biostudies-literatureSimon GENIMH NIH HHSNational Institute of Mental Health13-19https://www.ebi.ac.uk/biostudies/studies/S-EPMC10939795biostudies-literatureUnknown87<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.General hospital psychiatryPredicting suicide death after emergency department visits with mental health or self-harm diagnoses.PMC10939795U19 MH121738Rossom RCBeck ADaida YGLynch FLShortreed SMSimon GEAhmedani BKColeman KJJohnson EZiebell RAfalsePredicting suicide death after emergency department visits with mental health or self-harm diagnoses.<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.2024-01-01T00:00:00Z2024 Mar-Apr2025-04-22T18:50:30.688Z2025-04-06T02:38:34.142ZS-EPMC109397953827779810.1016/j.genhosppsych.2024.01.009