Predicting Suicide Attempts and Suicide Deaths Following Outpatient Visits Using Electronic Health Records
| Autor: | Susan M. Shortreed, Jean M. Lawrence, Gregory E. Simon, Robert B. Penfold, Arne Beck, Rebecca Ziebell, Brian K. Ahmedani, Rebecca C. Rossom, Frances L. Lynch, Beth Waitzfelder, Eric O. Johnson |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Adult
Male medicine.medical_specialty Adolescent Poison control Suicide Attempted Models Psychological Suicide prevention Occupational safety and health Young Adult 03 medical and health sciences 0302 clinical medicine Risk Factors Surveys and Questionnaires Outpatients medicine Electronic Health Records Humans 030212 general & internal medicine Depression (differential diagnoses) Aged Suicide attempt business.industry Emergency department Middle Aged Mental health 030227 psychiatry Psychiatry and Mental health Family medicine Female Death certificate business |
| Zdroj: | American Journal of Psychiatry. 175:951-960 |
| ISSN: | 1535-7228 0002-953X |
| DOI: | 10.1176/appi.ajp.2018.17101167 |
| Popis: | The authors sought to develop and validate models using electronic health records to predict suicide attempt and suicide death following an outpatient visit.Across seven health systems, 2,960,929 patients age 13 or older (mean age, 46 years; 62% female) made 10,275,853 specialty mental health visits and 9,685,206 primary care visits with mental health diagnoses between Jan. 1, 2009, and June 30, 2015. Health system records and state death certificate data identified suicide attempts (N=24,133) and suicide deaths (N=1,240) over 90 days following each visit. Potential predictors included 313 demographic and clinical characteristics extracted from records for up to 5 years before each visit: prior suicide attempts, mental health and substance use diagnoses, medical diagnoses, psychiatric medications dispensed, inpatient or emergency department care, and routinely administered depression questionnaires. Logistic regression models predicting suicide attempt and death were developed using penalized LASSO (least absolute shrinkage and selection operator) variable selection in a random sample of 65% of the visits and validated in the remaining 35%.Mental health specialty visits with risk scores in the top 5% accounted for 43% of subsequent suicide attempts and 48% of suicide deaths. Of patients scoring in the top 5%, 5.4% attempted suicide and 0.26% died by suicide within 90 days. C-statistics (equivalent to area under the curve) for prediction of suicide attempt and suicide death were 0.851 (95% CI=0.848, 0.853) and 0.861 (95% CI=0.848, 0.875), respectively. Primary care visits with scores in the top 5% accounted for 48% of subsequent suicide attempts and 43% of suicide deaths. C-statistics for prediction of suicide attempt and suicide death were 0.853 (95% CI=0.849, 0.857) and 0.833 (95% CI=0.813, 0.853), respectively.Prediction models incorporating both health record data and responses to self-report questionnaires substantially outperform existing suicide risk prediction tools. |
| Databáze: | OpenAIRE |
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