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dc.contributor.authorKessler, R.C.
dc.contributor.authorBauer, M.S.
dc.contributor.authorKreyenbuhl, J.
dc.date.accessioned2020-06-01T18:26:13Z
dc.date.available2020-06-01T18:26:13Z
dc.date.issued2020
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85085205449&doi=10.3389%2ffpsyt.2020.00390&partnerID=40&md5=98b2a74d1b4cac3f3efd8ed38306342c
dc.identifier.urihttp://hdl.handle.net/10713/12906
dc.description.abstractThere is a very high suicide rate in the year after psychiatric hospital discharge. Intensive postdischarge case management programs can address this problem but are not cost-effective for all patients. This issue can be addressed by developing a risk model to predict which inpatients might need such a program. We developed such a model for the 391,018 short-term psychiatric hospital admissions of US veterans in Veterans Health Administration (VHA) hospitals 2010-2013. Records were linked with the National Death Index to determine suicide within 12 months of hospital discharge (n=771). The Super Learner ensemble machine learning method was used to predict these suicides for time horizon between 1 week and 12 months after discharge in a 70% training sample. Accuracy was validated in the remaining 30% holdout sample. Predictors included VHA administrative variables and small area geocode data linked to patient home addresses. The models had AUC=.79-.82 for time horizons between 1 week and 6 months and AUC=.74 for 12 months. An analysis of operating characteristics showed that 22.4%-32.2% of patients who died by suicide would have been reached if intensive case management was provided to the 5% of patients with highest predicted suicide risk. Positive predictive value (PPV) at this higher threshold ranged from 1.2% over 12 months to 3.8% per case manager year over 1 week. Focusing on the low end of the risk spectrum, the 40% of patients classified as having lowest risk account for 0%-9.7% of suicides across time horizons. Variable importance analysis shows that 51.1% of model performance is due to psychopathological risk factors accounted, 26.2% to social determinants of health, 14.8% to prior history of suicidal behaviors, and 6.6% to physical disorders. The paper closes with a discussion of next steps in refining the model and prospects for developing a parallel precision treatment model. Copyright 2020 Kessler, et. al.en_US
dc.description.sponsorshipAmerican Heart Association, AHA; Center for Integrated Healthcare, U.S. Department of Veterans Affairs, CIH, VA; National Heart, Lung, and Blood Institute, NHLBIen_US
dc.description.urihttps://doi.org/10.3389/fpsyt.2020.00390en_US
dc.language.isoen_USen_US
dc.publisherFrontiers Media S.A.en_US
dc.relation.ispartofFrontiers in Psychiatry
dc.subjectintensive case managementen_US
dc.subjectmachine learningen_US
dc.subjectpredictive analyticsen_US
dc.subjectsuicideen_US
dc.subjectsuper learneren_US
dc.titleUsing Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration Systemen_US
dc.typeArticleen_US
dc.identifier.doi10.3389/fpsyt.2020.00390


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