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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2021
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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. |
format | Online Article Text |
id | pubmed-8640365 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
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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