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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