Investigating predictive factors of dialectical behavior therapy skills training efficacy for alcohol and concurrent substance use disorders: A machine learning study

Background: Dialectical Behavior Therapy Skills Training (DBT-ST) as stand-alone treatment has demonstrated promising outcomes for the treatment of alcohol use disorder (AUD) and concurrent substance use disorders (SUDs). However, no studies have so far empirically investigated factors that might predict efficacy of this therapeutic model. Methods: 275 treatment-seeking individuals with AUD and other SUDs were consecutively admitted to a 3-month DBT-ST program (in- + outpatient; outpatient settings). The machine learning routine applied (i.e. penalized regression combined with a nested cross-validation procedure) was conducted in order to estimate predictive values of a wide panel of clinical variables in a single statistical framework on drop-out and substance-use behaviors, dealing with related multicollinearity, and eliminating redundant variables. Results: The cross-validated elastic net model significantly predicted the drop-out. The bootstrap analysis revealed that subjects who showed substance-use behaviors during the intervention and who were treated with the mixed setting (i.e., in- and outpatient) program, together with higher ASI alcohol scores were associated with an higher probability of drop-out. On the contrary, older subjects, higher levels of education, together with higher scores of DERS awareness subscale were negatively associated to drop-out. Similarly, lifetime co-diagnoses of anxiety, bipolar, and gambling disorders, together with bulimia nervosa negatively predicted the drop-out. The machine learning model did not identify predictive variables of substance-use behaviors during the treatment. Conclusions: The DBT-ST program could be considered a valid therapeutic approach especially when AUD and other SUDs co-occur with other psychiatric conditions and, it is carried out as a full outpatient intervention.