Machine Learning-Driven Analysis of Individualized Treatment Effects Comparing Buprenorphine and Naltrexone in Opioid Use Disorder Relapse Prevention.

Journal: Journal of addiction medicine

PMID: 38776423

Abstract

OBJECTIVE: A trial comparing extended-release naltrexone and sublingual buprenorphine-naloxone demonstrated higher relapse rates in individuals randomized to extended-release naltrexone. The effectiveness of treatment might vary based on patient characteristics. We hypothesized that causal machine learning would identify individualized treatment effects for each medication.

Authors

Majid Afshar

Loyola University Chicago, Chicago, IL.
Emma J Graham Linck
Alexandra B Spicer
John Rotrosen
Elizabeth M Salisbury-Afshar
Pratik Sinha

Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, and.
Matthew W Semler

Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Matthew M Churpek

Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States.

Keywords

Adult Buprenorphine Buprenorphine, Naloxone Drug Combination Delayed-Action Preparations Female Humans Machine Learning Male Middle Aged Naltrexone Narcotic Antagonists Opiate Substitution Treatment Opioid-Related Disorders Precision Medicine Secondary Prevention

External Resources

View on PubMed Access via DOI PubMed (38776423)

Machine Learning-Driven Analysis of Individualized Treatment Effects Comparing Buprenorphine and Naltrexone in Opioid Use Disorder Relapse Prevention.

Abstract

Authors

Keywords

External Resources

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