Identifying risk factors for mortality among patients previously hospitalized for a suicide attempt

Doshi, Riddhi; Chen, Kun; Wang, Fei; Schwartz, Harold; Herzog, Alfred; Aseltine, Robert H.

doi:10.1038/s41598-020-71320-3

Cited by 17 publications

(10 citation statements)

References 25 publications

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“…Attempted suicide is the manifestation of a process that develops gradually ( 4 ) . It is necessary to know the circumstances in which suicidal behavior arises to prevent the factors that cause it ( 5 ) .…”

Section: Introductionmentioning

confidence: 99%

Prevalence of suicidal behavior in young university students: A systematic review with meta-analysis

Crispim

Santos

Frazão

et al. 2021

Rev. Latino-Am. Enfermagem

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Objective: to identify the prevalence of suicidal behavior in young university students. Method: a systematic review with meta-analysis of cross-sectional studies based on the Joanna Briggs Institute proposal, and carried out in the PubMed, Web of Science, Scopus, PsycINFO and LILACS databases and in the Brazilian Digital Library of Theses and Dissertations, with no language or year restrictions. A total of 2,942 publications were identified. Selection, data extraction and methodological evaluation of the studies were performed by two independent researchers. The meta-analysis was performed considering the random effects model. Results: eleven articles were included in this review. The prevalence variation for suicidal ideation was from 9.7% to 58.3% and, for attempted suicide, it was from 0.7% to 14.7%. The meta-analysis showed a 27.1% prevalence for suicidal ideation in life, 14.1% for ideation in the last year, and 3.1% for attempted suicide in life. Conclusion: the high prevalence of suicidal behavior, even with the considerable heterogeneity of the studies, raises the need to implement interventions aimed at preventing suicide and promoting mental health, especially in the academic environment.

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Section: Introductionmentioning

confidence: 99%

Prevalence of suicidal behavior in young university students: A systematic review with meta-analysis

Crispim

Santos

Frazão

et al. 2021

Rev. Latino-Am. Enfermagem

View full text Add to dashboard Cite

show abstract

“…Suicide among children and adolescents is one of the most critical public health concerns 1 – 5 . As the second leading cause of death among children, adolescents and young adults between ages 10–24 years, suicide claims over 6000 young lives every year in the US alone 6 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning for suicide risk prediction in children and adolescents with electronic health records

Aseltine

Doshi

et al. 2020

Transl Psychiatry

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Accurate prediction of suicide risk among children and adolescents within an actionable time frame is an important but challenging task. Very few studies have comprehensively considered the clinical risk factors available to produce quantifiable risk scores for estimation of short- and long-term suicide risk for pediatric population. In this paper, we built machine learning models for predicting suicidal behavior among children and adolescents based on their longitudinal clinical records, and determining short- and long-term risk factors. This retrospective study used deidentified structured electronic health records (EHR) from the Connecticut Children’s Medical Center covering the period from 1 October 2011 to 30 September 2016. Clinical records of 41,721 young patients (10–18 years old) were included for analysis. Candidate predictors included demographics, diagnosis, laboratory tests, and medications. Different prediction windows ranging from 0 to 365 days were adopted. For each prediction window, candidate predictors were first screened by univariate statistical tests, and then a predictive model was built via a sequential forward feature selection procedure. We grouped the selected predictors and estimated their contributions to risk prediction at different prediction window lengths. The developed predictive models predicted suicidal behavior across all prediction windows with AUCs varying from 0.81 to 0.86. For all prediction windows, the models detected 53–62% of suicide-positive subjects with 90% specificity. The models performed better with shorter prediction windows and predictor importance varied across prediction windows, illustrating short- and long-term risks. Our findings demonstrated that routinely collected EHRs can be used to create accurate predictive models for suicide risk among children and adolescents.

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“…Attempts were also modelled by age, gender, and fused social determinants alone for comparison with the conventional and fusion models. We followed the same “marginal screening + regularized logistic modeling” procedures as Step 2 and past related works [ 5 , 27 ]. Here, fusion features were screened also controlling for age, gender, and screened ICD-9 codes.…”

Section: Methodsmentioning

confidence: 99%

Target-based fusion using social determinants of health to enhance suicide prediction with electronic health records

et al. 2023

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Objective Preventing suicide in US youth is of paramount concern, with rates increasing over 50% between 2007 and 2018. Statistical modeling using electronic health records may help identify at-risk youth before a suicide attempt. While electronic health records contain diagnostic information, which are known risk factors, they generally lack or poorly document social determinants (e.g., social support), which are also known risk factors. If statistical models are built incorporating not only diagnostic records, but also social determinants measures, additional at-risk youth may be identified before a suicide attempt. Methods Suicide attempts were predicted in hospitalized patients, ages 10–24, from the State of Connecticut’s Hospital Inpatient Discharge Database (HIDD; N = 38943). Predictors included demographic information, diagnosis codes, and using a data fusion framework, social determinants features transferred or fused from an external source of survey data, The National Longitudinal Study of Adolescent to Adult Health (Add Health). Social determinant information for each HIDD patient was generated by averaging values from their most similar Add Health individuals (e.g., top 10), based upon matching shared features between datasets (e.g., Pearson’s r). Attempts were then modelled using an elastic net logistic regression with both HIDD features and fused Add Health features. Results The model including fused social determinants outperformed the conventional model (AUC = 0.83 v. 0.82). Sensitivity and positive predictive values at 90 and 95% specificity were almost 10% higher when including fused features (e.g., sensitivity at 90% specificity = 0.48 v. 0.44). Among social determinants variables, the perception that their mother cares and being non-religious appeared particularly important to performance improvement. Discussion This proof-of-concept study showed that incorporating social determinants measures from an external survey database could improve prediction of youth suicide risk from clinical data using a data fusion framework. While social determinant data directly from patients might be ideal, estimating these characteristics via data fusion avoids the task of data collection, which is generally time-consuming, expensive, and suffers from non-compliance.

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Identifying risk factors for mortality among patients previously hospitalized for a suicide attempt

Cited by 17 publications

References 25 publications

Prevalence of suicidal behavior in young university students: A systematic review with meta-analysis

Prevalence of suicidal behavior in young university students: A systematic review with meta-analysis

Machine learning for suicide risk prediction in children and adolescents with electronic health records

Target-based fusion using social determinants of health to enhance suicide prediction with electronic health records

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