Evidence map›Paper›PMID 41895732›Full record

ArticleBMJ health & care informatics2026

Biomarkers associated with future suicide risk enhance predictive performance in psychiatric inpatients.

Zheya Cai, Enzhao Zhu, Jianmeng Dai, Xu Zhang, Jiayi Wang, Xiuake Bahuojia, Ruyi Shui, Qiuyi Lu, Duoduo Bai, Shengbei Liu and 33 more

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In one paragraph

Article in BMJ health & care informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

43 authors.

Zheya Cai *Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Enzhao Zhu *Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Jianmeng Dai *Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Xu Zhang *Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Jiayi WangShanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Xiuake BahuojiaShanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Ruyi ShuiShanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Qiuyi LuShanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Duoduo BaiShanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Shengbei LiuShanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Ruichen TangSchool of Electronic and Information Engineering, Tongji University, Shanghai, China.
Xin WangSchool of Electronic and Information Engineering, Tongji University, Shanghai, China.
Qianyi YuShanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Han YangSchool of Life Sciences and Technology, Tongji University, Shanghai, China.
Guoquan ZhouShanghai Putuo District Mental Health Center, Shanghai, China.
Siqi LiuShanghai Putuo District Mental Health Center, Shanghai, China.
Zhihao ChenEast China University of Science and Technology, Shanghai, China.
Yuqin WengShanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Xinyi TangShanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Huan WangShanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Huiqing PanShanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Tongxing OuShanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Yue LiuShanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Weiwei XuShanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Kexin ChenSchool of Basic Medical Sciences, Nanjing Medical University, Nanjing, Jiangsu, China.
Xunuo LuSchool of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
Wenjing WangSchool of Public Health, Capital Medical University, Beijing, China.
Xuqi SongShanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Zongyuan WangShanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Feng WangClinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Dong WangClinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Kang JuShanghai Changning District Mental Health Center, Shanghai, China.
Liangliang ChenShanghai Changning District Mental Health Center, Shanghai, China.
Yichao YinShanghai Changning District Mental Health Center, Shanghai, China.
Chunbo LiShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Yanping ZhangShanghai Jinshan District Mental Health Center, Shanghai, China.
Pu AiShanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Tianyu JiShanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Weizhong ShiShanghai Hospital Development Center, Shanghai, China.
Jiaojiao HouUniversity Clinic of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, RWTH Aachen University, Aachen, Germany.
Fazhan ChenClinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China azs1966@126.com develop909@163.com lihuindyxs@163.com.
Hui LiShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, China azs1966@126.com develop909@163.com lihuindyxs@163.com.
Zisheng AiShanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China azs1966@126.com develop909@163.com lihuindyxs@163.com.ORCID http://orcid.org/0009-0005-9095-2105

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesSuicide risk assessments currently rely on subjective clinical judgement, lacking objective measures. This study aimed to evaluate the association between biomarkers and suicide risk and to explore their predictive potential using machine learning (ML).

methodsWe analysed data from 2785 first-admission psychiatric inpatients across three institutions, including 103 biomarkers and 13 demographic and clinical variables. Suicide risk was assessed 1 week after admission using the Nurses' Global Assessment of Suicide Risk.

resultsA total of 2785 patients met the inclusion criteria, from which 978 were selected via propensity score matching to minimise confounding from demographic factors, treatment differences and symptom severity. Multivariate random effects logistic regression identified nine biomarkers associated with elevated suicide risk and six with potential protective effects. Time-trend analyses further revealed that nine biomarkers showed significant changes following risk escalation. Integrating biomarkers with demographic data, treatment information and psychological scale scores substantially improved ML model performance, achieving an area under the receiver operating characteristic curve of 0.808 in the external testing cohort. The inclusion of biomarkers significantly enhanced predictive accuracy.

conclusionThis study highlights the potential of biomarkers with ML to predict future risk, offering objective assessments and supporting early interventions.

Indexed as

BiomarkersInpatientsMachine LearningMental DisordersSuicideAdultFemaleHumansLogistic ModelsMaleMiddle AgedPredictive Learning ModelsRisk AssessmentRisk FactorsBiomarkersArtificial intelligenceData Interpretation, StatisticalMachine Learning

Identifiers

PMID41895732
PMCPMC13034244

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.