RT Journal Article
T1 Prediction of pharmacological response in OCD using machine learning techniques and clinical and neuropsychological variables
A1 Tubío Fungueiriño, María
A1 Cernadas García, Eva
A1 Fernández Delgado, Manuel
A1 Arrojo, Manuel
A1 Bertolín, Sara
A1 Real, Eva
A1 Menchón, José Manuel
A1 Carracedo Álvarez, Ángel
A1 Alonso, María del Pino
A1 Fernández Prieto, Montserrat
A1 Segalàs, Cinto
K1 OCD
K1 OCD treatment
K1 Pharmacological response
K1 Machine learning
K1 Executive functions
AB 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.
PB Elsevier
SN 2950-2853
YR 2024
FD 2024-11-15
LK https://hdl.handle.net/10347/42126
UL https://hdl.handle.net/10347/42126
LA eng
NO Spanish Journal of Psychiatry and Mental Health Volume 18, Issue 1, January–March 2025, Pages 51-57
NO This work has received financial support from the Consellería de Educación, Universidade e Formación Profesional (accreditation 2019-2022 ED431G-2019/04) and the European Regional Development Fund (ERDF), which acknowledges the CiTIUS – Centro Singular de Investigación en Tecnoloxías Intelixentes da Universidade de Santiago de Compostela as a Research Center of the Galician University System. SB was supported by Río Hortega grant CM21/00278. This study has been funded by Instituto de Salud Carlos III through the grants PI16/00950, PI18/00856, PI19/01184, PI22/00752 and CM21/00278 (co-funded by European Social Fund. ESF investing in your future).
DS Minerva
RD 3 may 2026