Evidence map›Paper›PMID 41264072›Full record

ArticleChild psychiatry and human development2025

Predictive Model of Suicidal Ideation Among Children and Adolescents in Pediatric Outpatient Settings.

Yu Ogawa, Mariko Hosozawa, Akio Nakamura, Hitoshi Takei, Ken Takahashi, Shinji Mochizuki, Kanako Kusaba, Kyoko Tanaka, Tomoyuki Nakazawa, Takahiro Niizuma and 1 more

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Article in Child psychiatry and human development, 2025. 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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1 · What the graph read from it

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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

11 authors.

Yu OgawaDepartment of Pediatrics and Adolescent Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Mariko HosozawaBureau of Global Health Cooperation, Institute for Global Health Policy Research, Japan Institute for Health Security, 1-21-1 Toyama, Shinjuku-Ku, Tokyo, 162-8655, Japan. hosozawa.m@jihs.go.jp.
Akio NakamuraDepartment of Pediatrics and Adolescent Medicine, Juntendo University Hospital, Tokyo, Japan.
Hitoshi TakeiDepartment of Pediatric Psychiatry, Tokyo Metropolitan Otsuka Hospital, Tokyo, Japan.
Ken TakahashiDepartment of Pediatrics, Juntendo University Urayasu Hospital, Chiba, Japan.
Shinji MochizukiDepartment of Pediatrics, National Center for Global Health and Medicine, Japan Institute for Health Security, Tokyo, Japan.
Kanako KusabaDepartment of Pediatrics, Tokyo Metropolitan Tobu Medical Center, Tokyo, Japan.
Kyoko TanakaDepartment of Pediatrics, Faculty of Medicine, Juntendo University, Tokyo, Japan.
Tomoyuki NakazawaDepartment of Pediatrics, Tokyo Metropolitan Toshima Hospital, Tokyo, Japan.
Takahiro NiizumaDepartment of Pediatrics, Koshigaya Municipal Hospital, Tokyo, Japan.
Hiromichi ShojiDepartment of Pediatrics and Adolescent Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early identification of suicide risk in pediatric outpatient settings is crucial for preventive interventions. This multicenter study aimed to develop a machine learning model to predict self-reported suicidal ideation among children and adolescents aged 7-17 years visiting pediatric outpatient settings. Least Absolute Shrinkage and Selection Operator and logistic regression analysis were used for model development and feature selection. Of 855 patients recruited, 329 were included in the analysis (mean age 11.0 years, 61.7% males). Frequent suicidal ideation (defined as always thinking life is not worth living during the past week) was reported by 20 (6.1%) patients. Based on six items assessing child-rated psychosocial functioning and physical and psychological symptoms, the selected model achieved an Area Under the Curve (AUC) of 0.81 (95% CI: 0.70-0.92). This brief six-item model can help identify children at risk of suicide in pediatric outpatient settings, potentially facilitating timely intervention.

Indexed as

Child and Adolescent SuicidesMachine LearningPediatric Mental HealthPediatricsPrediction ModelSuicidal Ideation

Identifiers

PMID41264072

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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.