Classification of suicidal thoughts and behaviour in children: results from penalised logistic regression analyses in the Adolescent Brain Cognitive Development study

Abstract

Background Despite efforts to predict suicide risk in children, the ability to reliably identify who will engage in suicide thoughts or behaviours has remained unsuccessful. Aims We apply a novel machine-learning approach and examine whether children with suicide thoughts or behaviours could be differentiated from children without suicide thoughts or behaviours 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 included 5885 unrelated children (50% female, 67% White, 9–11 years of age) from the Adolescent Brain Cognitive Development (ABCD) study. We performed penalised logistic regression analysis to distinguish between: (a) children with current or past suicide thoughts or behaviours; (b) children with a mental illness but no suicide thoughts or behaviours (clinical controls); and (c) healthy control children (no suicide thoughts or behaviours and no history of mental illness). The model was subsequently validated with data from seven independent sites involved in the ABCD study (n = 1712). Results Our results showed that we were able to distinguish the suicide thoughts or behaviours group from healthy controls (area under the receiver operating characteristics curve: 0.80 child-report, 0.81 for parent-report) and clinical controls (0.71 child-report and 0.76–0.77 parent-report). However, we could not distinguish children with suicidal ideation from those who attempted suicide (AUROC: 0.55–0.58 child-report; 0.49–0.53 parent-report). The factors that differentiated the suicide thoughts or behaviours 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 suicide thoughts or behaviours from children without suicide thoughts or behaviours. Future research is needed to determine if these variables prospectively predict subsequent suicidal behaviour.

Suicidal Thoughts and Trajectories of Psychopathological and Behavioral Symptoms in Adolescence

“…However, the association between anxious depressive symptoms and suicidal thoughts became nonsignificant after adjusting for other symptoms and confounders. This result seems to be inconsistent with a previous cross-sectional study in which depression, among various risk factors including CBCL subscales, was found to predict suicidal thoughts and behaviors . The longitudinal nature of our study may partly explain this disparity, as we clustered anxious depressive symptoms into subgroups with different trajectories.…”

Evidence for the biopsychosocial model of suicide: a review of whole person modeling studies using machine learning

“…Of the 20 selected studies, eight ( 51 – 58 ) investigated suicide-related outcomes in the general population [including two studies in youth ( 51 , 52 ) and two in elderly ( 57 , 58 )]. Six studies ( 59 – 64 ) focused on individuals with a diagnosis of major depressive disorder (MDD) or mood-related disorders [two recruited from psychiatric hospitals ( 61 , 63 ), one of antidepressant users recruited from either the Australian Genetics of Depression Study or through the nationwide Pharmaceutical Benefits Scheme database ( 62 )], two from the general population (i.e., UK Biobank participants that voluntarily enrolled in the study, and participants of the Brazilian Longitudinal Study of Adult Health, consisting of public institutions’ employees) that met criteria for common mental disorders ( 59 , 60 ), one from retrospective analysis of health record data ( 64 ), and one ( 65 ) focused on individuals diagnosed with multiple sclerosis.…”

“…Van Velzen et al ( 52 ) assessed n = 5,885 children also from the ABCD study ( 71 ). Binomial penalized logistic regressions were used to predict either parent- or child-reported suicidal thoughts and behaviors, recorded in the K-SADS.…”

Identifying subtypes of youth suicidality based on psychopathology: alterations in genetic, neuroanatomical and environmental features

“…This heterogeneity may explain why a singular brain-centric model of youth STBs has been elusive to date 7 . For example, when reduced thickness of left superior temporal sulcus was observed without considering subtypes in a prior ABCD-based analysis, the effect size was small 22 ; another machine learning study using the same ABCD data also reported low contribution of neuroimaging features to suicidal behavior prediction 23 , suggesting treating all suicidal children as one homogeneous group prevents identification of sensitive STBs biomarkers.…”