Psychological medicine · 2024
Predicting remission following CBT for childhood anxiety disorders: a machine learning approach.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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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, Nauta Maaike, Rapee Ronald M, Schneider Silvia, Schniering Carolyn, Silverman Wendy K, Thastum Mikael, Thirlwall Kerstin, Waite Polly, Wergeland Gro Janne, Wuthrich Viviana, Hudson Jennifer L
Аннотация
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.
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.