Citation

Oliva V, De Prisco M, Pons-Cabrera MT, Guzmán P, Anmella G, Hidalgo-Mazzei D, Grande I, Fanelli G, Fabbri C, Serretti A, Fornaro M, Iasevoli F, de Bartolomeis A, Murru A, Vieta E, Fico G. J. Clin. Med. 2022; 11(14).

Copyright

(Copyright © 2022, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/jcm11143935

PMID

unavailable

Abstract

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. © 2022 by the authorsLicensee MDPI, Basel, Switzerland.

Language: en

Keywords

adult; human; female; male; alcoholism; bipolar disorder; alcohol use disorder; aggression; psychosis; suicide attempt; comorbidity; prediction; mood disorder; major clinical study; controlled study; seasonal variation; personality disorder; middle aged; mental patient; family history; drug dependence; sensitivity and specificity; hypomania; multivariate logistic regression analysis; Article; independent variable; random forest; machine learning; substance use disorder; cannabis use disorder