cognitive deficits. By targeting not only mood but also cognition, patients are more likely to achieve full recovery and prevent new relapses. Cambridge University Press 2021-12 2020-06-01 /pmc/articles/PMC8640365/ /pubmed/32476636 http://dx.doi.org/10.1017/S0033291720001567 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Vicent-Gil, Muriel
Portella, Maria J.
Serra-Blasco, Maria
Navarra-Ventura, Guillem
Crivillés, Sara
Aguilar, Eva
Palao, Diego
Cardoner, Narcís
Dealing with heterogeneity of cognitive dysfunction in acute depression: a clustering approach
title Dealing with heterogeneity of cognitive dysfunction in acute depression: a clustering approach
title_full Dealing with heterogeneity of cognitive dysfunction in acute depression: a clustering approach
title_fullStr Dealing with heterogeneity of cognitive dysfunction in acute depression: a clustering approach
title_full_unstemmed Dealing with heterogeneity of cognitive dysfunction in acute depression: a clustering approach
title_short Dealing with heterogeneity of cognitive dysfunction in acute depression: a clustering approach
title_sort dealing with heterogeneity of cognitive dysfunction in acute depression: a clustering approach
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8640365/
https://www.ncbi.nlm.nih.gov/pubmed/32476636
http://dx.doi.org/10.1017/S0033291720001567
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