Predictive value of biomarker signatures for suicide risk in hospitalised patients with major depressive disorders: a multicentre study in Shanghai.

BACKGROUND

Biomarkers for predicting suicide risk in hospitalised patients with mental disorders have been understudied. Currently, suicide risk assessment tools based on objective indicators are limited in China.

AIMS

To examine the value of various biomarkers in suicide risk prediction and develop a risk assessment model with clinical utility using machine learning.

METHODS

This cohort study analysed patients with major depressive disorder (MDD) who were hospitalised for the first time between January 2016 and March 2023 from four specialised mental health institutions. A total of 139 features, including biomarker measurements, medical orders and psychological scales, were assessed for analysis. Their suicide risk was evaluated by qualified nurses using Nurse's Global Assessment of Suicide Risk within 1 week after admission. Five machine learning models were trained with 10-fold cross-validation across three hospitals and were externally validated in an independent cohort. The primary performance was assessed using the area under the receiver operating characteristic curve (AUROC). The model was interpreted using the SHapley Additive exPlanations (SHAP) analysis. Biomarker importance was evaluated by comparing model performance with and without these biomarkers.

RESULTS

Of 3143 patients with MDD included in this study, the incidence of high suicide risk within 1 week after first admission was 660 (21.0%). Among all models, the Extreme Gradient Boosting can more effectively predict future risks, with an AUROC higher than 0.8 (p<0.001). The SHAP values identified the 10 most important features, including five biomarkers. After clustering analysis, electroconvulsive therapy, physical restraint, β2-microglobulin and triiodothyronine were found to have heterogeneous effects on suicide risk. Combining biomarkers with other data from electronic health records significantly improved the performance and clinical utility of machine learning models based on demographics, diagnosis, laboratory tests, medical orders and psychological scales.

CONCLUSIONS

This study demonstrates the potential for a biomarker-based suicide risk assessment for patients with MDD, emphasising the interaction between biomarkers and therapeutic interventions.