Cognitive reserve, depressive symptoms, obesity, and change in employment status predict mental processing speed and executive function after COVID-19

Other authors

Institut Català de la Salut

[Ariza M] Grup de Recerca en Cervell, Cognició i Conducta, Consorci Sanitari de Terrassa (CST), Terrassa, Spain. Unitat de Psicologia Mèdica, Departament de Medicina, Universitat de Barcelona (UB), Barcelona, Spain. [Béjar J] Departament de Ciències de la Computació, Universitat Politècnica de Catalunya-BarcelonaTech, Barcelona, Spain. [Barrué C] Departament de Ciències de la Computació, Universitat Politècnica de Catalunya-BarcelonaTech, Barcelona, Spain. [Cano N] Grup de Recerca en Cervell, Cognició i Conducta, Consorci Sanitari de Terrassa (CST), Terrassa, Spain. Departament de Ciències Bàsiques, Universitat Internacional de Catalunya (UIC), Sant Cugat del Vallès, Spain. [Segura B] Unitat de Psicologia Mèdica, Departament de Medicina, Universitat de Barcelona (UB), Barcelona, Spain. Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. Institut de Neurociències, Universitat de Barcelona (UB), Barcelona, Spain. [Garolera M] Grup de Recerca en Cervell, Cognició i Conducta, Consorci Sanitari de Terrassa (CST), Terrassa, Spain. Neuropsychology Unit, Consorci Sanitari de Terrassa (CST), Terrassa, Spain.

Consorci Sanitari de Terrassa

Publication date

2024-03-14T13:55:28Z

2024-03-14T13:55:28Z

2024-01-29



Abstract

Post-COVID-19 condition; Mental speed processing; Logistic regression


Condición post-COVID-19; Procesamiento de velocidad mental; Regresión logística


Estat post-COVID-19; Processament de velocitat mental; Regressió logística


The risk factors for post-COVID-19 cognitive impairment have been poorly described. This study aimed to identify the sociodemographic, clinical, and lifestyle characteristics that characterize a group of post-COVID-19 condition (PCC) participants with neuropsychological impairment. The study sample included 426 participants with PCC who underwent a neurobehavioral evaluation. We selected seven mental speed processing and executive function variables to obtain a data-driven partition. Clustering algorithms were applied, including K-means, bisecting K-means, and Gaussian mixture models. Different machine learning algorithms were then used to obtain a classifier able to separate the two clusters according to the demographic, clinical, emotional, and lifestyle variables, including logistic regression with least absolute shrinkage and selection operator (LASSO) (L1) and Ridge (L2) regularization, support vector machines (linear/quadratic/radial basis function kernels), and decision tree ensembles (random forest/gradient boosting trees). All clustering quality measures were in agreement in detecting only two clusters in the data based solely on cognitive performance. A model with four variables (cognitive reserve, depressive symptoms, obesity, and change in work situation) obtained with logistic regression with LASSO regularization was able to classify between good and poor cognitive performers with an accuracy and a weighted averaged precision of 72%, a recall of 73%, and an area under the curve of 0.72. PCC individuals with a lower cognitive reserve, more depressive symptoms, obesity, and a change in employment status were at greater risk for poor performance on tasks requiring mental processing speed and executive function.


Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research was supported by the European Archives of Psychiatry and Clinical Neuroscience Agency for Management of University and Research Grants (AGAUR) from the Generalitat de Catalunya (Pandemies, 2020PANDE00053), the La Marató de TV3 Foundation (202111–30-31–32), the Ministerio de Ciencia e Innovación (TED2021-130409B-C55).

Document Type

Article


Published version

Language

English

Publisher

Springer

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European Archives of Psychiatry and Clinical Neuroscience;

http://doi.org/10.1007/s00406-023-01748-x

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Attribution 4.0 International

http://creativecommons.org/licenses/by/4.0/

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