Evidence map›Paper›PMID 37350874›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2023

Detection of Suicidal Behavior and Self-harm Among Children Presenting to Emergency Departments: A Tree-based Classification Approach.

Juliet B Edgcomb, Chi-Hong Tseng, Mengtong Pan, Alexandra Klomhaus, Bonnie Zima

Abstract read
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Juliet B EdgcombUCLA Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA.
Chi-Hong TsengDepartment of Medicine, UCLA David Geffen School of Medicine, Los Angeles, CA.
Mengtong PanDepartment of Medicine, UCLA David Geffen School of Medicine, Los Angeles, CA.
Alexandra KlomhausDepartment of Medicine, UCLA David Geffen School of Medicine, Los Angeles, CA.
Bonnie ZimaUCLA Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA.

Funding

UCLA Clinical Translational Science InstituteUL1TR001881 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ARLEEN F. BROWN, ARASH NAEIM · 2016 to 2026
$118.1M
Electronic Health Record Phenotyping for Case Detection and Prediction of Emergency Department Visits for Child and Adolescent Suicide AttemptsK23MH130745 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI EDGCOMB, JULIET BENI · 2022 to 2025
$844k
NCATS NIH HHS UL1 TR001881NIMH NIH HHS K23 MH130745
6 · The paper itself

Abstract

Suicide is the second leading cause of death of U.S. children over 10 years old. Application of statistical learning to structured EHR data may improve detection of children with suicidal behavior and self-harm. Classification trees (CART) were developed and cross-validated using mental health-related emergency department (MH-ED) visits (2015-2019) of children 10-17 years (N=600) across two sites. Performance was compared with the CDC Surveillance Case Definition ICD-10-CM code list. Gold-standard was child psychiatrist chart review. Visits were suicide-related among 284/600 (47.3%) children. ICD-10-CM detected cases with sensitivity 70.7 (95%CI 67.0-74.3), specificity 99.0 (98.8-100), and 85/284 (29.9%) false negatives. CART detected cases with sensitivity 85.1 (64.7-100) and specificity 94.9 (89.2-100). Strongest predictors were suicide-related code, MH- and suicide-related chief complaints, site, area deprivation index, and depression. Diagnostic codes miss nearly one-third of children with suicidal behavior and self-harm. Advances in EHR-based phenotyping have the potential to improve detection of childhood-onset suicidality.

Identifiers

PMID37350874
PMCPMC10283119

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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.