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

Laura S. Van Velzen, Yara J. Toenders, Aina Avila-Parcet, Richard Dinga, Jill A. Rabinowitz, Adrián I. Campos, Neda Jahanshad, Miguel E. Rentería, Lianne Schmaal

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)210-218
Number of pages9
JournalBritish Journal of Psychiatry
Volume220
Issue number4
DOIs
StatePublished - Apr 2 2022

Keywords

  • Suicide
  • children
  • machine learning
  • penalised logistic regression
  • youth

ASJC Scopus subject areas

  • Psychiatry and Mental health

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