Evidence map›Paper›PMID 37851457›Full record

ArticleJAMA psychiatry2024

Estimated Average Treatment Effect of Psychiatric Hospitalization in Patients With Suicidal Behaviors: A Precision Treatment Analysis.

Eric L Ross, Robert M Bossarte, Steven K Dobscha, Sarah M Gildea, Irving Hwang, Chris J Kennedy, Howard Liu, Alex Luedtke, Brian P Marx, Matthew K Nock and 6 more

Open access · greenAbstract read
In one paragraph

Article in JAMA psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
9.1field-weighted citation impact, top 1% 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

22 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. From emulation to implementation: next steps for big data research in psychiatry.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026
    Article
  4. Using smartphone surveys to predict next-week suicide attempts.Journal of psychopathology and clinical science · 2026
    Article
  5. Big data and psychiatry: advances, constraints and future directions.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026
    Article
  6. Article
  7. Article
  8. Observational
  9. Suicide Risk Following Psychiatric Hospitalization: A Narrative Review and Conceptual Synthesis.International journal of environmental research and public health · 2026
    Review
  10. Article
  11. Article
  12. Review
  13. Review
  14. Article
  15. Review
  16. Let's Move Towards Precision Suicidology.Current psychiatry reports · 2025
    Review
  17. Potential Harms of Responding to Youth Suicide Risk in Schools.Research on child and adolescent psychopathology · 2025
    Review
  18. Article
  19. Article
  20. Article
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

16 authors at 9 institutions in 1 country.

Eric L RossDepartment of Psychiatry, Larner College of Medicine, University of Vermont, Burlington.
Robert M BossarteDepartment of Psychiatry and Behavioral Neurosciences, University of South Florida, Tampa.
Steven K DobschaVA Center to Improve Veteran Involvement in Care, Portland, Oregon.
Sarah M GildeaDepartment of Health Care Policy, Harvard Medical School, Boston, Massachusetts.
Irving HwangDepartment of Health Care Policy, Harvard Medical School, Boston, Massachusetts.
Chris J KennedyDepartment of Psychiatry, Massachusetts General Hospital, Boston.
Howard LiuDepartment of Health Care Policy, Harvard Medical School, Boston, Massachusetts.
Alex LuedtkeDepartment of Statistics, University of Washington, Seattle.
Brian P MarxNational Center for PTSD, VA Boston Healthcare System, Boston, Massachusetts.
Matthew K NockDepartment of Psychiatry, Massachusetts General Hospital, Boston.
Maria V PetukhovaDepartment of Health Care Policy, Harvard Medical School, Boston, Massachusetts.
Nancy A SampsonDepartment of Health Care Policy, Harvard Medical School, Boston, Massachusetts.
Nur Hani ZainalDepartment of Health Care Policy, Harvard Medical School, Boston, Massachusetts.
Erik SverdrupGraduate School of Business, Stanford University, Stanford, California.
Stefan WagerGraduate School of Business, Stanford University, Stanford, California.
Ronald C KesslerDepartment of Health Care Policy, Harvard Medical School, Boston, Massachusetts.
Harvard University · USCanandaigua VA Medical Center · USStanford University · USBoston University · USFred Hutch Cancer Center · USMassachusetts General Hospital · USUniversity of South Florida · USUniversity of Vermont · USVA Portland Health Care System · 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
NIMH NIH HHS R01 MH121478
6 · The paper itself

Abstract

Importance: Psychiatric hospitalization is the standard of care for patients presenting to an emergency department (ED) or urgent care (UC) with high suicide risk. However, the effect of hospitalization in reducing subsequent suicidal behaviors is poorly understood and likely heterogeneous. Objectives: To estimate the association of psychiatric hospitalization with subsequent suicidal behaviors using observational data and develop a preliminary predictive analytics individualized treatment rule accounting for heterogeneity in this association across patients. Design, Setting, and Participants: A machine learning analysis of retrospective data was conducted. All veterans presenting with suicidal ideation (SI) or suicide attempt (SA) from January 1, 2010, to December 31, 2015, were included. Data were analyzed from September 1, 2022, to March 10, 2023. Subgroups were defined by primary psychiatric diagnosis (nonaffective psychosis, bipolar disorder, major depressive disorder, and other) and suicidality (SI only, SA in past 2-7 days, and SA in past day). Models were trained in 70.0% of the training samples and tested in the remaining 30.0%. Exposures: Psychiatric hospitalization vs nonhospitalization. Main Outcomes and Measures: Fatal and nonfatal SAs within 12 months of ED/UC visits were identified in administrative records and the National Death Index. Baseline covariates were drawn from electronic health records and geospatial databases. Results: Of 196 610 visits (90.3% men; median [IQR] age, 53 [41-59] years), 71.5% resulted in hospitalization. The 12-month SA risk was 11.9% with hospitalization and 12.0% with nonhospitalization (difference, -0.1%; 95% CI, -0.4% to 0.2%). In patients with SI only or SA in the past 2 to 7 days, most hospitalization was not associated with subsequent SAs. For patients with SA in the past day, hospitalization was associated with risk reductions ranging from -6.9% to -9.6% across diagnoses. Accounting for heterogeneity, hospitalization was associated with reduced risk of subsequent SAs in 28.1% of the patients and increased risk in 24.0%. An individualized treatment rule based on these associations may reduce SAs by 16.0% and hospitalizations by 13.0% compared with current rates. Conclusions and Relevance: The findings of this study suggest that psychiatric hospitalization is associated with reduced average SA risk in the immediate aftermath of an SA but not after other recent SAs or SI only. Substantial heterogeneity exists in these associations across patients. An individualized treatment rule accounting for this heterogeneity could both reduce SAs and avert hospitalizations.

Indexed as

Major Depressive DisorderSuicidal IdeationFemaleHospitalizationHumansMaleMiddle AgedRetrospective StudiesRisk FactorsSuicide, Attempted

Identifiers

PMID37851457
PMCPMC10585585
OpenAlexW4387728899

What OpenQuestion holds

Textmetadata
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.