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Development of a model to predict psychotherapy response for depression among Veterans

Published online by Cambridge University Press:  11 February 2022

Hannah N. Ziobrowski ,
Ruifeng Cui ,
Eric L. Ross ,
Howard Liu ,
Victor Puac-Polanco ,
Brett Turner ,
Lucinda B. Leung ,
Robert M. Bossarte ,
Corey Bryant  and
Wilfred R. Pigeon
...Show all authors
Hannah N. Ziobrowski
Affiliation:
Department of Health Care Policy, Harvard Medical School, 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
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
Victor Puac-Polanco
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
Brett Turner
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA Harvard T.H. Chan School of Public Health, Boston, MA, 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, MI, 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
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
Edward P. Post
Affiliation:
Center for Clinical Management Research, VA, Ann Arbor, MI, USA Department of Medicine, University of Michigan Medical School, Ann Arbor, MI, 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
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
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 Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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
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Abstract

Background

Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.

Methods

This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.

Results

32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.

Conclusions

Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Keywords

Depressionmachine learningpsychotherapytreatment responseVeterans Health Administration
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 8 , June 2023 , pp. 3591 - 3600
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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