J Am Acad Child Adolesc Psychiatry. 2025 Jul 15:S0890-8567(25)00336-3. doi: 10.1016/j.jaac.2025.07.006. Online ahead of print.
ABSTRACT
OBJECTIVE: Suicide is one of the leading causes of death among youth worldwide, yet existing studies that aimed to predict the first onset of suicidal thoughts or behaviors (STB) included a limited number of data modalities, and/or focused on adult populations. We aimed to prospectively predict first-onset STB across four-year follow-ups in adolescents using (1) an existing STB history classification model that was previously applied to baseline data, and (2) a new machine learning model with 195 biopsychosocial features.
METHOD: The study included 7,503 unrelated adolescents (54.5% female, aged 9-11 years at baseline) from the multisite, longitudinal Adolescent Brain Cognitive Development (ABCD) project. Our existing baseline STB history classification model was applied to predict longitudinal first-onset STB versus healthy controls and clinical controls (those with mental illness but no STB). A new elastic net logistic regression model with 195 features was trained on data from 14 sites (n=5,220) and the resulting top 15 features were validated in seven independent sites (n=2,283).
RESULTS: Our previously developed model to classify STB lifetime history also prospectively predicted first-onset STB with an area under the curve (AUC)[95%] of 0.73[0.70,0.75], p<.001 relative to healthy controls and AUC[95%] of 0.63[0.60,0.66], p<.001 compared to clinical controls. The newly trained model with top 15 features performed similarly with AUC[95%]=0.73[0.71,0.76], p<.001 and AUC[95%]=0.64 [0.60,0.66], p<.001 for the same comparison groups. The most consistent predictors across models included female sex, sleep disturbances, and maladaptive home and school environments.
CONCLUSION: Our models predicted first-onset STB in adolescents with moderate accuracy. Our study also confirmed the roles of well-established psychological risk factors for STB and identified several novel neurocognitive and brain imaging risk factors. Future studies should validate our models in large-scale diverse samples before clinical translation.
PMID:40681146 | DOI:10.1016/j.jaac.2025.07.006
