2026-04-14T04:06:25Zhttps://recercat.cat/oai/requestoai:recercat.cat:11351/111952025-10-24T08:27:56Zcom_2072_451660com_2072_378040col_2072_451662
RECERCAT
author
Ariza, Mar
author
NAUTILUS-Project Collaborative Group
author
Barrué subirana, Cristian
author
Cano Marco, Neus
author
Segura, Barbara
author
Bejar, Javier
author
Garolera, Maite
2025-10-24T08:27:56Z
2025-10-24T08:27:56Z
2024-03-14T13:55:28Z2024-03-14T13:55:28Z2024-01-29
http://hdl.handle.net/11351/11195
Post-COVID-19 condition; Mental speed processing; Logistic regressionCondición post-COVID-19; Procesamiento de velocidad mental; Regresión logísticaEstat post-COVID-19; Processament de velocitat mental; Regressió logísticaThe 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).
Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess
COVID-19 (Malaltia)
DISEASES::Virus Diseases::RNA Virus Infections::Nidovirales Infections::Coronaviridae Infections::Coronavirus Infections::Severe Acute Respiratory Syndrome
DISEASES::Pathological Conditions, Signs and Symptoms::Pathologic Processes::Disease Attributes
PSYCHIATRY AND PSYCHOLOGY::Psychological Phenomena::Mental Processes
ENFERMEDADES::afecciones patológicas, signos y síntomas::procesos patológicos::atributos de la enfermedad
ENFERMEDADES::virosis::infecciones por virus ARN::infecciones por Nidovirales::infecciones por Coronaviridae::infecciones por Coronavirus
PSIQUIATRÍA Y PSICOLOGÍA::fenómenos psicológicos::procesos mentales
Cognitive reserve, depressive symptoms, obesity, and change in employment status predict mental processing speed and executive function after COVID-19
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion