Hannah N Ziobrowski1, Ruifeng Cui2,3, Eric L Ross4,5,6, Howard Liu1,7, Victor Puac-Polanco1, Brett Turner1,8, Lucinda B Leung9,10, Robert M Bossarte7,11, Corey Bryant12, Wilfred R Pigeon7,13, David W Oslin14,15, Edward P Post12,16, Alan M Zaslavsky1, Jose R Zubizarreta1,17,18, Andrew A Nierenberg6,19, Alex Luedtke20,21, Chris J Kennedy22, Ronald C Kessler1. 1. Department of Health Care Policy, Harvard Medical School, Boston, MA, USA. 2. Department of Veterans Affairs, VISN 4 Mental Illness Research, Education and Clinical Center, VA Pittsburgh Health Care System, Pittsburgh, PA, USA. 3. Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. 4. Department of Psychiatry, McLean Hospital, Belmont, MA, USA. 5. Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA. 6. Department of Psychiatry, Harvard Medical School, Boston, MA, USA. 7. Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA. 8. Harvard T.H. Chan School of Public Health, Boston, MA, USA. 9. Center for the Study of Healthcare Innovation, Implementation, and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA. 10. Division of General Internal Medicine and Health Services Research, UCLA David Geffen School of Medicine, Los Angeles, CA, USA. 11. Department of Behavioral Medicine and Psychiatry, West Virginia University, Morgantown, WV, USA. 12. Center for Clinical Management Research, VA, Ann Arbor, MI, USA. 13. Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, USA. 14. VISN 4 Mental Illness Research, Education, and Clinical Center, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA. 15. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. 16. Department of Medicine, University of Michigan Medical School, Ann Arbor, MI, USA. 17. Department of Statistics, Harvard University, Cambridge, MA, USA. 18. Department of Biostatistics, Harvard University, Cambridge, MA, USA. 19. Department of Psychiatry, Dauten Family Center for Bipolar Treatment Innovation, Massachusetts General Hospital, Boston, MA, USA. 20. Department of Statistics, University of Washington, Seattle, WA, USA. 21. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. 22. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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.
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.
Entities:
Keywords:
Depression; Veterans Health Administration; machine learning; psychotherapy; treatment response
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