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

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.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. DOI: 10.1016/j.drugalcdep.2021.108723作为单一治疗手段的辩证行为疗法技能训练（Dialectical Behavior Therapy Skills Training，DBT-ST）在酒精使用障碍（alcohol use disorder，AUD）及共病物质使用障碍（substance use disorders，SUDs）的治疗中已展现出颇具潜力的疗效。然而，目前尚无实证研究探究可预测该治疗模型疗效的影响因素。本研究纳入275名存在酒精使用障碍及其他物质使用障碍的求治者，将其连续纳入为期3个月的辩证行为疗法技能训练项目，该项目涵盖住院与门诊两种诊疗场景。为在单一统计框架内评估多维度临床变量对脱落行为及物质使用行为的预测价值，本研究采用了结合嵌套交叉验证流程的惩罚回归机器学习方法，以此处理相关多重共线性问题并剔除冗余变量。DOI: 10.1016/j.drugalcdep.2021.108723