Suicide Risk Assessment and Prevention: Challenges and Opportunities

Ryan, Eileen P.; Oquendo, María A.

doi:10.1176/appi.focus.20200011

Cited by 48 publications

(51 citation statements)

References 124 publications

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“…Even suicidal ideation and mental illness, the most commonly cited risk factors, do not always or exclusively predict suicidal behavior ( 5 ). Recently, various articles have been written to emphasize the importance of suicide risk assessment in improving suicide prevention ( 6 , 7 ). One possible way to improve suicide assessment is to include suicidal behavior more thoroughly in universal classification systems of mental disorders.…”

Section: Suicide In Dsm-5: Current Evidence For the Proposed Suicide mentioning

confidence: 99%

Suicide in DSM-5: Current Evidence for the Proposed Suicide Behavior Disorder and Other Possible Improvements

Fehling¹,

Selby

2021

Front. Psychiatry

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Suicide continues to be one of the greatest challenges faced by mental health clinicians and researchers, an issue made worse by increasing trends in the global suicide rate. Suicide behavior disorder (SBD) was introduced in DSM-5 as a disorder for further consideration and potential acceptance into the diagnostic system. There are numerous positive developments that would arise from the addition of a suicide-related diagnosis. Utilizing the 2009 guidelines established by Kendler and colleagues, the present review examines the evidence for SBD's validity and discusses the diagnosis' potential clinical benefits and limitations. Altogether, growing evidence indicates that SBD has preliminary validity and benefit. SBD presents with several significant limitations, however, and possible alternative additions to future DSMs are highlighted.

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Section: Suicide In Dsm-5: Current Evidence For the Proposed Suicide mentioning

confidence: 99%

Suicide in DSM-5: Current Evidence for the Proposed Suicide Behavior Disorder and Other Possible Improvements

Fehling¹,

Selby

2021

Front. Psychiatry

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show abstract

“…However, the identification of factors associated with STB does not necessarily imply that they could help predict future STB 10 , 11 and for that, models specifically designed for prediction are needed. Moreover, some variables used in previous studies (e.g., psychiatric assessment) 12 are impractical to assess in a large population of students as they require the expertise of a trained clinician. As pointed out in a recent paper summarizing 50 years of research on STB, further research should shift from identification of risk factors associated with STB to focus on developing predictive algorithms using machine learning methods 13 .…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach for predicting suicidal thoughts and behaviours among college students

Macalli

Navarro

Orri

et al. 2021

Sci Rep

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Suicidal thoughts and behaviours are prevalent among college students. Yet little is known about screening tools to identify students at higher risk. We aimed to develop a risk algorithm to identify the main predictors of suicidal thoughts and behaviours among college students within one-year of baseline assessment. We used data collected in 2013–2019 from the French i-Share cohort, a longitudinal population-based study including 5066 volunteer students. To predict suicidal thoughts and behaviours at follow-up, we used random forests models with 70 potential predictors measured at baseline, including sociodemographic and familial characteristics, mental health and substance use. Model performance was measured using the area under the receiver operating curve (AUC), sensitivity, and positive predictive value. At follow-up, 17.4% of girls and 16.8% of boys reported suicidal thoughts and behaviours. The models achieved good predictive performance: AUC, 0.8; sensitivity, 79% for girls, 81% for boys; and positive predictive value, 40% for girls and 36% for boys. Among the 70 potential predictors, four showed the highest predictive power: 12-month suicidal thoughts, trait anxiety, depression symptoms, and self-esteem. We identified a parsimonious set of mental health indicators that accurately predicted one-year suicidal thoughts and behaviours in a community sample of college students.

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“…8,9 However, the identi cation of factors associated with STB does not necessarily imply that they could help predict future STB 10,11 and for that, models speci cally designed for prediction are needed. Moreover, some variables used in previous studies (e.g., psychiatric assessment) 12 are impractical to assess in a large population of students as they require the expertise of a trained clinician.…”

Section: Introductionmentioning

confidence: 99%

Predicting suicidal thoughts and behaviors among college students: a machine learning approach

Macalli

Navarro

Orri

et al. 2021

Preprint

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Suicidal thoughts and behaviors are prevalent among college students. Yet little is known about screening tools to identify students at higher risk. We aimed to develop a risk algorithm to identify the main predictors of suicidal thoughts and behaviors among college students within one-year of baseline assessment. We used data collected in 2013–2019 from the French i-Share cohort, a longitudinal population-based study including 5066 volunteer students. To predict suicidal thoughts and behaviours at follow-up, we used random forests models with 70 potential predictors measured at baseline, including sociodemographic and familial characteristics, mental health and substance use. Model performance was measured using the area under the receiver operating curve (AUC), sensitivity, and positive predictive value. At follow-up, 17.4% of girls and 16.8% of boys reported suicidal thoughts and behaviors. The models achieved good predictive performance: AUC, 0.8; sensitivity, 79% for girls, 81% for boys; and positive predictive value, 40% for girls and 36% for boys. Among the 70 potential predictors, four showed the highest predictive power: 12-month suicidal thoughts, trait anxiety, depression symptoms, and self-esteem. We identified a parsimonious set of mental health indicators that accurately predicted one-year suicidal thoughts and behaviors in a community sample of college students.

show abstract

Suicide Risk Assessment and Prevention: Challenges and Opportunities

Cited by 48 publications

References 124 publications

Suicide in DSM-5: Current Evidence for the Proposed Suicide Behavior Disorder and Other Possible Improvements

Suicide in DSM-5: Current Evidence for the Proposed Suicide Behavior Disorder and Other Possible Improvements

A machine learning approach for predicting suicidal thoughts and behaviours among college students

Predicting suicidal thoughts and behaviors among college students: a machine learning approach

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