Literature DB >> 32078842

Treatment resistant opioid use disorder (TROUD): Definition, rationale, and recommendations.

David A Patterson Silver Wolf1, Mark Gold2.   

Abstract

The opioid overdose epidemic kills about 130 people a day in the United States and it is estimated that there are about 2.1 million people who suffer from an opioid use disorder (OUD). Academic neuroscientists, psychiatrists and the National Institute of Drug Abuse have spent the last forty-years establishing the foundation of addiction as a brain disorder. It is now clear that extended opioid use causes multiple important and at times, irreversible changes to the brain, especially to its dopamine and opioid systems. With our recognized criteria for diagnosis and the accepted multifaceted treatment approach of both professional psychotherapy and medications that assist treatments, treatment failures should be limited. Unfortunately, this is not the case. Slips, relapses, overdose and multiple failures are all too common. Similar to treatment resistant depression there is a subpopulation who do not respond to standard OUD treatments. However, the field has suggested that if a treatment does not work, it is either the patients fault, they have not hit bottom or simply we need to try the same treatment again. There is a rational to consider this a new category of OUD, treatment resistant opioid use disorder (TROUD). This paper explores past treatment attempts data from OUD patients entering traditional outpatient treatment and makes recommendations how TROUD can be defined. It challenges the addiction research and treatment providers to change its focus from individuals being resistant to the unique conditions associated with this brain disorder as being resistant to treatment as usual.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Addiction medicine; Addiction psychiatry; Addiction treatment; Depression; Opioid epidemic; Opioid use disorder; Treatment resistant; Treatment resistant opioid use disorder

Mesh:

Substances:

Year:  2020        PMID: 32078842     DOI: 10.1016/j.jns.2020.116718

Source DB:  PubMed          Journal:  J Neurol Sci        ISSN: 0022-510X            Impact factor:   3.181


  4 in total

1.  Machine Learning for Predicting Risk of Early Dropout in a Recovery Program for Opioid Use Disorder.

Authors:  Assaf Gottlieb; Andrea Yatsco; Christine Bakos-Block; James R Langabeer; Tiffany Champagne-Langabeer
Journal:  Healthcare (Basel)       Date:  2022-01-25

2.  Reduced habenular volumes and neuron numbers in male heroin addicts: a post-mortem study.

Authors:  Hans-Gert Bernstein; Johann Steiner; Ulf J Müller; Moritz Ahrens; Veronika Vasilevska; Henrik Dobrowolny; Kolja Schiltz; Konstantin Schlaaff; Christian Mawrin; Thomas Frodl; Bernhard Bogerts; Tomasz Gos; Kurt Truebner
Journal:  Eur Arch Psychiatry Clin Neurosci       Date:  2020-10-01       Impact factor: 5.270

Review 3.  Prevention, diagnosis, and treatment of opioid use disorder under the supervision of opioid stewardship programs: it's time to act now.

Authors:  Eun-Ji Kim; Eun-Jung Hwang; Yeong-Min Yoo; Kyung-Hoon Kim
Journal:  Korean J Pain       Date:  2022-10-01

Review 4.  A Novel Precision Approach to Overcome the "Addiction Pandemic" by Incorporating Genetic Addiction Risk Severity (GARS) and Dopamine Homeostasis Restoration.

Authors:  Kenneth Blum; Shan Kazmi; Edward J Modestino; Bill William Downs; Debasis Bagchi; David Baron; Thomas McLaughlin; Richard Green; Rehan Jalali; Panayotis K Thanos; Igor Elman; Rajendra D Badgaiyan; Abdalla Bowirrat; Mark S Gold
Journal:  J Pers Med       Date:  2021-03-16
  4 in total

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