AI to predict antidepressant outcomes in youth

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

“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.

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AI to predict antidepressant outcomes in youth

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