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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Detalles Bibliográficos
Autores: Oliva, Vincenzo, De Prisco, Michele, Pons Cabrera, Maria Teresa, Guzmán, Pablo, Anmella, Gerard, Hidalgo Mazzei, Diego, Grande i Fullana, Iria, Fanelli, Giuseppe, Fabbri, Chiara, Serretti, Alessandro, Fornaro, Michele, Iasevoli, Felice, De Bartolomeis, Andrea, Murru, Andrea, Vieta i Pascual, Eduard, 1963-, Fico, Giovanna
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/195865
Acceso en línea:https://hdl.handle.net/2445/195865
Access Level:acceso abierto
Palabra clave:Alcoholisme
Trastorn bipolar
Cànnabis
Drogoaddicció
Aprenentatge automàtic
Abús de substàncies
Alcoholism
Manic-depressive illness
Cannabis
Drug addiction
Machine learning
Substance abuse
Descripción
Sumario: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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