ArticleAddictive behaviors2025
Predictors of treatment attrition among individuals in substance use disorder treatment: A machine learning approach.
Article in Addictive behaviors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrating Psychosocial Factors into Artificial Intelligence Models for Predicting Addiction Treatment Outcomes: A Systematic Review.European addiction research · 2026Pooled it
- Project Harmony 2.0: protocol for an updated and expanded individual patient data meta-analysis and virtual clinical trial of treatments for comorbid PTSD and substance use disorders.European journal of psychotraumatology · 2026Article
- Challenges, Potential Solutions in Recruiting and Retaining Participants for Alcohol Use Disorder Research: A Literature Review.Healthcare (Basel, Switzerland) · 2026Review
- Predicting treatment retention in medication for opioid use disorder: a machine learning approach using NLP and LLM-derived clinical features.Journal of the American Medical Informatics Association : JAMIA · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundEarly treatment discontinuation in substance use disorder treatment settings is common and often difficult to predict. We leveraged a machine learning approach (i.e., random forest) to identify individuals at risk for treatment attrition, and specific factors associated with treatment discontinuation.
methodParticipants (N = 29,809) were individuals ≥ 18 years who attended substance use disorder treatment facilities in the United States. Using random forest, we aimed to predict three outcomes (1) leaving against medical advice (AMA), (2) discharging involuntarily, and (3) discharging early for any reason. Predictors included participant demographics, substance use the month before and at intake, indices of mental and physical health, as well as treatment center and program type.
findingsWe observed low to moderate area under the curve (range = 0.631-0.671), high negative predictive values (range = 0.853-0.965), and low positive predictive values (0.088-0.336) across the three treatment attrition outcomes. The most robust predictors of the three outcomes included treatment center, treatment type, and participant age. Additional predictors of the three outcomes included employment status; reason for treatment; primary drug at intake and frequency of use; prescription opioid, benzodiazepine, or heroin use at intake; living status at intake; and driving under the influence prior to treatment.
conclusionsOur models were able to accurately identify individuals who remained in treatment, but not those who left treatment prematurely. The most robust predictors of treatment discontinuation were treatment center and program type, suggesting that targeting treatment facility features may have a significant impact on reducing treatment attrition and improving long-term recovery.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.