Cognitive reserve, depressive symptoms, obesity, and change in employment status predict mental processing speed and executive function after COVID-19
© 2024. The Author(s)..
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. Study registration: www.ClinicalTrials.gov , identifier NCT05307575.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
European archives of psychiatry and clinical neuroscience - (2024) vom: 29. Jan. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ariza, Mar [VerfasserIn] |
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| Links: |
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| Themen: |
Clustering |
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| Anmerkungen: |
Date Revised 29.01.2024 published: Print-Electronic ClinicalTrials.gov: NCT05307575 Citation Status Publisher |
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| doi: |
10.1007/s00406-023-01748-x |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM367756544 |
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| 245 | 1 | 0 | |a Cognitive reserve, depressive symptoms, obesity, and change in employment status predict mental processing speed and executive function after COVID-19 |
| 264 | 1 | |c 2024 | |
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| 500 | |a published: Print-Electronic | ||
| 500 | |a ClinicalTrials.gov: NCT05307575 | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a © 2024. The Author(s). | ||
| 520 | |a 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. Study registration: www.ClinicalTrials.gov , identifier NCT05307575 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Clustering | |
| 650 | 4 | |a Executive function | |
| 650 | 4 | |a Logistic regression | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Mental speed processing | |
| 650 | 4 | |a Post-COVID-19 condition | |
| 700 | 1 | |a Béjar, Javier |e verfasserin |4 aut | |
| 700 | 1 | |a Barrué, Cristian |e verfasserin |4 aut | |
| 700 | 1 | |a Cano, Neus |e verfasserin |4 aut | |
| 700 | 1 | |a Segura, Bàrbara |e verfasserin |4 aut | |
| 700 | 0 | |a NAUTILUS Project Collaborative Group |e verfasserin |4 aut | |
| 700 | 1 | |a Cortés, Claudio Ulises |e verfasserin |4 aut | |
| 700 | 1 | |a Junqué, Carme |e verfasserin |4 aut | |
| 700 | 1 | |a Garolera, Maite |e verfasserin |4 aut | |
| 700 | 1 | |a Bernia, Jose A |e investigator |4 oth | |
| 700 | 1 | |a Arauzo, Vanesa |e investigator |4 oth | |
| 700 | 1 | |a Balague-Marmaña, Marta |e investigator |4 oth | |
| 700 | 1 | |a Pérez-Pellejero, Cristian |e investigator |4 oth | |
| 700 | 1 | |a Cañizares, Silvia |e investigator |4 oth | |
| 700 | 1 | |a Muñoz, Jose Antonio Lopez |e investigator |4 oth | |
| 700 | 1 | |a Caballero, Jesús |e investigator |4 oth | |
| 700 | 1 | |a Carnes-Vendrell, Anna |e investigator |4 oth | |
| 700 | 1 | |a Piñol-Ripoll, Gerard |e investigator |4 oth | |
| 700 | 1 | |a Gonzalez-Aguado, Ester |e investigator |4 oth | |
| 700 | 1 | |a Riera-Pagespetit, Mar |e investigator |4 oth | |
| 700 | 1 | |a Forcadell-Ferreres, Eva |e investigator |4 oth | |
| 700 | 1 | |a Reverte-Vilarroya, Silvia |e investigator |4 oth | |
| 700 | 1 | |a Forné, Susanna |e investigator |4 oth | |
| 700 | 1 | |a Muñoz-Padros, Jordina |e investigator |4 oth | |
| 700 | 1 | |a Bartes-Plan, Anna |e investigator |4 oth | |
| 700 | 1 | |a Muñoz-Moreno, Jose A |e investigator |4 oth | |
| 700 | 1 | |a Prats-Paris, Anna |e investigator |4 oth | |
| 700 | 1 | |a Pons, Inmaculada Rico |e investigator |4 oth | |
| 700 | 1 | |a Molina, Judit Martínez |e investigator |4 oth | |
| 700 | 1 | |a Casas-Henanz, Laura |e investigator |4 oth | |
| 700 | 1 | |a Castejon, Judith |e investigator |4 oth | |
| 700 | 1 | |a Mas, Maria José Ciudad |e investigator |4 oth | |
| 700 | 1 | |a Jodrà, Anna Ferré |e investigator |4 oth | |
| 700 | 1 | |a Lozano, Manuela |e investigator |4 oth | |
| 700 | 1 | |a Garzon, Tamar |e investigator |4 oth | |
| 700 | 1 | |a Cullell, Marta |e investigator |4 oth | |
| 700 | 1 | |a Vega, Sonia |e investigator |4 oth | |
| 700 | 1 | |a Alsina, Sílvia |e investigator |4 oth | |
| 700 | 1 | |a Maldonado-Belmonte, Maria J |e investigator |4 oth | |
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| 700 | 1 | |a García-Cabello, Eloy |e investigator |4 oth | |
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| 700 | 1 | |a Navarro, Sandra |e investigator |4 oth | |
| 700 | 1 | |a Baillès, Eva |e investigator |4 oth | |
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