Classification of suicidal thoughts and behaviour in children: results from penalized logistic regression analyses in the Adolescent Brain Cognitive Development study - van Velzen, Laura S. (Br J Psychiatry, 2022)
BACKGROUND: Despite efforts to predict suicide risk in children, the ability to reliably identify who will engage in suicide thoughts or behaviours (STB) has remained unsuccessful. AIMS: We apply a novel machine learning approach and examine whether children with STB could be differentiated from children without STB based on a combination of traditional (sociodemographic, physical health, social environmental, clinical psychiatric) risk factors, but also more novel risk factors (cognitive, neuroimaging and genetic characteristics). METHOD: The study sample included 5,885 unrelated children (50% female, 67% white, 9-11 years) from the Adolescent Brain Cognitive Development (ABCD) study. We performed penalized logistic regression analysis to distinguish between: 1. Children with current or past STB, 2. Children with a mental illness but no STB (clinical controls), and 3. Healthy control children (no STB and no history of mental illness). The model was subsequently validated on 7 independent sites of the ABCD study (N=1712). RESULTS: Results showed that we were able to distinguish the STB group from healthy controls and clinical controls (AUROC: 0.79-0.81 and 0.70-0.78 respectively). However, we could not distinguish children with suicidal ideation from those who attempted suicide (AUROC: 0.49-0.59). The factors that differentiated the STB group from the clinical control group included family conflict, prodromal psychosis symptoms, impulsivity, depression severity and history of mental health treatment. CONCLUSIONS: This work highlights that mostly clinical psychiatric factors were able to distinguish children with STB from children without STB. Future research is needed to determine if these variables prospectively predict subsequent suicidal behaviour.