View in EDS HTML Full Text PDF Full Text

Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants.

Saved in:
Bibliographic Details
Title: Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants.
Authors: Athreya, Arjun P., Vande Voort, Jennifer L., Shekunov, Julia, Rackley, Sandra J., Leffler, Jarrod M., McKean, Alastair J., Romanowicz, Magdalena, Kennard, Betsy D., Emslie, Graham J., Mayes, Taryn, Trivedi, Madhukar, Wang, Liewei, Weinshilboum, Richard M., Bobo, William V., Croarkin, Paul E.
Source: Journal of Child Psychology & Psychiatry. Nov2022, Vol. 63 Issue 11, p1347-1358. 12p. 2 Diagrams, 1 Chart, 1 Graph.
Subjects: Antidepressants, Fluoxetine, Duloxetine, Machine learning, Treatment effectiveness, Mental depression, Statistical models, Evaluation, Children, Adolescence
Abstract: Background: The treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo. Methods: The study samples included training datasets (N = 271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N = 255) or placebo (N = 265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment. Results: Variation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self‐esteem, and depressed feelings) assessed with the Children's Depression Rating Scale‐Revised at 4–6 weeks predicted treatment outcomes with fluoxetine at 10–12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10–12 week outcomes at 4–6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo‐treated patients with accuracies of 67%. In placebo‐treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants. Conclusions: PGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Child Psychology & Psychiatry is the property of Wiley-Blackwell 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
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: pbh
DbLabel: Psychology and Behavioral Sciences Collection
An: 159814196
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Athreya%2C+Arjun+P%2E%22">Athreya, Arjun P.</searchLink><br /><searchLink fieldCode="AR" term="%22Vande+Voort%2C+Jennifer+L%2E%22">Vande Voort, Jennifer L.</searchLink><br /><searchLink fieldCode="AR" term="%22Shekunov%2C+Julia%22">Shekunov, Julia</searchLink><br /><searchLink fieldCode="AR" term="%22Rackley%2C+Sandra+J%2E%22">Rackley, Sandra J.</searchLink><br /><searchLink fieldCode="AR" term="%22Leffler%2C+Jarrod+M%2E%22">Leffler, Jarrod M.</searchLink><br /><searchLink fieldCode="AR" term="%22McKean%2C+Alastair+J%2E%22">McKean, Alastair J.</searchLink><br /><searchLink fieldCode="AR" term="%22Romanowicz%2C+Magdalena%22">Romanowicz, Magdalena</searchLink><br /><searchLink fieldCode="AR" term="%22Kennard%2C+Betsy+D%2E%22">Kennard, Betsy D.</searchLink><br /><searchLink fieldCode="AR" term="%22Emslie%2C+Graham+J%2E%22">Emslie, Graham J.</searchLink><br /><searchLink fieldCode="AR" term="%22Mayes%2C+Taryn%22">Mayes, Taryn</searchLink><br /><searchLink fieldCode="AR" term="%22Trivedi%2C+Madhukar%22">Trivedi, Madhukar</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Liewei%22">Wang, Liewei</searchLink><br /><searchLink fieldCode="AR" term="%22Weinshilboum%2C+Richard+M%2E%22">Weinshilboum, Richard M.</searchLink><br /><searchLink fieldCode="AR" term="%22Bobo%2C+William+V%2E%22">Bobo, William V.</searchLink><br /><searchLink fieldCode="AR" term="%22Croarkin%2C+Paul+E%2E%22">Croarkin, Paul E.</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal+of+Child+Psychology+%26+Psychiatry%22">Journal of Child Psychology & Psychiatry</searchLink>. Nov2022, Vol. 63 Issue 11, p1347-1358. 12p. 2 Diagrams, 1 Chart, 1 Graph.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Antidepressants%22">Antidepressants</searchLink><br /><searchLink fieldCode="DE" term="%22Fluoxetine%22">Fluoxetine</searchLink><br /><searchLink fieldCode="DE" term="%22Duloxetine%22">Duloxetine</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Treatment+effectiveness%22">Treatment effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+depression%22">Mental depression</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation%22">Evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Children%22">Children</searchLink><br /><searchLink fieldCode="DE" term="%22Adolescence%22">Adolescence</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: The treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo. Methods: The study samples included training datasets (N = 271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N = 255) or placebo (N = 265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment. Results: Variation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self‐esteem, and depressed feelings) assessed with the Children's Depression Rating Scale‐Revised at 4–6 weeks predicted treatment outcomes with fluoxetine at 10–12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10–12 week outcomes at 4–6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo‐treated patients with accuracies of 67%. In placebo‐treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants. Conclusions: PGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Child Psychology & Psychiatry is the property of Wiley-Blackwell 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=159814196
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/jcpp.13580
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 1347
    Subjects:
      – SubjectFull: Antidepressants
        Type: general
      – SubjectFull: Fluoxetine
        Type: general
      – SubjectFull: Duloxetine
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Treatment effectiveness
        Type: general
      – SubjectFull: Mental depression
        Type: general
      – SubjectFull: Statistical models
        Type: general
      – SubjectFull: Evaluation
        Type: general
      – SubjectFull: Children
        Type: general
      – SubjectFull: Adolescence
        Type: general
    Titles:
      – TitleFull: Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Athreya, Arjun P.
      – PersonEntity:
          Name:
            NameFull: Vande Voort, Jennifer L.
      – PersonEntity:
          Name:
            NameFull: Shekunov, Julia
      – PersonEntity:
          Name:
            NameFull: Rackley, Sandra J.
      – PersonEntity:
          Name:
            NameFull: Leffler, Jarrod M.
      – PersonEntity:
          Name:
            NameFull: McKean, Alastair J.
      – PersonEntity:
          Name:
            NameFull: Romanowicz, Magdalena
      – PersonEntity:
          Name:
            NameFull: Kennard, Betsy D.
      – PersonEntity:
          Name:
            NameFull: Emslie, Graham J.
      – PersonEntity:
          Name:
            NameFull: Mayes, Taryn
      – PersonEntity:
          Name:
            NameFull: Trivedi, Madhukar
      – PersonEntity:
          Name:
            NameFull: Wang, Liewei
      – PersonEntity:
          Name:
            NameFull: Weinshilboum, Richard M.
      – PersonEntity:
          Name:
            NameFull: Bobo, William V.
      – PersonEntity:
          Name:
            NameFull: Croarkin, Paul E.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 11
              Text: Nov2022
              Type: published
              Y: 2022
          Identifiers:
            – Type: issn-print
              Value: 00219630
          Numbering:
            – Type: volume
              Value: 63
            – Type: issue
              Value: 11
          Titles:
            – TitleFull: Journal of Child Psychology & Psychiatry
              Type: main
ResultId 1