Evidence map›Paper›PMID 36709831›Full record

ArticleJournal of affective disorders2023

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 and 12 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of affective disorders, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

22 authors.

Robert M BossarteDepartment 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.
Eric L RossDepartment 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 LiuCenter of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA; Department of Health Care Policy, Harvard Medical School, Boston, MA, USA.
Brett TurnerCenter 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.
Corey BryantCenter for Clinical Management Research, VA Ann Arbor, Ann Arbor, MI, USA.
Nur Hani ZainalDepartment of Health Care Policy, Harvard Medical School, Boston, MA, USA.
Victor Puac-PolancoDepartment of Health Policy and Management, SUNY Downstate Health Sciences University, Brooklyn, NY, USA.
Hannah N ZiobrowskiDepartment of Epidemiology, Brown University School of Public Health, Providence, RI, USA.
Ruifeng CuiVISN 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.
Andrea CiprianiDepartment of Psychiatry, University of Oxford, Oxford, UK.
Toshiaki A FurukawaDepartment of Health Promotion and Human Behavior, School of Public Health, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Lucinda B LeungCenter 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.
Jutta JoormannDepartment of Psychology, Yale University, New Haven, CT, USA.
Andrew A NierenbergDepartment 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 OslinVISN 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 PigeonCenter 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 PostCenter for Clinical Management Research, VA Ann Arbor, Ann Arbor, MI, USA; Department of Medicine, University of Michigan Medical School, Ann Arbor, MI, USA.
Alan M ZaslavskyDepartment of Health Care Policy, Harvard Medical School, Boston, MA, USA.
Jose R ZubizarretaDepartment 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 LuedtkeDepartment of Statistics, University of Washington, Seattle, WA, USA; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Chris J KennedyDepartment of Psychiatry, Massachusetts General Hospital, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
Ronald C KesslerDepartment of Health Care Policy, Harvard Medical School, Boston, MA, USA. Electronic address: kessler@hcp.med.harvard.edu.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Leveraging EHR data to evaluate key treatment decisions to prevent suicide-related behaviorsR01MH121478 · NIMH · HARVARD MEDICAL SCHOOL · PI KESSLER, RONALD C · 2020 to 2023
$3.1M
Improving Depression Management in Primary CareIK2HX002867 · VA · VA GREATER LOS ANGELES HEALTHCARE SYSTEM · PI LEUNG, LUCINDA B · 2020 to 2025
–
HSRD VA IK2 HX002867NCATS NIH HHS UL1 TR001863NIMH NIH HHS R01 MH121478
6 · The paper itself

Abstract

backgroundAlthough 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).

methodsA 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.

results30.0 % 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.

conclusionsA 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.

Indexed as

DepressionVeteransAntidepressive AgentsCombined Modality TherapyHumansPsychotherapyAntidepressive AgentsAntidepressant medicationClinical decision supportDepressionMachine learningTreatment responseVeterans Health Administration

Identifiers

PMID36709831
PMCPMC9975041

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.