Zhu Enzhao, Wang Jiayi, Cai Zheya, Zhou Guoquan, Li Chunbo, Chen Fazhan, Ju Kang, Chen Liangliang, Yin Yichao, Chen Yi, Zhang Yanping, Liu Siqi, Zhang Xu, Dai Jianmeng, Yu Qianyi, Qiu Jianping, Wang Hui, Shi Weizhong, Wang Feng, Wang Dong, Chen Zhihao, Hou Jiaojiao, Li Hui, Ai Zisheng
School of Medicine, Tongji University, Shanghai, China.
Shanghai Putuo District Center Hospital, Shanghai, China.
Gen Psychiatr. 2025 Sep 14;38(5):e101957. doi: 10.1136/gpsych-2024-101957. eCollection 2025.
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.
To examine the value of various biomarkers in suicide risk prediction and develop a risk assessment model with clinical utility using machine learning.
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.
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.
This study demonstrates the potential for a biomarker-based suicide risk assessment for patients with MDD, emphasising the interaction between biomarkers and therapeutic interventions.
针对住院精神障碍患者自杀风险预测生物标志物的研究较少。目前，中国基于客观指标的自杀风险评估工具有限。
探讨各种生物标志物在自杀风险预测中的价值，并利用机器学习开发具有临床实用性的风险评估模型。
这项队列研究分析了2016年1月至2023年3月期间在四家专业精神卫生机构首次住院的重度抑郁症（MDD）患者。共评估了139个特征，包括生物标志物测量、医嘱和心理量表，用于分析。在入院后1周内，由合格护士使用护士自杀风险全球评估法评估其自杀风险。五个机器学习模型在三家医院进行了10倍交叉验证训练，并在独立队列中进行了外部验证。主要性能通过受试者操作特征曲线下面积（AUROC）进行评估。使用SHapley加性解释（SHAP）分析对模型进行解释。通过比较包含和不包含这些生物标志物的模型性能来评估生物标志物的重要性。
本研究纳入的3143例MDD患者中，首次入院后1周内高自杀风险的发生率为660例（21.0%）。在所有模型中，极端梯度提升模型能更有效地预测未来风险，AUROC高于0.8（p<0.001）。SHAP值确定了10个最重要的特征，包括5个生物标志物。聚类分析后发现，电休克治疗、身体约束、β2-微球蛋白和三碘甲状腺原氨酸对自杀风险有不同影响。将生物标志物与电子健康记录中的其他数据相结合，显著提高了基于人口统计学、诊断、实验室检查、医嘱和心理量表的机器学习模型的性能和临床实用性。
本研究证明了基于生物标志物的MDD患者自杀风险评估的潜力，强调了生物标志物与治疗干预之间的相互作用。
