Treatment Response Prediction in Major Depressive Disorder Using Multimodal MRI and Clinical Data: Secondary Analysis of a Randomized Clinical Trial
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VIEW CORRECTIONPublication: American Journal of Psychiatry
Abstract
Objective:
Response to antidepressant treatment in major depressive disorder varies substantially between individuals, which lengthens the process of finding effective treatment. The authors sought to determine whether a multimodal machine learning approach could predict early sertraline response in patients with major depressive disorder. They assessed the predictive contribution of MR neuroimaging and clinical assessments at baseline and after 1 week of treatment.
Methods:
This was a preregistered secondary analysis of data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a multisite double-blind, placebo-controlled randomized clinical trial that included 296 adult outpatients with unmedicated recurrent or chronic major depressive disorder. MR neuroimaging and clinical data were collected before and after 1 week of treatment. Performance in predicting response and remission, collected after 8 weeks, was quantified using balanced accuracy (bAcc) and area under the receiver operating characteristic curve (AUROC) scores.
Results:
A total of 229 patients were included in the analyses (mean age, 38 years [SD=13]; 66% female). Internal cross-validation performance in predicting response to sertraline (bAcc=68% [SD=10], AUROC=0.73 [SD=0.03]) was significantly better than chance. External cross-validation on data from placebo nonresponders (bAcc=62%, AUROC=0.66) and placebo nonresponders who were switched to sertraline (bAcc=65%, AUROC=0.68) resulted in differences that suggest specificity for sertraline treatment compared with placebo treatment. Finally, multimodal models outperformed unimodal models.
Conclusions:
The study results confirm that early sertraline treatment response can be predicted; that the models are sertraline specific compared with placebo; that prediction benefits from integrating multimodal MRI data with clinical data; and that perfusion imaging contributes most to these predictions. Using this approach, a lean and effective protocol could individualize sertraline treatment planning to improve psychiatric care.
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History
Received: 15 March 2023
Revision received: 21 July 2023
Revision received: 14 September 2023
Accepted: 12 October 2023
Published online: 7 February 2024
Published in print: March 01, 2024
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Author Contributions
Drs. Reneman and Caan contributed equally as senior authors.
Funding Information
Supported by the Eurostars funding program (reference number 113351). Dr. Mutsaerts is supported by the Dutch Heart Foundation (03-004-2020-T049), by the Eurostars-2 joint program with co-funding from the European Union Horizon 2020 research and innovation program (ASPIRE E!113701), provided by the Netherlands Enterprise Agency (RvO), and by the EU Joint Program for Neurodegenerative Disease Research, provided by the Netherlands Organization for Health Research and Development and Alzheimer Nederland (DEBBIE JPND2020-568-106). The EMBARC study was supported by NIMH under grants U01MH092221 and U01MH092250. The EMBARC study was also supported in part by Valeant Pharmaceuticals, which donated extended-release bupropion, used in the second phase of the EMBARC study (not reported here).Dr. Ruhe has received research grants from the Dutch Ministry of Health, EU Horizon 2020, HersenStichting, and ZonMW and an unrestricted educational grant from Janssen, and he has received speaking honoraria from Benecke, Janssen, Lundbeck, Prelum, and Wyeth. Dr. Marquering is a co-founder of and shareholder in InSteps, Nicolab, and TrianecT. Dr. Caan is a shareholder in Nicolab. The other authors report no financial relationships with commercial interests.
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