2026-04-17T06:05:04Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/1958652025-12-04T19:18:44Zcom_2072_1057col_2072_478858col_2072_478906col_2072_478917col_2072_478921

00925njm 22002777a 4500 dc Oliva, Vincenzo author De Prisco, Michele author Pons-Cabrera, Maria Teresa author Guzmán, Pablo author Anmella, Gerard author Hidalgo Mazzei, Diego author Grande i Fullana, Iria author Fanelli, Giuseppe author Fabbri, Chiara author Serretti, Alessandro author Fornaro, Michele author Iasevoli, Felice author De Bartolomeis, Andrea author Murru, Andrea author Vieta i Pascual, Eduard, 1963- author Fico, Giovanna author 2023-03-23T13:25:05Z 2023-03-23T13:25:05Z 2022-07-06 2023-03-23T13:25:06Z 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. Alcoholisme Trastorn bipolar Cànnabis Drogoaddicció Aprenentatge automàtic Abús de substàncies Alcoholism Manic-depressive illness Cannabis Drug addiction Machine learning Substance abuse Machine Learning Prediction of Comorbid Substance Use Disorders among People with Bipolar Disorder