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Westenberg JN, Tai AMY, Elsner J, Kamel MM, Wong JSH, Azar P, Vo DX, Moore E, Mathew N, Seethapathy V, Choi F, Vogel M, Krausz RM. Treatment approaches and outcome trajectories for youth with high-risk opioid use: A narrative review. Early Interv Psychiatry 2022; 16:207-220. [PMID: 33913589 DOI: 10.1111/eip.13155] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 03/22/2021] [Indexed: 12/26/2022]
Abstract
AIM First use of opioids often happens in adolescence and an increasing number of opioid overdoses are being reported among youth. The purpose of this narrative review was to present the treatment approaches for youth with high-risk opioid use, determine whether the literature supports the use of opioid agonist treatment among youth and identify evidence for better treatment outcomes in the younger population. METHODS A search of the literature on PubMed using MeSH terms specific to youth, opioid use and treatment approaches generated 1436 references. Following a screening process, 137 papers were found to be relevant to the treatment of high-risk opioid use among youth. After full-text review, 19 eligible studies were included: four randomized controlled trials, nine observational studies and six reviews. RESULTS Research for the different treatment options among youth is limited. The available evidence shows better outcomes in terms of retention in care and cost-effectiveness for opioid agonist treatment than abstinence-based comparisons. Integrating psychosocial interventions into the continuum of care for youth can be an effective way of addressing comorbid psychiatric conditions and emotional drivers of substance use, leading to improved treatment trajectories. CONCLUSIONS From the limited findings, there is no evidence to deny youth with high-risk opioid use the same treatment options available to adults. A combination of pharmacological and youth-specific psychosocial interventions is required to maximize retention and survival. There is an urgent need for more research to inform clinical strategies toward appropriate treatment goals for such vulnerable individuals.
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Affiliation(s)
- Jean Nicolas Westenberg
- Addictions and Concurrent Disorders Research Group, Institute of Mental Health, Department of Psychiatry, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Andy M Y Tai
- Addictions and Concurrent Disorders Research Group, Institute of Mental Health, Department of Psychiatry, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Julie Elsner
- Addictions and Concurrent Disorders Research Group, Institute of Mental Health, Department of Psychiatry, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Mostafa M Kamel
- Addictions and Concurrent Disorders Research Group, Institute of Mental Health, Department of Psychiatry, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Neuropsychiatry, Tanta University, Tanta, Egypt
| | - James S H Wong
- Addictions and Concurrent Disorders Research Group, Institute of Mental Health, Department of Psychiatry, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Pouya Azar
- Complex Pain and Addiction Services, Vancouver General Hospital, Vancouver, British Columbia, Canada.,Department of Psychiatry, University of British Columbia, Faculty of Medicine, Vancouver, British Columbia, Canada
| | - Dzung X Vo
- Division of Adolescent Health and Medicine, Department of Pediatrics, University of British Columbia, Faculty of Medicine, Vancouver, British Columbia, Canada
| | - Eva Moore
- Division of Adolescent Health and Medicine, Department of Pediatrics, University of British Columbia, Faculty of Medicine, Vancouver, British Columbia, Canada
| | - Nickie Mathew
- Department of Psychiatry, University of British Columbia, Faculty of Medicine, Vancouver, British Columbia, Canada.,BC Mental Health & Substance Use Services, Provincial Health Services Authority, Vancouver, British Columbia, Canada
| | - Vijay Seethapathy
- Department of Psychiatry, University of British Columbia, Faculty of Medicine, Vancouver, British Columbia, Canada.,BC Mental Health & Substance Use Services, Provincial Health Services Authority, Vancouver, British Columbia, Canada
| | - Fiona Choi
- Addictions and Concurrent Disorders Research Group, Institute of Mental Health, Department of Psychiatry, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Marc Vogel
- Psychiatric University Clinic Basel, Basel, Switzerland
| | - Reinhard M Krausz
- Addictions and Concurrent Disorders Research Group, Institute of Mental Health, Department of Psychiatry, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
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Tai AMY, Albuquerque A, Carmona NE, Subramanieapillai M, Cha DS, Sheko M, Lee Y, Mansur R, McIntyre RS. Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry. Artif Intell Med 2019; 99:101704. [PMID: 31606109 DOI: 10.1016/j.artmed.2019.101704] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Revised: 03/04/2019] [Accepted: 08/08/2019] [Indexed: 01/16/2023]
Abstract
INTRODUCTION Machine learning capability holds promise to inform disease models, the discovery and development of novel disease modifying therapeutics and prevention strategies in psychiatry. Herein, we provide an introduction on how machine learning/Artificial Intelligence (AI) may instantiate such capabilities, as well as provide rationale for its application to psychiatry in both research and clinical ecosystems. METHODS Databases PubMed and PsycINFO were searched from 1966 to June 2016 for keywords:Big Data, Machine Learning, Precision Medicine, Artificial Intelligence, Mental Health, Mental Disease, Psychiatry, Data Mining, RDoC, and Research Domain Criteria. Articles selected for review were those that were determined to be aligned with the objective of this particular paper. RESULTS Results indicate that AI is a viable option to build useful predictors of outcome while offering objective and comparable accuracy metrics, a unique opportunity, particularly in mental health research. The approach has also consistently brought notable insight into disease models through processing the vast amount of already available multi-domain, semi-structured medical data. The opportunity for AI in psychiatry, in addition to disease-model refinement, is in characterizing those at risk, and it is likely also relevant to personalizing and discovering therapeutics. CONCLUSIONS Machine learning currently provides an opportunity to parse disease models in complex, multi-factorial disease states (e.g. mental disorders) and could possibly inform treatment selection with existing therapies and provide bases for domain-based therapeutic discovery.
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Affiliation(s)
- Andy M Y Tai
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
| | - Alcides Albuquerque
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
| | - Nicole E Carmona
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
| | | | - Danielle S Cha
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
| | - Margarita Sheko
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
| | - Yena Lee
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
| | - Rodrigo Mansur
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
| | - Roger S McIntyre
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Department of Pharmacology, University of Toronto, Toronto, ON, Canada; Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada.
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