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Am J Psychiatry. 2018 Oct 1;175(10):951-960. doi: 10.1176/appi.ajp.2018.17101167. Epub 2018 May 24.

Predicting Suicide Attempts and Suicide Deaths Following Outpatient Visits Using Electronic Health Records.

Author information

1
From the Kaiser Permanente Washington Health Research Institute, Seattle; the Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena; the HealthPartners Institute, Minneapolis; the Center for Health Services Research, Henry Ford Health System, Detroit; the Center for Health Research, Kaiser Permanente Northwest, Portland, Oreg.; the Institute for Health Research, Kaiser Permanente Colorado, Denver; and the Center for Health Research, Kaiser Permanente Hawaii, Honolulu.

Abstract

OBJECTIVE:

The authors sought to develop and validate models using electronic health records to predict suicide attempt and suicide death following an outpatient visit.

METHOD:

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%.

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 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.

CONCLUSIONS:

Prediction models incorporating both health record data and responses to self-report questionnaires substantially outperform existing suicide risk prediction tools.

KEYWORDS:

Epidemiology; Suicide

PMID:
29792051
PMCID:
PMC6167136
[Available on 2019-10-01]
DOI:
10.1176/appi.ajp.2018.17101167
[Indexed for MEDLINE]

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