View in EDS

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

Saved in:
Bibliographic Details
Title: Predicting remission following CBT for childhood anxiety disorders: a machine learning approach.
Authors: 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
Source: Psychological Medicine. Dec2024, Vol. 54 Issue 16, p4612-4622. 11p.
Subjects: Anxiety disorders treatment, Risk assessment, Prediction models, Research funding, Disease remission, Descriptive statistics, Anxiety disorders, Cognitive therapy, Machine learning, Comorbidity, Mental depression, Children
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Psychological Medicine is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
FullText Text:
  Availability: 0
Header DbId: pbh
DbLabel: Psychology and Behavioral Sciences Collection
An: 182451611
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Predicting remission following CBT for childhood anxiety disorders: a machine learning approach.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Bertie%2C+Lizel-Antoinette%22">Bertie, Lizel-Antoinette</searchLink><br /><searchLink fieldCode="AR" term="%22Quiroz%2C+Juan+C%2E%22">Quiroz, Juan C.</searchLink><br /><searchLink fieldCode="AR" term="%22Berkovsky%2C+Shlomo%22">Berkovsky, Shlomo</searchLink><br /><searchLink fieldCode="AR" term="%22Arendt%2C+Kristian%22">Arendt, Kristian</searchLink><br /><searchLink fieldCode="AR" term="%22Bögels%2C+Susan%22">Bögels, Susan</searchLink><br /><searchLink fieldCode="AR" term="%22Coleman%2C+Jonathan+R%2E+I%2E%22">Coleman, Jonathan R. I.</searchLink><br /><searchLink fieldCode="AR" term="%22Cooper%2C+Peter%22">Cooper, Peter</searchLink><br /><searchLink fieldCode="AR" term="%22Creswell%2C+Cathy%22">Creswell, Cathy</searchLink><br /><searchLink fieldCode="AR" term="%22Eley%2C+Thalia+C%2E%22">Eley, Thalia C.</searchLink><br /><searchLink fieldCode="AR" term="%22Hartman%2C+Catharina%22">Hartman, Catharina</searchLink><br /><searchLink fieldCode="AR" term="%22Fjermestadt%2C+Krister%22">Fjermestadt, Krister</searchLink><br /><searchLink fieldCode="AR" term="%22In-Albon%2C+Tina%22">In-Albon, Tina</searchLink><br /><searchLink fieldCode="AR" term="%22Lavallee%2C+Kristen%22">Lavallee, Kristen</searchLink><br /><searchLink fieldCode="AR" term="%22Lester%2C+Kathryn+J%2E%22">Lester, Kathryn J.</searchLink><br /><searchLink fieldCode="AR" term="%22Lyneham%2C+Heidi+J%2E%22">Lyneham, Heidi J.</searchLink><br /><searchLink fieldCode="AR" term="%22Marin%2C+Carla+E%2E%22">Marin, Carla E.</searchLink><br /><searchLink fieldCode="AR" term="%22McKinnon%2C+Anna%22">McKinnon, Anna</searchLink><br /><searchLink fieldCode="AR" term="%22McLellan%2C+Lauren+F%2E%22">McLellan, Lauren F.</searchLink><br /><searchLink fieldCode="AR" term="%22Meiser-Stedman%2C+Richard%22">Meiser-Stedman, Richard</searchLink><br /><searchLink fieldCode="AR" term="%22Nauta%2C+Maaike%22">Nauta, Maaike</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Psychological+Medicine%22">Psychological Medicine</searchLink>. Dec2024, Vol. 54 Issue 16, p4612-4622. 11p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Anxiety+disorders+treatment%22">Anxiety disorders treatment</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+remission%22">Disease remission</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Anxiety+disorders%22">Anxiety disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+therapy%22">Cognitive therapy</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Comorbidity%22">Comorbidity</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+depression%22">Mental depression</searchLink><br /><searchLink fieldCode="DE" term="%22Children%22">Children</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Psychological Medicine is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=182451611
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1017/S0033291724002654
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 4612
    Subjects:
      – SubjectFull: Anxiety disorders treatment
        Type: general
      – SubjectFull: Risk assessment
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Research funding
        Type: general
      – SubjectFull: Disease remission
        Type: general
      – SubjectFull: Descriptive statistics
        Type: general
      – SubjectFull: Anxiety disorders
        Type: general
      – SubjectFull: Cognitive therapy
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Comorbidity
        Type: general
      – SubjectFull: Mental depression
        Type: general
      – SubjectFull: Children
        Type: general
    Titles:
      – TitleFull: Predicting remission following CBT for childhood anxiety disorders: a machine learning approach.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Bertie, Lizel-Antoinette
      – PersonEntity:
          Name:
            NameFull: Quiroz, Juan C.
      – PersonEntity:
          Name:
            NameFull: Berkovsky, Shlomo
      – PersonEntity:
          Name:
            NameFull: Arendt, Kristian
      – PersonEntity:
          Name:
            NameFull: Bögels, Susan
      – PersonEntity:
          Name:
            NameFull: Coleman, Jonathan R. I.
      – PersonEntity:
          Name:
            NameFull: Cooper, Peter
      – PersonEntity:
          Name:
            NameFull: Creswell, Cathy
      – PersonEntity:
          Name:
            NameFull: Eley, Thalia C.
      – PersonEntity:
          Name:
            NameFull: Hartman, Catharina
      – PersonEntity:
          Name:
            NameFull: Fjermestadt, Krister
      – PersonEntity:
          Name:
            NameFull: In-Albon, Tina
      – PersonEntity:
          Name:
            NameFull: Lavallee, Kristen
      – PersonEntity:
          Name:
            NameFull: Lester, Kathryn J.
      – PersonEntity:
          Name:
            NameFull: Lyneham, Heidi J.
      – PersonEntity:
          Name:
            NameFull: Marin, Carla E.
      – PersonEntity:
          Name:
            NameFull: McKinnon, Anna
      – PersonEntity:
          Name:
            NameFull: McLellan, Lauren F.
      – PersonEntity:
          Name:
            NameFull: Meiser-Stedman, Richard
      – PersonEntity:
          Name:
            NameFull: Nauta, Maaike
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 12
              Text: Dec2024
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 00332917
          Numbering:
            – Type: volume
              Value: 54
            – Type: issue
              Value: 16
          Titles:
            – TitleFull: Psychological Medicine
              Type: main
ResultId 1