3 - (SYM 78) Predicting Remission Following CBT for Childhood Anxiety Disorders: A Machine Learning Approach

The identification of predictors of treatment response is crucial for improving treatment outcome for children with anxiety disorders. Machine learning methods provide opportunities to identify combinations of factors that contribute to risk prediction models. To leverage this newer statistical method, we applied machine learning to predict anxiety disorder remission in a large, pooled sample of 2114 anxious youth (5-18 years). Potential predictors included demographic, clinical, parental and treatment variables with data obtained pre-treatment, post-treatment, and at least one follow-up. All machine learning models performed similarly for remission outcomes, with AUC between 0.67-0.69. There was significant alignment between the factors that contributed to the two models predicting remission. Children who were older, had multiple anxiety disorders, comorbid depression, comorbid externalising disorders, received group treatment and therapy delivered by a less experienced therapist, and who had a parent with higher anxiety and depression symptoms, were more likely than other children to still meet criteria for anxiety disorders at the completion of therapy. In both models, the absence of a social anxiety disorder and being treated by a therapist with less experience contributed to the model predicting a higher likelihood of remission. These findings underscore the utility of prediction models that may indicate which children are more likely to remit or are more at risk of non-remission following CBT for childhood anxiety.