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Accurately predicting who will respond to treatment can improve clinical decision\u2010making 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\u2010responders.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This secondary analysis used data from a National Drug Abuse Treatment Clinical Trials Network multi\u2010site outpatient clinical trial in the United States. Adults with CUD (<jats:italic>n<\/jats:italic>\u2009=\u2009302) received 12\u2009weeks of contingency management, brief cessation counseling and were randomized to receive additionally either (1) N\u2010Acetylcysteine 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\u2010responders using baseline demographic, medical, psychiatric and substance use information.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Prediction performance for various machine learning and regression prediction models yielded area under the curves (AUCs) &gt;0.70 for four models (0.72\u20130.77), with support vector machine models having the highest overall accuracy (73%; 95% CI = 68\u201378%) 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.1111\/add.16226","type":"journal-article","created":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T06:20:41Z","timestamp":1683094841000},"page":"1965-1974","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Who responds to a multi\u2010component treatment for cannabis use disorder? 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