Evidence map›Paper›PMID 40991285›Full record

ArticleJAMA network open2025

Screening and Risk Algorithms for Detecting Pediatric Suicide Risk in the Emergency Department.

Robert H Aseltine, Shane J Sacco, Steven Rogers, Fei Wang, Harold Schwartz, Kun Chen

Abstract read
In one paragraph

Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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.

  1. Article
  2. Observational
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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

6 authors.

Robert H AseltineDivision of Behavioral Sciences and Community Health, UConn Health, Farmington, Connecticut.
Shane J SaccoCenter for Population Health, UConn Health, Farmington, Connecticut.
Steven RogersCenter for Population Health, UConn Health, Farmington, Connecticut.
Fei WangDepartment of Population Health Sciences, Weill Cornell Medicine, New York, New York.
Harold SchwartzInstitute of Living, Hartford HealthCare, Hartford, Connecticut.
Kun ChenCenter for Population Health, UConn Health, Farmington, Connecticut.

Funding

Developing Suicide Risk Algorithms for Diverse Clinical Settings using Data FusionR01MH124740 · NIMH · UNIVERSITY OF CONNECTICUT SCH OF MED/DNT · PI ASELTINE, ROBERT H, CHEN, KUN · 2020 to 2023
$3.2M
NIMH NIH HHS R01 MH124740
6 · The paper itself

Abstract

Importance: The Joint Commission requires hospitals and behavioral health care organizations to identify patients at risk of suicide (National Patient Safety Goal 15.01.01). Risk algorithms and in-person screening for suicide risk show utility in identifying patients at risk of suicidal behavior, yet there is little research examining their comparative performance in children and adolescents. Objective: To assess the performance of suicide risk screening and risk algorithms in identifying the risk of suicide attempts among pediatric patients in the emergency department (ED). Design, Setting, and Participants: This retrospective cohort study included youths aged 10 to 18 years presenting to the ED of a northeastern US state between September 1, 2019, and August 31, 2021. Patients were screened for suicide risk using the Ask Suicide-Screening Questions survey and the Columbia-Brief Suicide Severity Rating Scale. Electronic health records from this same cohort containing data from May 31, 2017, to the date of their first encounter within this period were extracted to train a risk algorithm. To observe the presence or absence of a suicide attempt, patients were followed up from their first ED encounter for a minimum of 6 months and a maximum of 2.5 years, through March 2, 2022. Data were analyzed from May 2023 to December 2024. Exposure: Assessments from suicide risk screening and a risk algorithm. Main Outcomes and Measures: The occurrence of a suicide attempt following a patient's first suicide risk screening or first visit in the screening period, if not screened. Results: Among 19 653 patients included in the analysis, the median age was 14.3 (IQR, 12.1-16.2) years, and 10 007 (50.9%) were female. Four hundred ninety-five patients (2.5%) were treated for a suicide attempt. Among patients screening positive for suicide risk in testing samples (mean, 8.1% [95% CI, 7.6%-8.6%]) and patients in the top 8.1% of the distribution on the algorithm, the algorithm correctly identified a mean of 50.7% (95% CI, 47.3%-54.1%) of those who attempted suicide in contrast to 36.5% (95% CI, 31.9%-41.2%) identified by screening. The algorithm uniquely identified 127% more youths who attempted suicide (125) than did screening (55). Conclusions and Relevance: In this cohort study of pediatric patients, the risk algorithm was superior to screening across all performance metrics and could substantially assist health care organizations' efforts to meet the Joint Commission's National Patient Safety Goal to reduce the risk of suicide.

Indexed as

AlgorithmsEmergency Service, HospitalMass ScreeningSuicide, AttemptedSuicide PreventionAdolescentChildFemaleHumansMaleRetrospective StudiesRisk AssessmentRisk FactorsSuicide

Identifiers

PMID40991285
PMCPMC12461438

What OpenQuestion holds

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Registered trials

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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.