Developing an individualized treatment rule for Veterans with major depressive disorder using electronic health records.

Journal: Molecular psychiatry

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Affiliated Institutions:  Department of Health Care Policy, Harvard Medical School, Boston, MA, USA. Department of Psychiatry and Behavioral Neurosciences, University of South Florida, Tampa, FL, USA. Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA. Department of Statistics, University of Washington, Seattle, WA, USA. National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA. Center for Clinical Management Research, VA Ann Arbor Health Care System, Ann Arbor, MI, USA. Department of Psychiatry, Larner College of Medicine, University of Vermont, Burlington, VT, USA. Graduate School of Business, Stanford University, Stanford, CA, USA. Harvard T.H. Chan School of Public Health, Boston, MA, USA. Department of Health Care Policy, Harvard Medical School, Boston, MA, USA. kessler@hcp.med.harvard.edu.

Abstract summary 

Efforts to develop an individualized treatment rule (ITR) to optimize major depressive disorder (MDD) treatment with antidepressant medication (ADM), psychotherapy, or combined ADM-psychotherapy have been hampered by small samples, small predictor sets, and suboptimal analysis methods. Analyses of large administrative databases designed to approximate experiments followed iteratively by pragmatic trials hold promise for resolving these problems. The current report presents a proof-of-concept study using electronic health records (EHR) of n = 43,470 outpatients beginning MDD treatment in Veterans Health Administration Primary Care Mental Health Integration (PC-MHI) clinics, which offer access not only to ADMs but also psychotherapy and combined ADM-psychotherapy. EHR and geospatial databases were used to generate an extensive baseline predictor set (5,865 variables). The outcome was a composite measure of at least one serious negative event (suicide attempt, psychiatric emergency department visit, psychiatric hospitalization, suicide death) over the next 12 months. Best-practices methods were used to adjust for nonrandom treatment assignment and to estimate a preliminary ITR in a 70% training sample and to evaluate the ITR in the 30% test sample. Statistically significant aggregate variation was found in overall probability of the outcome related to baseline predictors (AU-ROC = 0.68, S.E. = 0.01), with test sample outcome prevalence of 32.6% among the 5% of patients having highest predicted risk compared to 7.1% in the remainder of the test sample. The ITR found that psychotherapy-only was the optimal treatment for 56.0% of patients (roughly 20% lower risk of the outcome than if receiving one of the other treatments) and that treatment type was unrelated to outcome risk among other patients. Change in aggregate treatment costs of implementing this ITR would be negligible, as 16.1% fewer patients would be prescribed ADMs and 2.9% more would receive psychotherapy. A pragmatic trial would be needed to confirm the accuracy of the ITR.

Authors & Co-authors:  Zainal Bossarte Gildea Hwang Kennedy Liu Luedtke Marx Petukhova Post Ross Sampson Sverdrup Turner Wager Kessler

Study Outcome 

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Statistics
Citations :  GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396:P1204–22.
Authors :  16
Identifiers
Doi : 10.1038/s41380-024-02500-0
SSN : 1476-5578
Study Population
Male,Female
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Publication Country
England