Who Responds to a multi-component treatment for cannabis use disorder? Using multivariable and machine learning models to classify treatment responders and non-responders.

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 vs. non-responders.

This secondary analysis used data from National Drug Abuse Treatment Clinical Trials Network (NIDA CTN) multi-site outpatient clinical trial (CTN-0053, Achieving Cannabis Cessation – Evaluating N-Acetylcysteine Treatment (ACCENT)). 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.

Prediction performance for various machine learning and regression prediction models yielded area under the curves (AUCs) greater than 0.70 for four models (0.72-0.77), with support vector machine models having the highest overall accuracy (73%; 95% confidence interval [CI]: 68-78%) and AUC (0.77; 95% CI: 0.72, 0.83). Fourteen variables were retained in at least 3 of 4 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 upon chance prediction of treatment response to outpatient cannabis use disorder treatment, though further improvements in prediction performance are likely necessary for decisions about clinical care.

Related protocols: CTN-0053