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Development of a model to predict antidepressant treatment response for depression among Veterans

Published online by Cambridge University Press:  15 July 2022

Victor Puac-Polanco
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
Hannah N. Ziobrowski
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
Eric L. Ross
Affiliation:
Department of Psychiatry, McLean Hospital, Belmont, MA, USA Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA
Howard Liu
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
Brett Turner
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA Harvard T.H. Chan School of Public Health, Boston, MA, USA
Ruifeng Cui
Affiliation:
Department of Veterans Affairs, VISN 4 Mental Illness Research, Education and Clinical Center, VA Pittsburgh Health Care System, Pittsburgh, PA, USA Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
Lucinda B. Leung
Affiliation:
Center for the Study of Healthcare Innovation, Implementation, and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA Division of General Internal Medicine and Health Services Research, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
Robert M. Bossarte
Affiliation:
Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA Department of Behavioral Medicine and Psychiatry, West Virginia University, Morgantown, WV, USA
Corey Bryant
Affiliation:
Center for Clinical Management Research, VA Ann Arbor, Ann Arbor, MI, USA
Jutta Joormann
Affiliation:
Department of Psychology, Yale University, New Haven, CT, USA
Andrew A. Nierenberg
Affiliation:
Department of Psychiatry, Harvard Medical School, Boston, MA, USA Department of Psychiatry, Dauten Family Center for Bipolar Treatment Innovation, Massachusetts General Hospital, Boston, MA, USA
David W. Oslin
Affiliation:
VISN 4 Mental Illness Research, Education, and Clinical Center, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Wilfred R. Pigeon
Affiliation:
Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, USA
Edward P. Post
Affiliation:
Center for Clinical Management Research, VA Ann Arbor, Ann Arbor, MI, USA Department of Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
Nur Hani Zainal
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
Alan M. Zaslavsky
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
Jose R. Zubizarreta
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA Department of Statistics, Harvard University, Cambridge, MA, USA Department of Biostatistics, Harvard University, Cambridge, MA, USA
Alex Luedtke
Affiliation:
Department of Statistics, University of Washington, Seattle, WA, USA Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
Chris J. Kennedy
Affiliation:
Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA
Andrea Cipriani
Affiliation:
Department of Psychiatry, University of Oxford, Oxford, UK
Toshiaki A. Furukawa
Affiliation:
Department of Health Promotion and Human Behavior, School of Public Health, Kyoto University Graduate School of Medicine, Kyoto, Japan
Ronald C. Kessler*
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
*
Author for correspondence: Ronald C. Kessler, E-mail: kessler@hcp.med.harvard.edu

Abstract

Background

Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).

Methods

A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.

Results

In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.

Conclusions

Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Type
Original Article
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press.

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