Evidence map›Paper›PMID 41331149›Full record

ArticleSocial psychiatry and psychiatric epidemiology2026

Comparing patterns of recent mental health service use for predicting suicidal events following emergency department mental health visits in the United States: A national cohort study.

Timothy Schmutte, Steven C Marcus, Ming Xie, Sara Wiesel Cullen, Tony Liu, Lyle H Ungar, Nick Cardamone, Nathaniel J Williams, Mark Olfson

Abstract readComparative Study
In one paragraph

Article in Social psychiatry and psychiatric epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Timothy SchmutteDepartment of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 901, New Haven, CT, US. timothy.schmutte@yale.edu.
Steven C MarcusSchool of Social Policy & Practice, University of Pennsylvania, Philadelphia, PA, US.
Ming XieDepartment of Psychiatry,Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US.
Sara Wiesel CullenSchool of Social Policy & Practice, University of Pennsylvania, Philadelphia, PA, US.
Tony LiuDepartment of Computer Science, Mount Holyoke College, South Hadley, MA, US.
Lyle H UngarDepartment of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, US.
Nick CardamoneDepartment of Psychiatry,Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US.
Nathaniel J WilliamsSchool of Social Work, Boise State University, Boise, ID, US.
Mark OlfsonDepartment of Psychiatry,Vagelos College of Physicians and Surgeons, Columbia University and the New York State Psychiatric Institute, New York, NY, US.

Funding

Development and clinical interpretation of machine learning emergency department suicide prediction algorithms using electronic health records and claimsR01MH126895 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI MARCUS, STEVEN C, OLFSON, MARK · 2021 to 2024
$3.3M
NIH HHS R01MH126895
6 · The paper itself

Abstract

objectiveTo examine patterns of recent service use to predict non-fatal suicidal events shortly following emergency department (ED) visits for mental health.

methodsFor this retrospective cohort study, we used Optum electronic health record data from 2,445,597 ED mental health episodes (2015-2022) for persons aged ≥ 10 years. We then constructed a series of logistic regression models to evaluate how six permutations of characterizing prior 180-day mental health service use predicted acute non-fatal suicidal events within 180 days of ED discharge beyond demographic characteristics and ED mental health diagnoses. Model performance was assessed by area under the receiver operating curve (AUC).

resultsOverall, 7.2% (n = 176,000) of episodes resulted in an acute suicidal event within 180 days. Model performance improved from demographic characteristics and ED mental health diagnoses (AUC = 0.76) when past 180-day service use variables were added, but with minimal differences between a binary variable of any mental health service use compared to monthly counts, weighted slopes, or interactions (AUC's 0.78-0.82). The final model containing demographics, ED mental diagnosis, and past 180-day service use yielded an AUC of 0.83. The most predictive past service use variables were any inpatient or ED event for self-harm or suicide ideation (OR = 5.45, 95% CI = 5.37, 5.54) and any ED mental health visit (OR = 1.87, 95% CI = 1.84, 1.90).

conclusionsAs part of evaluating suicide risk in ED settings, information about recent acute care for mental health, suicidal ideation, or self-harm use significantly contributes to the short-term prediction of non-fatal suicidal events.

Indexed as

Emergency Service, HospitalMental DisordersMental Health ServicesSuicidal IdeationSuicideAdolescentAdultAgedChildCohort StudiesFemaleHumansMaleMiddle AgedRetrospective StudiesUnited StatesEmergency DepartmentHealthcare UtilizationHealth ServicesMental HealthSelf-HarmSuicide

Identifiers

PMID41331149
PMCPMC12995995

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.