Development of a model to predict combined antidepressant medication and psychotherapy treatment response for depression among veterans

Robert M Bossarte; Eric L Ross; Howard Liu; Brett Turner; Corey Bryant; Nur Hani Zainal; Victor Puac-Polanco; Hannah N Ziobrowski; Ruifeng Cui; Andrea Cipriani; Toshiaki A Furukawa; Lucinda B Leung; Jutta Joormann; Andrew A Nierenberg; David W Oslin; Wilfred R Pigeon; Edward P Post; Alan M Zaslavsky; Jose R Zubizarreta; Alex Luedtke; Chris J Kennedy; Ronald C Kessler

doi:10.1016/j.jad.2023.01.082

Development of a model to predict combined antidepressant medication and psychotherapy treatment response for depression among veterans

J Affect Disord. 2023 Apr 1:326:111-119. doi: 10.1016/j.jad.2023.01.082. Epub 2023 Jan 26.

Authors

Robert M Bossarte¹, Eric L Ross², Howard Liu³, Brett Turner⁴, Corey Bryant⁵, Nur Hani Zainal⁶, Victor Puac-Polanco⁷, Hannah N Ziobrowski⁸, Ruifeng Cui⁹, Andrea Cipriani¹⁰, Toshiaki A Furukawa¹¹, Lucinda B Leung¹², Jutta Joormann¹³, Andrew A Nierenberg¹⁴, David W Oslin¹⁵, Wilfred R Pigeon¹⁶, Edward P Post¹⁷, Alan M Zaslavsky⁶, Jose R Zubizarreta¹⁸, Alex Luedtke¹⁹, Chris J Kennedy²⁰, Ronald C Kessler²¹

Affiliations

¹ Department of Psychiatry and Behavioral Neurosciences, University of South Florida, Tampa, FL, USA; Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA.
² 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.
³ Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA; Department of Health Care Policy, Harvard Medical School, Boston, MA, USA.
⁴ Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA; Department of Health Care Policy, Harvard Medical School, Boston, MA, USA; Harvard T.H. Chan School of Public Health, Boston, MA, USA.
⁵ Center for Clinical Management Research, VA Ann Arbor, Ann Arbor, MI, USA.
⁶ Department of Health Care Policy, Harvard Medical School, Boston, MA, USA.
⁷ Department of Health Policy and Management, SUNY Downstate Health Sciences University, Brooklyn, NY, USA.
⁸ Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA.
⁹ VISN 4 Mental Illness Research, Education, and Clinical Center, VA Pittsburgh Health Care System, Department of Veterans Affairs, Pittsburgh, PA, USA; Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
¹⁰ Department of Psychiatry, University of Oxford, Oxford, UK.
¹¹ Department of Health Promotion and Human Behavior, School of Public Health, Kyoto University Graduate School of Medicine, Kyoto, Japan.
¹² 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.
¹³ Department of Psychology, Yale University, New Haven, CT, USA.
¹⁴ 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.
¹⁵ 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.
¹⁶ Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA; Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, USA.
¹⁷ Center for Clinical Management Research, VA Ann Arbor, Ann Arbor, MI, USA; Department of Medicine, University of Michigan Medical School, Ann Arbor, MI, USA.
¹⁸ 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.
¹⁹ Department of Statistics, University of Washington, Seattle, WA, USA; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
²⁰ Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
²¹ Department of Health Care Policy, Harvard Medical School, Boston, MA, USA. Electronic address: [email protected].

Abstract

Background: Although research shows that more depressed patients respond to combined antidepressants (ADM) and psychotherapy than either alone, many patients do not respond even to combined treatment. A reliable prediction model for this could help treatment decision-making. We attempted to create such a model using machine learning methods among patients in the US Veterans Health Administration (VHA).

Methods: A 2018-2020 national sample of VHA patients beginning combined depression treatment completed self-report assessments at baseline and 3 months (n = 658). A learning model was developed using baseline self-report, administrative, and geospatial data to predict 3-month treatment response defined by reductions in the Quick Inventory of Depression Symptomatology Self-Report and/or in the Sheehan Disability Scale. The model was developed in a 70 % training sample and tested in the remaining 30 % test sample.

Results: 30.0 % of patients responded to treatment. The prediction model had a test sample AUC-ROC of 0.657. A strong gradient was found in probability of treatment response from 52.7 % in the highest predicted quintile to 14.4 % in the lowest predicted quintile. The most important predictors were episode characteristics (symptoms, comorbidities, history), personality/psychological resilience, recent stressors, and treatment characteristics.

Limitations: Restrictions in sample definition, a low recruitment rate, and reliance on patient self-report rather than clinician assessments to determine treatment response limited the generalizability of results.

Conclusions: A machine learning model could help depressed patients and providers predict likely response to combined ADM-psychotherapy. Parallel information about potential harms and costs of alternative treatments would be needed, though, to inform optimal treatment selection.

Keywords: Antidepressant medication; Clinical decision support; Depression; Machine learning; Treatment response; Veterans Health Administration.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Antidepressive Agents / therapeutic use
Combined Modality Therapy
Depression* / drug therapy
Depression* / psychology
Humans
Psychotherapy / methods
Veterans*

Substances

Antidepressive Agents

Abstract

Publication types

MeSH terms

Substances

Grants and funding