• AI to predict antidepressant outcomes in

    From ScienceDaily@1:317/3 to All on Wednesday, March 16, 2022 22:30:42
    AI to predict antidepressant outcomes in youth

    Date:
    March 16, 2022
    Source:
    Mayo Clinic
    Summary:
    Researchers have taken the first step in using artificial
    intelligence (AI) to predict early outcomes with antidepressants
    in children and adolescents with major depressive disorder.



    FULL STORY ==========================================================================
    Mayo Clinic researchers have taken the first step in using artificial intelligence (AI) to predict early outcomes with antidepressants in
    children and adolescents with major depressive disorder, in a study
    published in The Journal of Child Psychology and Psychiatry. This
    work resulted from a collaborative effort between the departments of
    Molecular Pharmacology and Experimental Therapeutics, and Psychiatry
    and Psychology, at Mayo Clinic, with support from Mayo Clinic's Center
    for Individualized Medicine.


    ========================================================================== "This preliminary work suggests that AI has promise for assisting clinical decisions by informing physicians on the selection, use and dosing of antidepressants for children and adolescents with major depressive
    disorder," says Paul Croarkin, D.O., a Mayo Clinic psychiatrist and
    senior author of the study. "We saw improved predictions of treatment
    outcomes in samples of children and adolescents across two classes
    of antidepressants." In the study, researchers identified variation
    in six depressive symptoms: difficulty having fun, social withdrawal,
    excessive fatigue, irritability, low self-esteem and depressed feelings.

    They assessed these symptoms with the Children's Depression Rating
    Scale- Revised to predict outcomes to 10 to 12 weeks of antidepressant pharmacotherapy:
    * The six symptoms predicted 10- to 12-week outcomes at four to
    six weeks
    in fluoxetine testing datasets, with an average accuracy of 73%.

    * The same six symptoms predicted 10- to 12-week outcomes at four
    to six
    weeks in duloxetine testing datasets, with an average accuracy
    of 76%.

    * In placebo-treated patients, predicting response and remission
    accuracy
    was significantly lower than for antidepressants at 67%.

    These outcomes show the potential of AI and patient data to ensure
    children and adolescents receive treatment that has the highest likelihood
    of delivering therapeutic benefits with minimized side effects, explains
    Arjun Athreya, Ph.D., a Mayo Clinic researcher and lead author of
    the study.

    "We designed the algorithm to mimic a clinician's logic of treatment
    management at an interim time point based on their estimated guess of
    whether a patient will likely or not benefit from pharmacotherapy at
    the current dose," says Dr.

    Athreya. "Hence, it was essential for me as a computer engineer to
    embed and observe the practice closely to not only understand the
    needs of the patient, but also how AI can be consumed and useful to the clinician to benefit the patient." Next steps The research findings are
    a foundation for future work incorporating physiological information, brain-based measures and pharmacogenomic data for precision medicine
    approaches in treating youth with depression. This will improve the care
    of young patients with depression, and help clinicians initiate and dose antidepressants in patients who benefit most.

    "Technological advances are understudied tools that could enhance
    treatment approaches," says Liewei Wang, M.D., Ph.D., the Bernard and
    Edith Waterman Director of the Pharmacogenomics Program and Director of
    the Center for Individualized Medicine at the Mayo Clinic. "Predicting
    outcomes in children and adolescents treated for depression is
    critical in managing what could become a lifelong disease burden." Acknowledgments This work was supported by Mayo Clinic Foundation
    for Medical Education and Research; the National Science Foundation
    under award No. 2041339; and the National Institute of Mental Health
    under awards R01MH113700, R01MH124655 and R01AA027486. The content is
    solely the authors' responsibility and does not necessarily represent
    the official views of the funding agencies. The authors have declared
    no competing or potential conflicts of interest.


    ========================================================================== Story Source: Materials provided by Mayo_Clinic. Original written by
    Colette Gallagher. Note: Content may be edited for style and length.


    ========================================================================== Journal Reference:
    1. Arjun P. Athreya, Jennifer L. Vande Voort, Julia Shekunov, Sandra J.

    Rackley, Jarrod M. Leffler, Alastair J. McKean, Magdalena
    Romanowicz, Betsy D. Kennard, Graham J. Emslie, Taryn Mayes,
    Madhukar Trivedi, Liewei Wang, Richard M. Weinshilboum, William
    V. Bobo, Paul E. Croarkin.

    Evidence for machine learning guided early prediction of acute
    outcomes in the treatment of depressed children and adolescents
    with antidepressants. Journal of Child Psychology and Psychiatry,
    2022; DOI: 10.1111/jcpp.13580 ==========================================================================

    Link to news story: https://www.sciencedaily.com/releases/2022/03/220316115013.htm

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