Who responds to a multi‐component treatment for cannabis use disorder? Using multivariable and machine learning models to classify treatment responders and non‐responders Article Swipe
Rachel L. Tomko
,
Bethany J. Wolf
,
Erin A. McClure
,
Matthew J. Carpenter
,
Kathryn M. Magruder
,
Lindsay M. Squeglia
,
Kevin M. Gray
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1111/add.16226
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1111/add.16226
Background and Aims Treatments for cannabis use disorder (CUD) have limited efficacy and little is known about who responds to existing treatments. Accurately predicting who will respond to treatment can improve clinical decision‐making by allowing clinicians to offer the most appropriate level and type of care. This study aimed to determine whether multivariable/machine learning models can be used to classify CUD treatment responders versus non‐responders. Methods This secondary analysis used data from a National Drug Abuse Treatment Clinical Trials Network multi‐site outpatient clinical trial in the United States. Adults with CUD ( n = 302) received 12 weeks of contingency management, brief cessation counseling and were randomized to receive additionally either (1) N‐Acetylcysteine or (2) placebo. Multivariable/machine learning models were used to classify treatment responders (i.e. two consecutive negative urine cannabinoid tests or a 50% reduction in days of use) versus non‐responders using baseline demographic, medical, psychiatric and substance use information. Results Prediction performance for various machine learning and regression prediction models yielded area under the curves (AUCs) >0.70 for four models (0.72–0.77), with support vector machine models having the highest overall accuracy (73%; 95% CI = 68–78%) and AUC (0.77; 95% CI = 0.72, 0.83). Fourteen variables were retained in at least three of four top models, including demographic (ethnicity, education), medical (diastolic/systolic blood pressure, overall health, neurological diagnosis), psychiatric (depressive symptoms, generalized anxiety disorder, antisocial personality disorder) and substance use (tobacco smoker, baseline cannabinoid level, amphetamine use, age of experimentation with other substances, cannabis withdrawal intensity) characteristics. Conclusions Multivariable/machine learning models can improve on chance prediction of treatment response to outpatient cannabis use disorder treatment, although further improvements in prediction performance are likely necessary for decisions about clinical care.
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Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/add.16226
- OA Status
- green
- Cited By
- 8
- References
- 75
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4367835180
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4367835180Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1111/add.16226Digital Object Identifier
- Title
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Who responds to a multi‐component treatment for cannabis use disorder? Using multivariable and machine learning models to classify treatment responders and non‐respondersWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-03Full publication date if available
- Authors
-
Rachel L. Tomko, Bethany J. Wolf, Erin A. McClure, Matthew J. Carpenter, Kathryn M. Magruder, Lindsay M. Squeglia, Kevin M. GrayList of authors in order
- Landing page
-
https://doi.org/10.1111/add.16226Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://pmc.ncbi.nlm.nih.gov/articles/PMC10524796/pdf/nihms-1902908.pdfDirect OA link when available
- Concepts
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Medicine, Cannabis, Substance abuse, Contingency management, Anxiety, Randomized controlled trial, Psychiatry, Clinical trial, Machine learning, Clinical psychology, Internal medicine, Computer science, Intervention (counseling)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
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2025: 6, 2024: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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