Cargando…
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...
Autores principales: | , , , , , , , , , , , , , , , |
---|---|
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 |
_version_ | 1784754569551544320 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9315469 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT olivavincenzo machinelearningpredictionofcomorbidsubstanceusedisordersamongpeoplewithbipolardisorder AT depriscomichele machinelearningpredictionofcomorbidsubstanceusedisordersamongpeoplewithbipolardisorder AT ponscabreramariateresa machinelearningpredictionofcomorbidsubstanceusedisordersamongpeoplewithbipolardisorder AT guzmanpablo machinelearningpredictionofcomorbidsubstanceusedisordersamongpeoplewithbipolardisorder AT anmellagerard machinelearningpredictionofcomorbidsubstanceusedisordersamongpeoplewithbipolardisorder AT hidalgomazzeidiego machinelearningpredictionofcomorbidsubstanceusedisordersamongpeoplewithbipolardisorder AT grandeiria machinelearningpredictionofcomorbidsubstanceusedisordersamongpeoplewithbipolardisorder AT fanelligiuseppe machinelearningpredictionofcomorbidsubstanceusedisordersamongpeoplewithbipolardisorder AT fabbrichiara machinelearningpredictionofcomorbidsubstanceusedisordersamongpeoplewithbipolardisorder AT serrettialessandro machinelearningpredictionofcomorbidsubstanceusedisordersamongpeoplewithbipolardisorder AT fornaromichele machinelearningpredictionofcomorbidsubstanceusedisordersamongpeoplewithbipolardisorder AT iasevolifelice machinelearningpredictionofcomorbidsubstanceusedisordersamongpeoplewithbipolardisorder AT debartolomeisandrea machinelearningpredictionofcomorbidsubstanceusedisordersamongpeoplewithbipolardisorder AT murruandrea machinelearningpredictionofcomorbidsubstanceusedisordersamongpeoplewithbipolardisorder AT vietaeduard machinelearningpredictionofcomorbidsubstanceusedisordersamongpeoplewithbipolardisorder AT ficogiovanna machinelearningpredictionofcomorbidsubstanceusedisordersamongpeoplewithbipolardisorder |