Risk prediction models for adolescent suicide: A systematic review and meta-analysis

Abstract

Background

Adolescence is recognized as a high-risk period for suicide, with the prevalence of suicide risk among adolescents rising globally, positioning it as one of the most urgent public health concerns worldwide. This study systematically reviews and evaluates adolescent suicide risk prediction models, identifies key predictors, and offers valuable insights for the development of future tools to assess suicide risk in adolescents.

Methods

We systematically searched four international databases (PubMed, Web of Science, Embase, and Cochrane Libraries) and four Chinese databases (Chinese Biomedical Literature Database, China National Knowledge Infrastructure, Wanfang, and Weipu Libraries) up to May 14, 2024. Two researchers independently screened the literature, extracted data, and evaluated the model quality using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Stata17.0 and R4.4.2 softwares were used to conduct meta-analysis.

Results

25 studies involving 62 prediction models were included, of which 51 models were internally validated with an area under the curve (AUC) > 0.7. The researchers mainly used modeling methods such as logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), decision tree (DT), and support vector machine (SVM). 22 studies performed internal validation of the model, while only 3 had undergone external validation. The models developed in all 25 studies demonstrated good applicability, 19 studies showed a high risk of bias, primarily due to inappropriate data sources and poor reporting of the analysis domain. Meta-analysis results showed that the pooled AUC for internal validation of 28 adolescent suicide risk prediction models was 0.846 (95 %CI=0.828–0.866), while the AUC for external validation of 2 models was 0.810 (95 %CI=0.704–0.932). The detection rate of suicide risk among adolescents was 22.5 % (95 %CI=18.0 %-27.0 %), gender(OR=1.490,95 %CI=1.217–1.824), depressive symptoms (OR=3.175,95 %CI=1.697–5.940), stress level (OR=2.436,95 %CI=1.019–5.819), previous suicidal ideation (OR=1.772,95 %CI=1.640–1.915), previous self-injurious behaviors (OR=4.138,95 %CI=1.328–12.895), drug abuse(OR=3.316,95 %CI=1.537–7.154), history of bullying(OR=3.417,95 %CI=2.567–4.547), and family relationships (OR=1.782,95 %CI=1.115–2.849) were independent influences on adolescent suicide risk (P < 0.05).

Conclusion

The adolescent suicide risk prediction model demonstrated excellent predictive performance. However, given the high risk of bias in most studies and the insufficient external validation, its clinical applicability requires further investigation. Future studies on adolescent suicide risk prediction models should focus on predictors, including gender, depressive symptoms, stress level, previous suicidal ideation, previous self-injurious behaviors, drug abuse, history of bullying, and family relationships.