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, 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, 54 (16). pp. 4612-4622. 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
Additional Information:	Funding statement: This work was supported by Australian Research Council grant DP0878609 (J.L.H., Jenny Donald, PhD, R.M.R., T.C.E.); Australian National Health and Medical Research Council grants (1027556: R.M.R., J.L.H., H.J.L., Cathy Mihalopolous, BSc[Hons], PhD, V.W), (488505: H.J.L., J.L.H., R.M.R.), (382008: J.L.H. and R.M.R.), and (1103611 L.F.M., V.W); TrygFonden grant (7-10-1391: M.T. and Esben Hougaard, PhD); Edith og Godtfred Kirk Christiansens Fond grant (21- 5675: M.T.); Swiss National Science Foundation grant (105314-116517: S.S.); Western Norway Regional Health Authority grants (911253: Odd E. Havik, PhD) and (911366: E.H.); National Institute of Mental Health R01MH079943 (W.K.S.); UK MRC Clinical Fellowship (G0802821: R.M.-S.); UK National Institute for Health Research (NIHR) grants (PB-PG-0110-21190: C.C.) and (PB-PG-0107-12042: P.C.); UK MRC grants (09-800-17: P.C. and C.C.; G0802326: K.T., P.C., and C.C.; G1002011: P.W., C.C., and P.C.; and G0601874; C.C.). Grant 09-800-17 was managed by NIHR on behalf of the MRC-NIHR partnership. Combined study supported by UK MRC grant G0901874/1 (T.C.E.); Combined study presents independent research partly funded by the NIHR Biomedical Research Centre at South London and Maudsley, NHS Foundation Trust, and King's College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health.
Uncontrolled Keywords:	childhood anxiety,cognitive behavior therapy,machine learning,risk prediction,psychiatry and mental health,applied psychology ,/dk/atira/pure/subjectarea/asjc/2700/2738
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
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	20 Dec 2024 01:13
Last Modified:	14 Feb 2025 01:23
URI:	https://ueaeprints.uea.ac.uk/id/eprint/98051
DOI:	10.1017/S0033291724002654

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