{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Simon GE"],"funding":["NIMH NIH HHS","National Institute of Mental Health"],"pagination":["13-19"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10939795"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["87"],"pubmed_abstract":["<h4>Objective</h4>Use health records data to predict suicide death following emergency department visits.<h4>Methods</h4>Electronic health records and insurance claims from seven health systems were used to: identify emergency department visits with mental health or self-harm diagnoses by members aged 11 or older; extract approximately 2500 potential predictors including demographic, historical, and baseline clinical characteristics; and ascertain subsequent deaths by self-harm. Logistic regression with lasso and random forest models predicted self-harm death over 90 days after each visit.<h4>Results</h4>Records identified 2,069,170 eligible visits, 899 followed by suicide death within 90 days. The best-fitting logistic regression with lasso model yielded an area under the receiver operating curve of 0.823 (95% CI 0.810-0.836). Visits above the 95th percentile of predicted risk included 34.8% (95% CI 31.1-38.7) of subsequent suicide deaths and had a 0.303% (95% CI 0.261-0.346) suicide death rate over the following 90 days. Model performance was similar across subgroups defined by age, sex, race, and ethnicity.<h4>Conclusions</h4>Machine learning models using coded data from health records have moderate performance in predicting suicide death following emergency department visits for mental health or self-harm diagnosis and could be used to identify patients needing more systematic follow-up."],"journal":["General hospital psychiatry"],"pubmed_title":["Predicting suicide death after emergency department visits with mental health or self-harm diagnoses."],"pmcid":["PMC10939795"],"funding_grant_id":["U19 MH121738"],"pubmed_authors":["Rossom RC","Beck A","Daida YG","Lynch FL","Shortreed SM","Simon GE","Ahmedani BK","Coleman KJ","Johnson E","Ziebell RA"],"additional_accession":[]},"is_claimable":false,"name":"Predicting suicide death after emergency department visits with mental health or self-harm diagnoses.","description":"<h4>Objective</h4>Use health records data to predict suicide death following emergency department visits.<h4>Methods</h4>Electronic health records and insurance claims from seven health systems were used to: identify emergency department visits with mental health or self-harm diagnoses by members aged 11 or older; extract approximately 2500 potential predictors including demographic, historical, and baseline clinical characteristics; and ascertain subsequent deaths by self-harm. Logistic regression with lasso and random forest models predicted self-harm death over 90 days after each visit.<h4>Results</h4>Records identified 2,069,170 eligible visits, 899 followed by suicide death within 90 days. The best-fitting logistic regression with lasso model yielded an area under the receiver operating curve of 0.823 (95% CI 0.810-0.836). Visits above the 95th percentile of predicted risk included 34.8% (95% CI 31.1-38.7) of subsequent suicide deaths and had a 0.303% (95% CI 0.261-0.346) suicide death rate over the following 90 days. Model performance was similar across subgroups defined by age, sex, race, and ethnicity.<h4>Conclusions</h4>Machine learning models using coded data from health records have moderate performance in predicting suicide death following emergency department visits for mental health or self-harm diagnosis and could be used to identify patients needing more systematic follow-up.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Mar-Apr","modification":"2025-04-22T18:50:30.688Z","creation":"2025-04-06T02:38:34.142Z"},"accession":"S-EPMC10939795","cross_references":{"pubmed":["38277798"],"doi":["10.1016/j.genhosppsych.2024.01.009"]}}