Guía de práctica clínica para el tratamiento farmacológico y psicológico de los pacientes adultos con depresión y un diagnóstico comórbido de trastorno por uso de sustancias

Torrens, Marta; Muñoz, Judit Tirado; Fonseca, Francina; Albaladejo, Magı́ Farré; González-Pinto, Ana; Arrojo, Manuel; Bernardo, Miguel; Arranz, Belén; Garriga, Marina; Sáiz, Pilar A.; Flórez, Gerardo; Goikolea, José Manuel; Zorrilla, Iñaki; Cunill, Ruth; Castells, Xavier; Becoña, Elisardo; López, Ana; Román, Luís San

doi:10.20882/adicciones.1559

Cited by 5 publications

(1 citation statement)

References 48 publications

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“…Our ndings are consistent with several other randomized clinical trials for PTSD pharmacotherapy which include: sertraline 98 , paroxetine 99 , uoxetine 100 and venlafaxine 100 . However, a recent clinical practice guideline study suggested that patients with depression and cannabis use, should not be treated with venlafaxine 101 . But the need for more de nitive larger, multisite, randomized clinical trials is essential.…”

Section: Montelukastmentioning

confidence: 99%

DeepBiomarker2: Prediction of alcohol and substance use disorder risk in post-traumatic stress disorder patients using electronic medical records and multiple social determinants of health

Miranda

Fan

et al. 2023

Preprint

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Introduction: Prediction of high-risk events amongst patients with mental disorders is critical for personalized interventions. In our previous study, we developed a deep learning-based model, DeepBiomarker by utilizing electronic medical records (EMR) to predict the outcomes of patients with suicide-related events in post-traumatic stress disorder (PTSD) patients. Methods We improved our deep learning model to develop DeepBiomarker2 through data integration of multimodal information: lab tests, medication use, diagnosis, and social determinants of health (SDoH) parameters (both individual and neighborhood level) from EMR data for outcome prediction. We further refined our contribution analysis for identifying key factors. We applied DeepBiomarker2 to analyze EMR data of 38,807 patients from University of Pittsburgh Medical Center diagnosed with PTSD to determine their risk of developing alcohol and substance use disorder (ASUD). Results DeepBiomarker2 predicted whether a PTSD patient will have a diagnosis of ASUD within the following 3 months with a c-statistic (receiver operating characteristic AUC) of 0·93. We used contribution analysis technology to identify key lab tests, medication use and diagnosis for ASUD prediction. These identified factors imply that the regulation of the energy metabolism, blood circulation, inflammation, and microbiome is involved in shaping the pathophysiological pathways promoting ASUD risks in PTSD patients. Our study found protective medications such as oxybutynin, magnesium oxide, clindamycin, cetirizine, montelukast and venlafaxine all have a potential to reduce risk of ASUDs. Discussion DeepBiomarker2 can predict ASUD risk with high accuracy and can further identify potential risk factors along with medications with beneficial effects. We believe that our approach will help in personalized interventions of PTSD for a variety of clinical scenarios.

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Section: Montelukastmentioning

confidence: 99%