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PREDICTING SUICIDE ATTEMPTS AND SUICIDE DEATHS FOLLOWING OUTPATIENT VISITS USING ELECTRONIC HEALTH RECORDS
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May 24 2018
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Source: Am J Psychiatry. 175(10):951-960
Details:
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Alternative Title:Am J Psychiatry
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Personal Author:
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Description:Objective
Develop and validate models using electronic health records to predict suicide attempt and suicide death following an outpatient visit.
Methods
Across seven health systems, 2,960,929 patients aged 13 or older (mean age 46, 62% female) made 10,275,853 specialty mental health visits and 9,685,206 primary care visits with mental health diagnoses between 1/1/2009 and 6/30/2015. Health system records and state death certificate data identified suicide attempts (n=24,133) and suicide deaths (n=1240) over 90 days following each visit. Potential predictors included 313 demographic and clinical characteristics extracted from records for up to five years prior to each visit: prior suicide attempts, mental health and substance use diagnoses, medical diagnoses, psychiatric medications dispensed, inpatient or emergency department care, and routinely administered PHQ-9 depression questionnaires. Logistic regression models predicting suicide attempt and death were developed using penalized LASSO variable selection in a random 65% sample of visits and validated in the remaining 35%.
Results
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 AUC) for prediction of suicide attempt and suicide death were 0.851 (95% CI 0.848 to 0.853) and 0.861 (95% CI 0.848 to 0.875). 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 to 0.857) and 0.833 (95% CI 0.813 to 0.853).
Conclusions
Prediction models incorporating both health records data and responses to self-report questionnaires substantially outperform existing suicide risk prediction tools.
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Pubmed ID:29792051
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Pubmed Central ID:PMC6167136
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Funding:
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Volume:175
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Issue:10
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