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An individualized treatment rule to optimize probability of remission by continuation, switching, or combining antidepressant medications after failing a first-line antidepressant in a two-stage randomized trial

Published online by Cambridge University Press:  08 March 2021

Ronald C. Kessler Open the ORCID record for Ronald C. Kessler [Opens in a new window] ,
Toshi A. Furukawa ,
Tadashi Kato ,
Alex Luedtke ,
Maria Petukhova ,
Ekaterina Sadikova  and
Nancy A. Sampson
Ronald C. Kessler*
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA
Toshi A. Furukawa
Affiliation:
Department of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan
Tadashi Kato
Affiliation:
Aratama Kokorono Clinic, Nagoya, Japan
Alex Luedtke
Affiliation:
Department of Statistics, University of Washington, Seattle, Washington, USA Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
Maria Petukhova
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA
Ekaterina Sadikova
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
Nancy A. Sampson
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA
*
Author for correspondence: Ronald C. Kessler, E-mail: Kessler@hcp.med.harvard.edu
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Abstract

Background

There is growing interest in using composite individualized treatment rules (ITRs) to guide depression treatment selection, but best approaches for doing this are not widely known. We develop an ITR for depression remission based on secondary analysis of a recently published trial for second-line antidepression medication selection using a cutting-edge ensemble machine learning method.

Methods

Data come from the SUN(^_^)D trial, an open-label, assessor blinded pragmatic trial of previously-untreated patients with major depressive disorder from 48 clinics in Japan. Initial clinic-level randomization assigned patients to 50 or 100 mg/day sertraline. We focus on the 1549 patients who failed to remit within 3 weeks and were then rerandomized at the individual-level to continuation with sertraline, switching to mirtazapine, or combining mirtazapine with sertraline. The outcome was remission 9 weeks post-baseline. Predictors included socio-demographics, clinical characteristics, baseline symptoms, changes in symptoms between baseline and week 3, and week 3 side effects.

Results

Optimized treatment was associated with significantly increased cross-validated week 9 remission rates in both samples [5.3% (2.4%), p = 0.016 50 mg/day sample; 5.1% (2.7%), p = 0.031 100 mg/day sample] compared to randomization (30.1–30.8%). Optimization was also associated with significantly increased remission in both samples compared to continuation [24.7% in both: 11.2% (3.8%), p = 0.002 50 mg/day sample; 11.7% (3.9%), p = 0.001 100 mg/day sample]. Non-significant gains were found for optimization compared to switching or combining.

Conclusions

An ITR can be developed to improve second-line antidepressant selection, but replication in a larger study with more comprehensive baseline predictors might produce stronger and more stable results.

Keywords

Ensemble machine learningindividualized treatmentmajor depressive episodeprecision treatmentSMART trialSUN(^_^)D

Information

Type
Original Article
Information
Psychological Medicine , Volume 52 , Issue 15 , November 2022 , pp. 3371 - 3380
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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Footnotes

*

Dr Kessler and Dr Furukawa contributed equally to this manuscript as co-first authors.

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