A Preliminary Risk Prediction Model For Cannabis Use Disorder

The ongoing trend toward legalization of cannabis for medicinal/recreational purposes is expected to increase the prevalence of cannabis use disorder (CUD). Thus, it is imperative to be able to predict the quantitative risk of developing CUD for a cannabis user based on their personal risk factors. Yet no such model currently exists. In this study, we perform preliminary analysis toward building such a model. The data come from n = 94 regular cannabis users recruited from Albuquerque, New Mexico during 2007-2010. As the data are cross-sectional, we only consider risk factors that remain relatively stable over time. We apply statistical and machine learning classification techniques that allow n to be small relative to the number of predictors. We use predictive accuracy estimated using leave-one-out-cross-validation to evaluate model performance. The final model is a LASSO logistic regression model consisting of the following seven risk factors: age; level of enjoyment from initial cigarette smoking; total score on Impulsive Sensation-Seeking Scale questionnaire; score on cognitive instability factor of Barratt Impulsivity Scale questionnaire; and scores on neuroticism, openness, and conscientiousness personality traits of Neuroticism, Extraversion, and Openness inventory. This model has an overall accuracy of 0.66 and the area under its receiver operating characteristic curve is 0.65. In summary, a preliminary relative risk model for predicting the quantitative risk of CUD is developed. It can be employed to identify users at high risk of CUD who may be provided with early intervention.