Prediction of individual patient outcomes to psychotherapy vs medication for major depression
Abstract Treatments for major depressive disorder (MDD) include antidepressant medications and evidence-based psychotherapies, which are approximately equally efficacious. Individual response to treatment, however, is variable, implying individual differences that could allow for prospective differe...
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Nature Portfolio
2025-02-01
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Series: | npj Mental Health Research |
Online Access: | https://doi.org/10.1038/s44184-025-00119-9 |
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author | Devon LoParo Boadie W. Dunlop Charles B. Nemeroff Helen S. Mayberg W. Edward Craighead |
author_facet | Devon LoParo Boadie W. Dunlop Charles B. Nemeroff Helen S. Mayberg W. Edward Craighead |
author_sort | Devon LoParo |
collection | DOAJ |
description | Abstract Treatments for major depressive disorder (MDD) include antidepressant medications and evidence-based psychotherapies, which are approximately equally efficacious. Individual response to treatment, however, is variable, implying individual differences that could allow for prospective differential prediction of treatment response and personalized treatment recommendation. We used machine learning to develop predictor variables that combined demographic and clinical items from a randomized clinical trial. The variables predicted a meaningful proportion of variance in end-of-treatment depression severity for cognitive behavioral therapy (39.7%), escitalopram (32.1%), and duloxetine (67.7%), leading to a high accuracy in predicting remission (71%). Further, we used these variables to simulate treatment recommendation and found that patients who received their recommended treatment had significantly improved depression severity and remission likelihood. Finally, the prediction algorithms and treatment recommendation tool were externally validated in an independent sample. These results represent a highly promising, easily implemented, potential advance for personalized medicine in MDD treatment. |
format | Article |
id | doaj-art-8aea84c3efb54b99a0be3da3a3df1a3e |
institution | Kabale University |
issn | 2731-4251 |
language | English |
publishDate | 2025-02-01 |
publisher | Nature Portfolio |
record_format | Article |
series | npj Mental Health Research |
spelling | doaj-art-8aea84c3efb54b99a0be3da3a3df1a3e2025-02-09T13:00:19ZengNature Portfolionpj Mental Health Research2731-42512025-02-014111010.1038/s44184-025-00119-9Prediction of individual patient outcomes to psychotherapy vs medication for major depressionDevon LoParo0Boadie W. Dunlop1Charles B. Nemeroff2Helen S. Mayberg3W. Edward Craighead4Department of Psychiatry and Behavioral Sciences, Emory University School of MedicineDepartment of Psychiatry and Behavioral Sciences, Emory University School of MedicineDepartment of Psychiatry, University of Texas at AustinThe Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount SinaiDepartment of Psychiatry and Behavioral Sciences, Emory University School of MedicineAbstract Treatments for major depressive disorder (MDD) include antidepressant medications and evidence-based psychotherapies, which are approximately equally efficacious. Individual response to treatment, however, is variable, implying individual differences that could allow for prospective differential prediction of treatment response and personalized treatment recommendation. We used machine learning to develop predictor variables that combined demographic and clinical items from a randomized clinical trial. The variables predicted a meaningful proportion of variance in end-of-treatment depression severity for cognitive behavioral therapy (39.7%), escitalopram (32.1%), and duloxetine (67.7%), leading to a high accuracy in predicting remission (71%). Further, we used these variables to simulate treatment recommendation and found that patients who received their recommended treatment had significantly improved depression severity and remission likelihood. Finally, the prediction algorithms and treatment recommendation tool were externally validated in an independent sample. These results represent a highly promising, easily implemented, potential advance for personalized medicine in MDD treatment.https://doi.org/10.1038/s44184-025-00119-9 |
spellingShingle | Devon LoParo Boadie W. Dunlop Charles B. Nemeroff Helen S. Mayberg W. Edward Craighead Prediction of individual patient outcomes to psychotherapy vs medication for major depression npj Mental Health Research |
title | Prediction of individual patient outcomes to psychotherapy vs medication for major depression |
title_full | Prediction of individual patient outcomes to psychotherapy vs medication for major depression |
title_fullStr | Prediction of individual patient outcomes to psychotherapy vs medication for major depression |
title_full_unstemmed | Prediction of individual patient outcomes to psychotherapy vs medication for major depression |
title_short | Prediction of individual patient outcomes to psychotherapy vs medication for major depression |
title_sort | prediction of individual patient outcomes to psychotherapy vs medication for major depression |
url | https://doi.org/10.1038/s44184-025-00119-9 |
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