Kristian Arendt

Background: 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.Methods: A machine learning approach was applied to predict anxiety disorder remission in a large 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.Results: All machine learning models performed similarly for remission outcomes, with AUC between 0.67 and 0.69. There was significant alignment between the factors that contributed to the models predicting two target outcomes: remission of all anxiety disorders and the primary anxiety disorder. Children who were older, had multiple anxiety disorders, comorbid depression, comorbid externalising disorders, received group treatment and therapy delivered by a more 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.Conclusions: 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.

Background: Children and adolescents demonstrate diverse patterns of symptom change and disorder remission following cognitive behavioural therapy (CBT) for anxiety disorders. To better understand children who respond sub‐optimally to CBT, this study investigated youths ( N = 1,483) who continued to meet criteria for one or more clinical anxiety diagnosis immediately following treatment or at any point during the 12 months following treatment. Methods: Data were collected from 10 clinical sites with assessments at pre‐and post‐treatment and at least once more at 3, 6 or 12‐month follow‐up. Participants were assigned to one of three groups based on diagnostic status for youths who: (a) retained an anxiety diagnosis from post to end point (minimal responders); (b) remitted anxiety diagnoses at post but relapsed by end point (relapsed responders); and (c) retained a diagnosis at post but remitted to be diagnosis free at end point (delayed responders). Growth curve models assessed patterns of change over time for the three groups and examined predictors associated with these patterns including demographic, clinical and parental factors, as well as treatment factors. Results: Higher primary disorder severity, being older, having a greater number of anxiety disorders, having social anxiety disorder, as well as higher maternal psychopathology differentiated the minimal responders from the delayed and relapsed responders at the baseline. Results from the growth curve models showed that severity of the primary disorder and treatment modality differentiated patterns of linear change only. Higher severity was associated with significantly less improvement over time for the minimal and relapsed response groups, as was receiving group CBT, when compared to the delayed response group. Conclusions: Sub‐optimal response patterns can be partially differentiated using variables assessed at pre‐treatment. Increased understanding of different patterns of change following treatment may provide direction for clinical decision‐making and for tailoring treatments to specific groups of clinically anxious youth. Future research may benefit from assessing progress during treatment to detect emerging response patterns earlier.

Background: We previously reported an association between 5HTTLPR genotype and outcome following cognitive–behavioural therapy (CBT) in child anxiety (Cohort 1). Children homozygous for the low-expression short-allele showed more positive outcomes. Other similar studies have produced mixed results, with most reporting no association between genotype and CBT outcome.Aims: To replicate the association between 5HTTLPR and CBT outcome in child anxiety from the Genes for Treatment study (GxT Cohort 2, n = 829).Method: Logistic and linear mixed effects models were used to examine the relationship between 5HTTLPR and CBT outcomes. Mega-analyses using both cohorts were performed.Results: There was no significant effect of 5HTTLPR on CBT outcomes in Cohort 2. Mega-analyses identified a significant association between 5HTTLPR and remission from all anxiety disorders at follow-up (odds ratio 0.45, P = 0.014), but not primary anxiety disorder outcomes.Conclusions: The association between 5HTTLPR genotype and CBT outcome did not replicate. Short-allele homozygotes showed more positive treatment outcomes, but with small, non-significant effects. Future studies would benefit from utilising whole genome approaches and large, homogenous samples.