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Machine Learning Prediction of Comorbid Substance Use Disorders among People with Bipolar Disorder

Substance use disorder (SUD) is a common comorbidity in individuals with bipolar disorder (BD), and it is associated with a severe course of illness, making early identification of the risk factors for SUD in BD warranted. We aimed to identify, through machine-learning models, the factors associated...

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Autores principales: Oliva, Vincenzo, De Prisco, Michele, Pons-Cabrera, Maria Teresa, Guzmán, Pablo, Anmella, Gerard, Hidalgo-Mazzei, Diego, Grande, Iria, Fanelli, Giuseppe, Fabbri, Chiara, Serretti, Alessandro, Fornaro, Michele, Iasevoli, Felice, de Bartolomeis, Andrea, Murru, Andrea, Vieta, Eduard, Fico, Giovanna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9315469/
https://www.ncbi.nlm.nih.gov/pubmed/35887699
http://dx.doi.org/10.3390/jcm11143935
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author Oliva, Vincenzo
De Prisco, Michele
Pons-Cabrera, Maria Teresa
Guzmán, Pablo
Anmella, Gerard
Hidalgo-Mazzei, Diego
Grande, Iria
Fanelli, Giuseppe
Fabbri, Chiara
Serretti, Alessandro
Fornaro, Michele
Iasevoli, Felice
de Bartolomeis, Andrea
Murru, Andrea
Vieta, Eduard
Fico, Giovanna
author_facet Oliva, Vincenzo
De Prisco, Michele
Pons-Cabrera, Maria Teresa
Guzmán, Pablo
Anmella, Gerard
Hidalgo-Mazzei, Diego
Grande, Iria
Fanelli, Giuseppe
Fabbri, Chiara
Serretti, Alessandro
Fornaro, Michele
Iasevoli, Felice
de Bartolomeis, Andrea
Murru, Andrea
Vieta, Eduard
Fico, Giovanna
author_sort Oliva, Vincenzo
collection PubMed
description Substance use disorder (SUD) is a common comorbidity in individuals with bipolar disorder (BD), and it is associated with a severe course of illness, making early identification of the risk factors for SUD in BD warranted. We aimed to identify, through machine-learning models, the factors associated with different types of SUD in BD. We recruited 508 individuals with BD from a specialized unit. Lifetime SUDs were defined according to the DSM criteria. Random forest (RF) models were trained to identify the presence of (i) any (SUD) in the total sample, (ii) alcohol use disorder (AUD) in the total sample, (iii) AUD co-occurrence with at least another SUD in the total sample (AUD+SUD), and (iv) any other SUD among BD patients with AUD. Relevant variables selected by the RFs were considered as independent variables in multiple logistic regressions to predict SUDs, adjusting for relevant covariates. AUD+SUD could be predicted in BD at an individual level with a sensitivity of 75% and a specificity of 75%. The presence of AUD+SUD was positively associated with having hypomania as the first affective episode (OR = 4.34 95% CI = 1.42–13.31), and the presence of hetero-aggressive behavior (OR = 3.15 95% CI = 1.48–6.74). Machine-learning models might be useful instruments to predict the risk of SUD in BD, but their efficacy is limited when considering socio-demographic or clinical factors alone.
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spelling pubmed-93154692022-07-27 Machine Learning Prediction of Comorbid Substance Use Disorders among People with Bipolar Disorder Oliva, Vincenzo De Prisco, Michele Pons-Cabrera, Maria Teresa Guzmán, Pablo Anmella, Gerard Hidalgo-Mazzei, Diego Grande, Iria Fanelli, Giuseppe Fabbri, Chiara Serretti, Alessandro Fornaro, Michele Iasevoli, Felice de Bartolomeis, Andrea Murru, Andrea Vieta, Eduard Fico, Giovanna J Clin Med Article Substance use disorder (SUD) is a common comorbidity in individuals with bipolar disorder (BD), and it is associated with a severe course of illness, making early identification of the risk factors for SUD in BD warranted. We aimed to identify, through machine-learning models, the factors associated with different types of SUD in BD. We recruited 508 individuals with BD from a specialized unit. Lifetime SUDs were defined according to the DSM criteria. Random forest (RF) models were trained to identify the presence of (i) any (SUD) in the total sample, (ii) alcohol use disorder (AUD) in the total sample, (iii) AUD co-occurrence with at least another SUD in the total sample (AUD+SUD), and (iv) any other SUD among BD patients with AUD. Relevant variables selected by the RFs were considered as independent variables in multiple logistic regressions to predict SUDs, adjusting for relevant covariates. AUD+SUD could be predicted in BD at an individual level with a sensitivity of 75% and a specificity of 75%. The presence of AUD+SUD was positively associated with having hypomania as the first affective episode (OR = 4.34 95% CI = 1.42–13.31), and the presence of hetero-aggressive behavior (OR = 3.15 95% CI = 1.48–6.74). Machine-learning models might be useful instruments to predict the risk of SUD in BD, but their efficacy is limited when considering socio-demographic or clinical factors alone. MDPI 2022-07-06 /pmc/articles/PMC9315469/ /pubmed/35887699 http://dx.doi.org/10.3390/jcm11143935 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Oliva, Vincenzo
De Prisco, Michele
Pons-Cabrera, Maria Teresa
Guzmán, Pablo
Anmella, Gerard
Hidalgo-Mazzei, Diego
Grande, Iria
Fanelli, Giuseppe
Fabbri, Chiara
Serretti, Alessandro
Fornaro, Michele
Iasevoli, Felice
de Bartolomeis, Andrea
Murru, Andrea
Vieta, Eduard
Fico, Giovanna
Machine Learning Prediction of Comorbid Substance Use Disorders among People with Bipolar Disorder
title Machine Learning Prediction of Comorbid Substance Use Disorders among People with Bipolar Disorder
title_full Machine Learning Prediction of Comorbid Substance Use Disorders among People with Bipolar Disorder
title_fullStr Machine Learning Prediction of Comorbid Substance Use Disorders among People with Bipolar Disorder
title_full_unstemmed Machine Learning Prediction of Comorbid Substance Use Disorders among People with Bipolar Disorder
title_short Machine Learning Prediction of Comorbid Substance Use Disorders among People with Bipolar Disorder
title_sort machine learning prediction of comorbid substance use disorders among people with bipolar disorder
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9315469/
https://www.ncbi.nlm.nih.gov/pubmed/35887699
http://dx.doi.org/10.3390/jcm11143935
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