Evidence map›Paper›PMID 38486050›Full record

ArticleMolecular psychiatry2024

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

Nur Hani Zainal, Robert M Bossarte, Sarah M Gildea, Irving Hwang, Chris J Kennedy, Howard Liu, Lucinda B Leung, Alex Luedtke, Brian P Marx, Maria V Petukhova and 7 more

Erratum issuedOpen access · greenAbstract read
In one paragraph

Article in Molecular psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
3.9field-weighted citation impact, top 7% of its field
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, 6 citations in OpenAlex.

  1. Article
  2. Innovations to improve outcomes and uptake of psychotherapies for mental disorders: a state-of-the-art review.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026
    Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors at 7 institutions in 1 country.

Nur Hani ZainalDepartment of Health Care Policy, Harvard Medical School, Boston, MA, USA.
Robert M BossarteDepartment of Psychiatry and Behavioral Neurosciences, University of South Florida, Tampa, FL, USA.
Sarah M GildeaDepartment of Health Care Policy, Harvard Medical School, Boston, MA, USA.
Irving HwangDepartment of Health Care Policy, Harvard Medical School, Boston, MA, USA.
Chris J KennedyDepartment of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.ORCID 0000-0001-7444-2766
Howard LiuDepartment of Health Care Policy, Harvard Medical School, Boston, MA, USA.
Lucinda B LeungCenter for the Study of Healthcare Innovation, Implementation, and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA.
Alex LuedtkeDepartment of Statistics, University of Washington, Seattle, WA, USA.
Brian P MarxNational Center for PTSD, VA Boston Healthcare System, Boston, MA, USA.
Maria V PetukhovaDepartment of Health Care Policy, Harvard Medical School, Boston, MA, USA.
Edward P PostCenter for Clinical Management Research, VA Ann Arbor Health Care System, Ann Arbor, MI, USA.
Eric L RossDepartment of Psychiatry, Larner College of Medicine, University of Vermont, Burlington, VT, USA.
Nancy A SampsonDepartment of Health Care Policy, Harvard Medical School, Boston, MA, USA.
Erik SverdrupGraduate School of Business, Stanford University, Stanford, CA, USA.
Brett TurnerHarvard T.H. Chan School of Public Health, Boston, MA, USA.
Stefan WagerGraduate School of Business, Stanford University, Stanford, CA, USA.
Ronald C KesslerDepartment of Health Care Policy, Harvard Medical School, Boston, MA, USA. kessler@hcp.med.harvard.edu.ORCID 0000-0003-4831-2305
Harvard University · USUniversity of Vermont · USBoston University · USMassachusetts General Hospital · USUniversity of California, Los Angeles · USUniversity of South Florida · USUniversity of Washington · US

Funding

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
HSRD VA IK2 HX002867NIMH NIH HHS R01 MH121478U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01MH121478
6 · The paper itself

Abstract

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.

Indexed as

Antidepressive AgentsElectronic Health RecordsMajor Depressive DisorderPrecision MedicinePsychotherapyVeteransAdultAgedFemaleHumansMaleMiddle AgedSuicide, AttemptedTreatment OutcomeUnited StatesUnited States Department of Veterans AffairsAntidepressive Agents

Identifiers

PMID38486050
PMCPMC11399319
OpenAlexW4392809179

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.