Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/221637
Title: Prediction of pharmacological response in OCD using machine learning techniques and clinical and neuropsychological variables
Author: Tubío Fungueiriño, María
Cernadas, Eva
Fernández Delgado, Manuel
Arrojo, Manuel
Bertolín Triquell, Sara
Real, Eva
Menchón Magriñá, José Manuel
Carracedo, Angel
Alonso Ortega, María del Pino
Fernández Prieto, Montse
Segalàs Cosi, Cinto
Keywords: Conducta compulsiva
Funcions executives (Neuropsicologia)
Adults
Aprenentatge automàtic
Compulsive behavior
Executive functions (Neuropsychology)
Adulthood
Machine learning
Issue Date: 15-Nov-2024
Publisher: Elsevier
Abstract: Introduction: Obsessive compulsive disorder is associated with affected executive functioning, including memory, cognitive flexibility, and organizational strategies. As it was reported in previous studies, patients with preserved executive functions respond better to pharmacological treatment, while others need to keep trying different pharmacological strategies. Material and methods: In this work we used machine learning techniques to predict pharmacological response (OCD patients' symptomatology reduction) based on executive functioning and clinical variables. Among those variables we used anxiety, depression and obsessive-compulsive symptoms scores by applying State-Trait Anxiety Inventory, Hamilton Depression Rating Scale and Yale-Brown Obsessive Compulsive Scale respectively, while Rey-Osterrieth Complex Figure Test was used to assess organisation skills and non-verbal memory; Digits' subtests from Wechsler Adult Intelligence Scale-IV were used to assess short-term memory and working memory; and Raven's Progressive Matrices were applied to assess problem solving and abstract reasoning. Results: As a result of our analyses, we created a reliable algorithm that predicts Y-BOCS score after 12 weeks based on patients' clinical characteristics (sex at birth, age, pharmacological strategy, depressive and obsessive-compulsive symptoms, years passed since diagnostic and Raven's Progressive Matrices score) and Digits' scores. A high correlation (0.846) was achieved in predicted and true values. Conclusions: The present study proves the viability to predict if a patient would respond or not to a certain pharmacological strategy with high reliability based on sociodemographics, clinical variables and cognitive functions as short-term memory and working memory. These results are promising to develop future prediction models to help clinical decision making.
Note: Reproducció del document publicat a: https://doi.org/10.1016/j.sjpmh.2024.11.001
It is part of: Spanish Journal of Psychiatry and Mental Health, 2024, vol. 18, num.1, p. 51-57
URI: https://hdl.handle.net/2445/221637
Related resource: https://doi.org/10.1016/j.sjpmh.2024.11.001
ISSN: 2950-2853
Appears in Collections:Articles publicats en revistes (Ciències Clíniques)
Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))

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