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Implementing the Child Psychosis-risk Screening System in Pediatric-to-Psychiatric Care: A Novel Approach Using Child Behavior Checklist Data

Published online by Cambridge University Press:  26 August 2025

Y. Hamasaki ,
Y. Sakaue ,
S. Michikoshi ,
T. Nakayama ,
S. Ueba ,
M. Isobe  and
T. Hikida
Y. Hamasaki*
Affiliation:
Faculty of Contemporary Society, Kyoto Women’s University, Kyoto
Y. Sakaue
Affiliation:
Department of Pediatrics, Shiga University of Medical Science, Shiga
S. Michikoshi
Affiliation:
Faculty of Data Science, Kyoto Women’s University, Kyoto
T. Nakayama
Affiliation:
Faculty of Contemporary Society, Kyoto Women’s University, Kyoto
S. Ueba
Affiliation:
Department of Pediatrics, Saiseikai Moriyama Municipal Hospital, Shiga
M. Isobe
Affiliation:
Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto
T. Hikida
Affiliation:
Laboratory for Advanced Brain Functions, Osaka University, Osaka, Japan
*
*Corresponding author.

Abstract

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Introduction

Screening children for the potential development of psychosis is challenging, resulting in a suboptimal healthcare transition from pediatrics to psychiatry.

Objectives

In this prospective study, we aimed to evaluate the predictive ability of the Child Psychosis-Risk Screening System (CPSS) for schizophrenia spectrum disorder (SSD) by observing the outcomes of pediatric and psychiatric outpatients.

Methods

A total of 478 outpatients aged 6–18 years visiting the pediatric and psychiatric departments of university and community hospitals were enrolled in this study. Assessments included the Child Behavior Checklist (CBCL) and clinical data (sex, age, birth month, chief complaint, diagnosis, abuse, bullying, and withdrawal). The CPSS calculated the risk of developing SSD using eight CBCL subscale scores. The presence of SSD was confirmed after one year. Receiver operating characteristic (ROC) curve analysis was used to evaluate the accuracy of the CPSS in predicting SSD onset. Light-gradient boosting machine (LightGBM) learning algorithm was used to calculate the importance of each clinical data point for SSD onset prediction.

Results

ROC analysis demonstrated that CPSS showed adequate predictive power for determining SSD onset (area under the curve = 0.902, 95% confidence interval: 0.866–0.939). LightGBM revealed that the importance of the CPSS risk% in predicting SSD onset exceeded other variables, including the CBCL Thought Problems subscale.

Conclusions

The CPSS demonstrated adequate predictive power for SSD development and may serve as an objective adjunctive diagnostic method to screen children at risk for SSD who require early psychiatric referral. As a simple screening system utilizing common clinical practice CBCL data, we advocate CPSS implementation in pediatric-to-psychiatric healthcare.

Disclosure of Interest

None Declared

Information

Type
Abstract
Information
European Psychiatry , Volume 68 , Special Issue S1: Abstracts of the 33rd European Congress of Psychiatry , April 2025 , pp. S556
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of European Psychiatric Association
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