What question did this study set out to answer?

To create and validate a model for predicting suicide attempts in untreated adolescents with major depressive disorder.

March 4, 2026Open Access

Development and External Validation of a Prediction Model to Identify Suicide Attempters in Treatment-Naive Adolescents With Major Depressive Disorder.

YSYuqin SongNorth Sichuan Medical University MDMengqin DaiNorth Sichuan Medical University QFQiuyue FanNorth Sichuan Medical University

Key Points

To create and validate a model for predicting suicide attempts in untreated adolescents with major depressive disorder.
Utilized XGBoost algorithm for modeling
Included 11 clinical variables for identification
Conducted external validation using independent datasets
Model effectively identifies suicide attempters among treatment-naive adolescents
Achieved stable performance in external validation sets
Supports evidence-based suicide prevention efforts for clinicians

Abstract

Despite including only 11 easily collectible clinical variables, the XGBoost model effectively identifies suicide attempters among untreated adolescent first-visit MDD patients and performs stably in external validation sets. This is beneficial for clinicians to conduct evidence-based suicide prevention efforts.

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