Trial reportFrontiers in public health2023
Identification of risk factors for attempted suicide by self-poisoning and a nomogram to predict self-poisoning suicide.
Trial report in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 22 citations in OpenAlex.
- Utilizing machine learning models for predicting outcomes in acute pancreatitis: development and validation in three retrospective cohorts.BMC medical informatics and decision making · 2025Article
- Assessment of suicidal risk factors in young depressed persons with non-suicidal self-injury based on an artificial intelligence.BMC psychology · 2025Article
- Depressive Symptoms and Risk Factors During the COVID-19 Pandemic Among People with/Without Mental Disorders.Healthcare (Basel, Switzerland) · 2025Article
- Risk Factors and Prediction of 28-Day-All Cause Mortality Among Critically Ill Patients with Acute Pancreatitis Using Machine Learning Techniques: A Retrospective Analysis of Multi-Institutions.Journal of inflammation research · 2024Article
- Performance of Machine Learning Algorithms for Predicting Disease Activity in Inflammatory Bowel Disease.Inflammation · 2023Article
- Trends and profiles of acute poisoning cases: a retrospective analysis.Frontiers in public health · 2023Article
- Editorial: Hopelessness and suicide among children and adolescents in low and middle income countries.Frontiers in sociology · 2023Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Suicide is a global concern, especially among young people. Suicide prediction models have the potential to make it easier to identify patients who are at a high risk of suicide, but they have very little predictive power when there is a positive value for suicide mortality. Therefore, the aim of the study is to uncover potential risk factors associated with suicide by self-poisoning and further to provide a trustworthy nomogram to predict self-poisoning suicide among poisoned patients. Methods: This study prospectively enrolled 237 patients who were treated for poisoning at the Fifth Medical Center of PLA General Hospital (Beijing) between May 2021 and May 2022. Patient's basic characteristics, daily activities, mental health status, and history of psychological illnesses were gathered to examine their predictive power for self-poisoning suicide. On developing a prediction model, patients were split 8:2 into a training ( Results: Of all poisoned patients, 64.6% committed suicide by self-poisoning. With regard to self-poisoning attempted suicide, multivariate analysis demonstrated that female gender, smoking, generalized anxiety disorder-7 (GAD-7), and beck hopelessness scale-20 (BHS-20) were significant risk factors, whereas married status, relatively higher education level, a sedentary time of 1-3 h per day, higher sport frequency per week, higher monthly income were significant protective features. The nomogram contained each of the aforementioned nine features. In the training group, the area under curve (AUC) of the nomogram was up to 0.938 (0.904-0.972), whereas in the validation group, it reached a maximum of 0.974 (0.937-1.000). Corresponding accuracy rates were up to 0.883 and 0.927, respectively, and the Conclusions: This study proposes a prediction model to stratify patients at a high risk of suicide by self-poisoning and to guide individual preventive strategies. Patients in the high-risk group require further mental health counseling to alleviate anxiety and hopelessness, healthy lifestyle like quitting smoking and exercising more, and restriction of access to poison and psychiatric drugs.
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