Predicting remission following CBT for childhood anxiety disorders: a machine learning approach

Bertie, Lizel-Antoinette, Quiroz, Juan C., Berkovsky, Shlomo, Arendt, Kristian, Bögels, Susan, Coleman, Jonathan R. I., Cooper, Peter, Creswell, Cathy, Eley, Thalia C., Hartman, Catharina, Fjermestadt, Krister, In-Albon, Tina, Lavallee, Kristen, Lester, Kathryn J., Lyneham, Heidi J., Marin, Carla E., McKinnon, Anna, McLellan, Lauren F., Meiser-Stedman, Richard ORCID: https://orcid.org/0000-0002-0262-623X, Nauta, Maaike, Rapee, Ronald M., Schneider, Silvia, Schniering, Carolyn, Silverman, Wendy K., Thastum, Mikael, Thirlwall, Kerstin, Waite, Polly, Wergeland, Gro Janne, Wuthrich, Viviana and Hudson, Jennifer L. (2024) Predicting remission following CBT for childhood anxiety disorders: a machine learning approach. Psychological Medicine. ISSN 0033-2917

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Abstract

BackgroundThe 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.MethodsA 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.ResultsAll 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.ConclusionsThese 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.

Item Type:	Article
Uncontrolled Keywords:	childhood anxiety,cognitive behavior therapy,machine learning,risk prediction
Faculty \ School:	Faculty of Medicine and Health Sciences > Norwich Medical School
UEA Research Groups:	Faculty of Medicine and Health Sciences > Research Centres > Lifespan Health Faculty of Medicine and Health Sciences > Research Groups > Mental Health
Depositing User:	LivePure Connector
Date Deposited:	20 Dec 2024 01:13
Last Modified:	20 Dec 2024 01:13
URI:	https://ueaeprints.uea.ac.uk/id/eprint/98051
DOI:	10.1017/S0033291724002654

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